Question:
Task: ch02-vecadd-single-turn
Target file: student_kernel.cu

Here is the current skeleton you must complete:

```cuda
#include <cuda_runtime.h>

__global__ void vecAddKernel(const float* A, const float* B, float* C, int n) {
    // TODO: Implement vector addition
    // Hints:
    // - Calculate global thread index i from blockIdx.x, blockDim.x, and threadIdx.x
    // - Add bounds check to ensure i < n
    // - Compute C[i] = A[i] + B[i]
}
```


Question:
Task: ch02-vecmul-single-turn
Target file: student_kernel.cu

Here is the current skeleton you must complete:

```cuda
#include <cuda_runtime.h>

__global__ void vecMulKernel(const float* A, const float* B, float* C, int n) {
    // TODO: Each thread i computes: C[i] = A[i] * B[i]  (if i < n)
    // Hints:
    //  - Derive global index i from block and thread indices
    //  - Guard against i >= n
}
```


Question:
Task: ch03-ex1a-matmul-row-per-thread
Target file: student_kernel.cu

Here is the current skeleton you must complete:

```cuda
#include <cuda_runtime.h>

extern "C" __global__
void matrixMulRowKernel(const float* __restrict__ M,
                        const float* __restrict__ N,
                        float* __restrict__ P,
                        int size) {
    // TODO:
    // - Each thread computes ONE output row 'row'
    // - Guard: if (row < size)
    // - For each column 'col', compute dot(M[row, :], N[:, col])
    // - Write P[row * size + col]
    // Hints:
    //   int row = blockIdx.x * blockDim.x + threadIdx.x;
    //   for (int col = 0; col < size; ++col) { ... }
}
```


Question:
Task: ch03-ex1b-matmul-col-per-thread
Target file: student_kernel.cu

Here is the current skeleton you must complete:

```cuda
#include <cuda_runtime.h>

extern "C" __global__
void matrixMulColKernel(const float* __restrict__ M,
                        const float* __restrict__ N,
                        float* __restrict__ P,
                        int size) {
    // TODO:
    // - Each thread computes ONE output column 'col'
    // - Guard: if (col < size)
    // - For each row 'row', compute dot(M[row, :], N[:, col])
    // - Write P[row * size + col]
    // Hints:
    //   int col = blockIdx.x * blockDim.x + threadIdx.x;
    //   for (int row = 0; row < size; ++row) { ... }
}
```


Question:
Task: ch03-rgb2gray-single-turn
Target file: student_kernel.cu

Here is the current skeleton you must complete:

```cuda
#include <cuda_runtime.h>
#include <math.h>

__device__ __forceinline__ unsigned char clamp_u8(int v) {
    return (unsigned char)(v < 0 ? 0 : (v > 255 ? 255 : v));
}

__global__ void rgb2grayKernel(const unsigned char* R,
                               const unsigned char* G,
                               const unsigned char* B,
                               unsigned char* gray,
                               int n) {
    // TODO:
    // - Compute global index i
    // - If (i < n), compute:
    //     float y = 0.299f*R[i] + 0.587f*G[i] + 0.114f*B[i];
    //     int yi = (int)floorf(y + 0.5f);   // round to nearest
    //     gray[i] = clamp_u8(yi);
}
```


Question:
Task: ch04-device-props-eval
Target file: student_kernel.cu

Here is the current skeleton you must complete:

```cuda
#include "student_kernel.cuh"
#include <cuda_runtime.h>
#include <string.h>

int collect_device_info(DeviceInfo* out, int max_out, int* out_count) {
    // TODO: Implement using CUDA Runtime API
    // Required calls:
    //   - cudaGetDeviceCount(&count)
    //   - For each device id in [0, count): cudaGetDeviceProperties(&prop, id)
    //
    // Required fields to fill in for each DeviceInfo (from cudaDeviceProp prop):
    //   name -> prop.name (ensure null-terminated)
    //   major -> prop.major
    //   minor -> prop.minor
    //   totalGlobalMem -> prop.totalGlobalMem
    //   multiProcessorCount -> prop.multiProcessorCount
    //   totalConstMem -> prop.totalConstMem
    //   sharedMemPerBlock -> prop.sharedMemPerBlock
    //   regsPerBlock -> prop.regsPerBlock
    //   warpSize -> prop.warpSize
    //   maxThreadsPerBlock -> prop.maxThreadsPerBlock
    //   maxThreadsDim{0,1,2} -> prop.maxThreadsDim[0..2]
    //   maxGridSize{0,1,2} -> prop.maxGridSize[0..2]
    //   clockRate -> prop.clockRate
    //   memoryClockRate -> prop.memoryClockRate
    //   memoryBusWidth -> prop.memoryBusWidth
    //   l2CacheSize -> prop.l2CacheSize
    //
    // Return 0 on success, non-zero on failure.

    (void)out; (void)max_out; (void)out_count; // remove after implementing
    return 1; // placeholder: non-zero means "not implemented"
}
```


Question:
Task: ch05-matmul-tiled
Target file: student_kernel.cu

Here is the current skeleton you must complete:

```cuda
#include <cuda_runtime.h>
#include <vector>
#include <cstring>
#include <cstdio>

#ifndef TILE
#define TILE 16
#endif

#define gpuErrchk(ans) { gpuAssert((ans), __FILE__, __LINE__); }
inline void gpuAssert(cudaError_t code, const char* file, int line, bool abort=true){
  if(code != cudaSuccess){
    fprintf(stderr,"GPUassert: %s %s %d\n", cudaGetErrorString(code), file, line);
    if(abort) exit(code);
  }
}

// TODO: Implement shared-memory tiled matrix multiplication kernel
//
// Requirements:
//  - Implement GEMM: P = M * N where M is mn, N is no, P is mo
//  - Use shared memory tiling to reduce global memory accesses
//  - Declare two shared memory tiles (size TILETILE) for M and N submatrices
//  - Process matrix multiplication in phases, loading one tile at a time
//  - Use __syncthreads() to ensure all threads have loaded data before computing
//  - Handle edge cases with bounds checking (matrices may not align to TILE boundaries)
//  - Do NOT modify inputs M or N (they are const)
//
// Algorithm outline:
//  1. Calculate output row and column for this thread
//  2. Loop over tiles along the shared dimension (k-dimension):
//     a. Load one TILETILE submatrix from M into shared memory
//     b. Load one TILETILE submatrix from N into shared memory
//     c. Use bounds checks: load 0.0f if out of matrix bounds
//     d. Synchronize threads (__syncthreads())
//     e. Compute partial dot product using shared memory tiles
//     f. Synchronize threads again before next tile
//  3. Write accumulated result to global memory P
//
// Hints:
//  - TILE is defined as a compile-time constant (default 16)
//  - Use threadIdx for within-block indexing, blockIdx for block position
//  - Shared memory declarations: __shared__ float tile_M[TILE][TILE];
//  - Number of phases: (n + TILE - 1) / TILE
//  - Matrix indexing: M[row * n + k], N[k * o + col], P[row * o + col]

__global__ void TiledMatMulKernel(const float* __restrict__ M,
                                  const float* __restrict__ N,
                                  float* __restrict__ P,
                                  int m, int n, int o)
{
    // TODO: Implement tiled matrix multiplication
}

// TODO: Implement kernel launcher
//
// Requirements:
//  - Allocate device memory for M, N, and P matrices
//  - Copy input matrices M and N from host to device
//  - Configure grid and block dimensions for tiled execution
//  - Launch TiledMatMulKernel with appropriate parameters
//  - Copy result matrix P from device back to host
//  - Free all device allocations
//
// Hints:
//  - Handle edge case: if m==0 || n==0 || o==0, return early
//  - Memory size: M needs m*n*sizeof(float), N needs n*o*sizeof(float), P needs m*o*sizeof(float)
//  - Use gpuErrchk() macro to wrap all CUDA API calls for error checking
//  - Block dimensions: dim3 block(TILE, TILE) for 2D thread blocks
//  - Grid dimensions: ensure enough blocks to cover entire output matrix
//  - Grid size formula: (dimension + TILE - 1) / TILE
//  - Remember to synchronize after kernel launch to catch execution errors

extern "C"
void launch_tiled_matmul(const float* M_h, const float* N_h, float* P_h,
                         int m, int n, int o)
{
    // TODO: Implement launcher
    (void)M_h; (void)N_h; (void)P_h; (void)m; (void)n; (void)o;
}```


Question:
Task: ch05-matmul-tiled-multiturn
Target file: student_kernel.cu

Here is the current skeleton you must complete:

```cuda
// student_kernel.cu
// Implement a shared-memory tiled matrix multiply kernel:
//   C[M x K] = A[M x N] * B[N x K]
// TILE size is 16x16. Handle non-multiple sizes and out-of-bounds safely.

#include <cuda_runtime.h>

extern "C" void launch_student(const float* A, const float* B, float* C,
                               int M, int N, int K, int blockSize);

// TODO: Implement this kernel
__global__ void matmul_tiled_student(const float* __restrict__ A,
                                     const float* __restrict__ B,
                                     float* __restrict__ C,
                                     int M, int N, int K)
{
    // TODO: Implement shared-memory tiled matrix multiplication
    // REQUIRED: TILE = 16
    //
    // Steps to implement:
    // 1. Define TILE size (16)
    // 2. Calculate 2D thread coordinates (row, col) in output matrix C
    // 3. Declare shared memory tiles for A and B submatrices
    // 4. Initialize accumulator
    // 5. Loop over tiles along the inner dimension N:
    //    a. Cooperatively load A tile and B tile into shared memory
    //    b. Guard against out-of-bounds accesses (pad with zeros)
    //    c. Synchronize threads (__syncthreads())
    //    d. Compute partial products using shared memory tiles
    //    e. Synchronize threads again
    // 6. Write final result to global memory (with bounds checking)
}

extern "C" void launch_student(const float* A, const float* B, float* C,
                               int M, int N, int K, int /*blockSize*/)
{
    // TODO: Set up proper grid and block dimensions
    // Hint: Use 16x16 thread blocks, calculate grid size based on output dimensions

    dim3 block(16, 16);
    dim3 grid((K + 15) / 16, (M + 15) / 16);
    matmul_tiled_student<<<grid, block>>>(A, B, C, M, N, K);
}
```


Question:
Task: ch05-matmul-tiled-speed
Target file: student_kernel.cu

Here is the current skeleton you must complete:

```cuda
// student_kernel.cu
#include <cuda_runtime.h>
#include <cstdio>

#ifndef TILE
#define TILE 16
#endif

// Students implement this kernel: C[M x K] = A[M x N] * B[N x K]
// One thread computes one C element; shared-memory tiled load of A and B.
__global__ void matmul_tiled_student_kernel(const float* __restrict__ A,
                                            const float* __restrict__ B,
                                            float* __restrict__ C,
                                            int M, int N, int K) {
    // TODO:
    // - Compute (row, col) from blockIdx/threadIdx
    // - Loop over tiles of N dimension
    // - Use shared memory tiles for A (TILE x TILE) and B (TILE x TILE)
    // - Guard for out-of-bounds loads/stores
    // - Accumulate sum into a register and store to C[row*K + col]

    // Hints (remove after implementing):
    // extern __shared__ float smem[]; // or static shared tiles
    // __shared__ float As[TILE][TILE], Bs[TILE][TILE];
    // int row = blockIdx.y * blockDim.y + threadIdx.y;
    // int col = blockIdx.x * blockDim.x + threadIdx.x;

    // --- your code here ---

    // Placeholder stub (compiles but gives wrong results):
    int row = blockIdx.y * blockDim.y + threadIdx.y;
    int col = blockIdx.x * blockDim.x + threadIdx.x;

    if (row < M && col < K) {
        C[row * K + col] = 0.0f; // TODO: replace with proper tiled computation
    }
}

// Host wrapper called by test harness
extern "C" void matmul_student(const float* dA, const float* dB, float* dC,
                               int M, int N, int K, int tile) {
    dim3 block(TILE, TILE);
    dim3 grid((K + TILE - 1)/TILE, (M + TILE - 1)/TILE);
    // You may ignore `tile` and use TILE macro; the harness passes TILE=16.
    matmul_tiled_student_kernel<<<grid, block>>>(dA, dB, dC, M, N, K);
}
```


Question:
Task: ch06-thread-coarsening-matmul
Target file: student_kernel.cu

Here is the current skeleton you must complete:

```cuda
#include <cuda_runtime.h>
#include <cstdio>

#ifndef TILE_WIDTH
#define TILE_WIDTH 16
#endif

#ifndef COARSE_FACTOR
#define COARSE_FACTOR 4
#endif

// Students: implement a coarsened, tiled GEMM C[MK] = A[MN] * B[NK]
// Each block computes a tile: height TILE_WIDTH, width TILE_WIDTH*COARSE_FACTOR
// Use shared memory tiles for A and B; do safe (bounds-checked) loads.
// Row-major layout: elem(i,j) at base[i*ld + j].

__global__ void MatmulCoarsenedKernel(const float* __restrict__ A,
                                      const float* __restrict__ B,
                                      float* __restrict__ C,
                                      int M, int N, int K)
{
    // TODO: Implement thread coarsening matrix multiplication
    //
    // Key requirements:
    // 1. Use shared memory tiles for A and B:
    //    __shared__ float As[TILE_WIDTH][TILE_WIDTH];
    //    __shared__ float Bs[TILE_WIDTH][TILE_WIDTH * COARSE_FACTOR];
    //
    // 2. Each thread computes COARSE_FACTOR output elements
    //    - Thread (tx,ty) computes elements at columns: colBase + c*TILE_WIDTH for c=0..COARSE_FACTOR-1
    //    - Use register array: float acc[COARSE_FACTOR];
    //
    // 3. Loop over tiles of the N dimension:
    //    - Load A tile (TILE_WIDTH x TILE_WIDTH)
    //    - Load B super-tile (TILE_WIDTH x TILE_WIDTH*COARSE_FACTOR) in COARSE_FACTOR stripes
    //    - __syncthreads() after loading
    //    - Compute partial products with triple nested loop (k, c)
    //    - __syncthreads() before next iteration
    //
    // 4. Write results with bounds checking
    //
    // Template structure:
    // const int ty = threadIdx.y;
    // const int tx = threadIdx.x;
    // const int row = blockIdx.y * TILE_WIDTH + ty;
    // const int colBase = blockIdx.x * (TILE_WIDTH * COARSE_FACTOR) + tx;
    //
    // float acc[COARSE_FACTOR];
    // for (int c = 0; c < COARSE_FACTOR; ++c) acc[c] = 0.0f;
    //
    // Loop over tiles...
    //
    // Write results...

    // Placeholder implementation (incorrect, will fail tests):
    int row = blockIdx.y * blockDim.y + threadIdx.y;
    int col = blockIdx.x * blockDim.x + threadIdx.x;

    if (row < M && col < K) {
        C[row * K + col] = 0.0f; // TODO: Replace with actual coarsened computation
    }
}

// Student launcher: choose grid/block and launch your kernel
extern "C" void launch_student(const float* A_d,
                               const float* B_d,
                               float* C_d,
                               int M, int N, int K)
{
    // TODO: Configure proper grid and block dimensions for thread coarsening
    //
    // Key points:
    // - Block size should be (TILE_WIDTH, TILE_WIDTH)
    // - Grid X dimension should account for COARSE_FACTOR: (K + TILE_WIDTH*COARSE_FACTOR - 1) / (TILE_WIDTH*COARSE_FACTOR)
    // - Grid Y dimension covers rows: (M + TILE_WIDTH - 1) / TILE_WIDTH

    // Placeholder launch (incorrect dimensions):
    dim3 block(TILE_WIDTH, TILE_WIDTH);
    dim3 grid((K + TILE_WIDTH - 1) / TILE_WIDTH, (M + TILE_WIDTH - 1) / TILE_WIDTH);

    MatmulCoarsenedKernel<<<grid, block>>>(A_d, B_d, C_d, M, N, K);
}
```


Question:
Task: ch07-conv2d-tiled-constant
Target file: student_kernel.cu

Here is the current skeleton you must complete:

```cuda
// students edit only this file
#include <cuda_runtime.h>
#include <cstdio>

#ifndef TILE
#define TILE 16
#endif

#ifndef MAX_RADIUS
#define MAX_RADIUS 8   // supports up to (2*8+1)=17x17 filters
#endif

// 1D constant buffer for filter coefficients (row-major)
// Size = (2*MAX_RADIUS+1)^2
__constant__ float c_filter[(2*MAX_RADIUS+1)*(2*MAX_RADIUS+1)];

extern "C" __host__ void setFilterConstant(const float* h_filter, int r) {
    const int K = 2*r + 1;
    cudaMemcpyToSymbol(c_filter, h_filter, K*K*sizeof(float), 0, cudaMemcpyHostToDevice);
}

// Students must implement this kernel.
// Requirements:
// - Shared-memory tiling with halo of +/-r (use zero padding for out-of-bounds loads)
// - Use c_filter (in constant memory) for filter coefficients
// - Each thread computes one output pixel (if in bounds)
// - Grid/block: 2D, blockDim=(TILE,TILE), gridDim=ceil(W/TILE) x ceil(H/TILE)
// - Inputs/outputs are float* (grayscale), shapes: in/out = H*W
// - r is runtime radius (<= MAX_RADIUS)
__global__ void conv2d_tiled_constant_kernel(const float* __restrict__ in,
                                             float* __restrict__ out,
                                             int H, int W, int r)
{
    // TODO: Implement tiled 2D convolution with constant memory
    //
    // Key steps:
    // 1) Compute global (x,y) coordinates for this thread
    // 2) Declare shared memory tile with halo: extern __shared__ float smem[];
    //    Size needed: (TILE+2*r) * (TILE+2*r) * sizeof(float)
    // 3) Compute the tile's coverage region including halo
    // 4) Cooperatively load the entire tile+halo region into shared memory
    //    - Use zero padding for out-of-bounds pixels
    //    - May need nested loops for threads to cover entire shared memory region
    // 5) __syncthreads() to ensure all data is loaded
    // 6) If this thread's output pixel is in bounds, compute convolution:
    //    - Access input pixels from shared memory (with proper offsets)
    //    - Access filter coefficients from c_filter constant memory
    //    - Accumulate weighted sum
    // 7) Write result to global output memory
    //
    // Hints:
    // - Filter coefficients in c_filter are stored row-major: c_filter[(dy+r)*(2*r+1) + (dx+r)]
    // - Shared memory indexing: smem[sy * tileWidth + sx] where tileWidth = TILE+2*r
    // - Global input indexing: in[gy * W + gx]
    // - Consider boundary conditions carefully for both image edges and tile edges

    // Placeholder implementation (will fail until properly implemented):
    int x = blockIdx.x * blockDim.x + threadIdx.x;
    int y = blockIdx.y * blockDim.y + threadIdx.y;

    if (x < W && y < H) {
        // This is incorrect - just copies input to output
        out[y * W + x] = in[y * W + x];
    }
}
```


Question:
Task: ch08-heat-3d-single-turn
Target file: student_kernel.cu

Here is the current skeleton you must complete:

```cuda
// student_kernel.cu
#include <cuda_runtime.h>
#include <cstdio>

// Implement a single explicit 7-point heat step.
// in  : N*N*N input (flattened, row-major: i*N*N + j*N + k)
// out : N*N*N output (same layout)
// N   : grid dimension
// alpha, dt, dx: physical parameters; r = alpha*dt/(dx*dx)
// Boundary policy: copy boundary through (out = in) if any neighbor would be OOB.
__global__ void heat_step_kernel(const float* __restrict__ in,
                                 float* __restrict__ out,
                                 unsigned int N,
                                 float alpha, float dt, float dx)
{
    // TODO:
    // 1) Compute (i,j,k) from block and thread indices
    // 2) If any of i,j,k is 0 or N-1 => boundary: out = in and return
    // 3) Else compute r = alpha*dt/(dx*dx) and 7-point update:
    //    out = in + r*(sum six neighbors - 6*in)
    // Guard small N (N<3) by simply copying (no interior exists).
}
```


Question:
Task: ch08-stencil3d-basic-single-turn
Target file: student_kernel.cu

Here is the current skeleton you must complete:

```cuda
// student_kernel.cu
#include <cuda_runtime.h>
#include <cstdio>

__global__ void stencil3d_basic_student(
    const float* __restrict__ in,
    float* __restrict__ out,
    int N,
    float c0, float c1, float c2, float c3, float c4, float c5, float c6)
{
    // TODO:
    // - Each thread computes OUT(i,j,k) for one grid point
    // - Use 7-point stencil on INTERIOR points: (1..N-2) in each dim
    // - For boundary points (i==0 || i==N-1 || ...), copy through: out = in
    // - Guard for N==0 or N==1 safely
    // Hints:
    //   int i = blockIdx.z * blockDim.z + threadIdx.z;
    //   int j = blockIdx.y * blockDim.y + threadIdx.y;
    //   int k = blockIdx.x * blockDim.x + threadIdx.x;
    //   int idx = (i * N + j) * N + k;
    //   int L = ((i) * N + j) * N + (k-1); // k-1, etc.
}
```


Question:
Task: ch08-stencil3d-sharedmem-single-turn
Target file: student_kernel.cu

Here is the current skeleton you must complete:

```cuda
// student_kernel.cu
#include <cuda_runtime.h>
#include <cstdio>

// Tile parameters for this task
#ifndef IN_TILE_DIM
#define IN_TILE_DIM 8          // threads per dim that load (with halo)
#endif
#define OUT_TILE_DIM (IN_TILE_DIM-2)

__global__ void stencil3d_shared_student(
    const float* __restrict__ in,
    float* __restrict__ out,
    int N,
    float c0, float c1, float c2, float c3, float c4, float c5, float c6)
{
    // TODO:
    // - Launch with block=(IN_TILE_DIM,IN_TILE_DIM,IN_TILE_DIM)
    // - Each block loads a IN_TILE_DIM^3 tile (with halo) into shared memory
    // - Only threads with local coords in [1..IN_TILE_DIM-2] compute outputs
    //   for the corresponding global interior coordinates
    // - Copy-through boundary cells (same rule as basic)
    // Hints:
    //   Shared array: __shared__ float tile[IN_TILE_DIM][IN_TILE_DIM][IN_TILE_DIM];
    //   Global coords start at (blockIdx * OUT_TILE_DIM) - 1 (to include halo)
    //   Guard global loads (row/col/depth) that fall outside [0..N-1]
}
```


Question:
Task: ch09-histogram-naive-single-turn
Target file: student_kernel.cu

Here is the current skeleton you must complete:

```cuda
// student_kernel.cu
#include <cuda_runtime.h>
#include <cstddef>

// TODO: Implement naive global-atomic histogram.
//
// Requirements:
// - Use global-memory atomicAdd(&hist[bin], 1u)
// - Grid-stride loop over N
// - Ignore out-of-range bin indices
// - Do not write to 'in'
// - No shared memory
//
// Signature must not change.
__global__ void histogram_kernel(const int* in, unsigned int* hist,
                                 size_t N, int num_bins)
{
    // TODO:
    // size_t i = ...
    // size_t stride = ...
    // for (; i < N; i += stride) {
    //   int bin = in[i];
    //   if (0 <= bin && bin < num_bins) atomicAdd(&hist[bin], 1u);
    // }
}
```


Question:
Task: ch09-histogram-shared-single-turn
            Target file: student_kernel.cu

            Implementation requirements:
- Use extern __shared__ unsigned int s_hist[] with num_bins elements.
- Cooperatively zero the shared histogram before processing data.
- Iterate over the input with a grid-stride loop and accumulate counts into shared memory (no atomics within the block).
- Synchronize threads before and after shared-memory accumulation.
- Cooperatively merge shared counts to global hist with atomicAdd, handling num_bins > blockDim.x.
- Do not modify the input array and avoid out-of-bounds accesses.

            Here is the current student_kernel.cu:

            ```cuda
            // student_kernel.cu
#include <cuda_runtime.h>
#include <cstddef>

// TODO: Implement shared-memory privatized histogram
//
// Requirements:
//  - Use shared memory to reduce global atomic contention
//  - Each block maintains its own private histogram in shared memory
//  - Accumulate counts into shared histogram using atomicAdd
//  - Flush shared histogram to global histogram at the end
//  - Process all N input elements across the grid
//
// Algorithm steps:
//  1. Initialize shared memory histogram to zero (cooperative initialization)
//     - Each thread zeros multiple bins if num_bins > blockDim.x
//     - Use loop: for (int bin = threadIdx.x; bin < num_bins; bin += blockDim.x)
//     - Synchronize after initialization
//
//  2. Accumulate input values into shared histogram
//     - Use grid-stride loop to process input elements
//     - Read input value, treat it as bin index
//     - Validate bin is in range [0, num_bins)
//     - Use atomicAdd to increment shared histogram bin
//     - Synchronize after accumulation
//
//  3. Merge shared histogram into global histogram
//     - Each thread handles multiple bins if needed
//     - Use atomicAdd to add shared counts to global histogram
//
// Hints:
//  - Shared memory is declared: extern __shared__ unsigned int s_hist[];
//  - Grid-stride loop: for (size_t i = blockIdx.x*blockDim.x + threadIdx.x; i < N; i += blockDim.x*gridDim.x)
//  - Atomic operations: atomicAdd(&s_hist[bin], 1u) for shared, atomicAdd(&hist[bin], count) for global
//  - Synchronization required between phases: __syncthreads()

__global__ void histogram_kernel(const int* in, unsigned int* hist,
                                 size_t N, int num_bins)
{
    extern __shared__ unsigned int s_hist[];

    // TODO: Implement 3-phase histogram: init shared  accumulate  flush to global
    (void)in; (void)hist; (void)N; (void)num_bins; (void)s_hist;
}
            ```


Question:
Task: ch10-reduction-max-arbitrary
Target file: student_kernel.cu

Goal: Implement an arbitrary-length maximum reduction that scans the input with grid-stride loops, performs a shared-memory tree reduction per block, and emits the final maximum via a race-free CAS-based atomic max on floats.

Key requirements:
- Kernel signature: `__global__ void reduce_max_arbitrary(const float* in, float* out, int n)`
- Initialize each thread's local maximum to `-INFINITY` when it sees no elements.
- Use grid-stride loops to cover the full array and reduce into shared memory before the atomic.
- Implement `atomicMaxFloat` using `atomicCAS` on the integer bit pattern and update only `out[0]`.

Skeleton:
```cuda
// student_kernel.cu
#include <cuda_runtime.h>
#include <limits>

// TODO: Implement arbitrary-length maximum reduction with grid-stride, shared
// memory, and a CAS-loop atomicMax for float. Initialize per-thread local max
// to -INFINITY when n==0 or no elements in its stride.
//
// IMPORTANT: Use the -INFINITY macro (not std::numeric_limits<float>::infinity())
// Example: float local_max = -INFINITY;
__device__ inline
void atomicMaxFloat(float* addr, float val) {
    // TODO: Implement via atomicCAS on int bit patterns
}

extern "C" __global__
void reduce_max_arbitrary(const float* in, float* out, int n) {
    // TODO
}
```


Question:
Task: ch10-reduction-sum-2048
Target file: student_kernel.cu

Here is the current skeleton you must complete:

```cuda
// student_kernel.cu
#include <cuda_runtime.h>

// TODO: Implement convergent shared-memory reduction for exactly 2048 elements.
// Contract:
//  - gridDim.x == 1, blockDim.x == 1024
//  - Each thread loads two elements: in[tid] and in[tid + 1024]
//  - Reduce in shared memory (convergent pattern), write out[0] only.
extern "C" __global__
void reduce_sum_2048(const float* in, float* out) {
    // TODO
    // Suggested shape:
    // __shared__ float s[1024];
    // unsigned t = threadIdx.x;
    // float v = in[t] + in[t + 1024];
    // s[t] = v;
    // __syncthreads();
    // for (unsigned stride = blockDim.x/2; stride >= 1; stride >>= 1) { ... }
    // if (t == 0) out[0] = s[0];
}
```


Question:
Task: ch10-reduction-sum-arbitrary
Target file: student_kernel.cu

Here is the current skeleton you must complete:

```cuda
// student_kernel.cu
#include <cuda_runtime.h>

// TODO: Implement arbitrary-length sum with grid-stride loads, shared-memory
// reduction per block, and atomicAdd(out, block_sum). Use dynamic shared memory.
extern "C" __global__
void reduce_sum_arbitrary(const float* in, float* out, int n) {
    // TODO
    // Suggested shape:
    // extern __shared__ float s[];
    // int tid = threadIdx.x;
    // long long idx = blockIdx.x * (long long)blockDim.x * 2 + tid;
    // long long stride = (long long)gridDim.x * blockDim.x * 2;
    // float sum = 0.f;
    // for (; idx < n; idx += stride) {
    //   sum += in[idx];
    //   long long idx2 = idx + blockDim.x;
    //   if (idx2 < n) sum += in[idx2];
    // }
    // s[tid] = sum; __syncthreads();
    // for (int step = blockDim.x/2; step >= 1; step >>= 1) { ... }
    // if (tid==0) atomicAdd(out, s[0]);
}
```


Question:
Task: ch12-merge-path-single-turn
Target file: student_kernel.cu

Here is the current skeleton you must complete:

```cuda
// student_kernel.cu
#include <cuda_runtime.h>

// TODO: Implement parallel merge using merge-path (diagonal partition).
// Contract summary:
//  - Stable: on ties, choose A first
//  - Partition per thread using diagonals; then sequentially merge that slice
//  - Inputs A,B are sorted ascending; write C of length nA+nB

__device__ inline int clampi(int x, int lo, int hi) {
    return x < lo ? lo : (x > hi ? hi : x);
}

// Find (i,j) on diagonal d (i+j = d) satisfying merge-path conditions.
// Returns i; j = d - i.
// Invariants:
//   lo = max(0, d - nB), hi = min(d, nA)
// Stable tie-breaking: A[i-1] <= B[j]  (and B[j-1] < A[i])
__device__ __forceinline__
int merge_path_search(const int* __restrict__ A, int nA,
                      const int* __restrict__ B, int nB,
                      int d)
{
    // TODO: Implement binary search to find merge-path coordinates
    // Return i such that (i, d-i) satisfies merge-path conditions
    return 0; // placeholder
}

extern "C" __global__
void merge_path_kernel(const int* __restrict__ A, int nA,
                       const int* __restrict__ B, int nB,
                       int* __restrict__ C)
{
    // TODO: Implement merge-path parallel merge
    // 1. Calculate thread's diagonal range [d0, d1)
    // 2. Find merge coordinates (i0,j0) and (i1,j1) using merge_path_search
    // 3. Sequentially merge A[i0..i1) and B[j0..j1) into C[d0..d1)
}
```


Question:
Task: ch13-merge-path-fullsort-single
Target file: student_kernel.cu

Here is the current skeleton you must complete:

```cuda
// ch13-merge-path-fullsort-single / student_kernel.cu
#include <cuda_runtime.h>
#include <stdint.h>

// CONTRACT:
// Implement stable GPU merge sort using iterative merge passes.
// You must implement:
//   - merge_path_search (device): diagonal search
//   - merge_path_kernel (global): merges a slice [d0,d1)
//   - gpu_merge_sort (host): doubles width and ping-pongs buffers until sorted

__device__ int merge_path_search(const uint32_t* A, int nA,
                                 const uint32_t* B, int nB,
                                 int d)
{
    // TODO: diagonal binary search; return i (then j=d-i)
    return 0;
}

__global__ void merge_path_kernel(const uint32_t* __restrict__ A, int nA,
                                  const uint32_t* __restrict__ B, int nB,
                                  uint32_t* __restrict__ C)
{
    // TODO:
    //  - P = total threads
    //  - segment size seg = ceil((nA+nB)/P)
    //  - each thread t merges its slice [d0,d1)
    //  - compute (i0,j0) & (i1,j1) via merge_path_search
    //  - sequentially merge into C[d0..d1)
}

extern "C" void gpu_merge_sort(const uint32_t* d_in, uint32_t* d_out, int n)
{
    // TODO:
    //  - width = 1; ping-pong buffers
    //  - for width < n:
    //      * launch merges of adjacent runs [k..k+width) and [k+width..k+2*width)
    //  - final result copied to d_out
    (void)d_in; (void)d_out; (void)n;
}
```


Question:
Task: ch13-radix-multiradix-coarsened-fullsort-single
Target file: student_kernel.cu

Here is the current skeleton you must complete:

```cuda
// student_kernel.cu
#include <cuda_runtime.h>
#include <stdint.h>
#include <cstdio>

static inline void CK(cudaError_t e,const char* m){
    if(e!=cudaSuccess){ std::fprintf(stderr,"CUDA %s: %s\n", m, cudaGetErrorString(e)); std::exit(2); }
}

#ifndef RADIX_BITS
#define RADIX_BITS 4
#endif
#ifndef COARSENING_FACTOR
#define COARSENING_FACTOR 8
#endif
#ifndef BLOCK
#define BLOCK 256
#endif

// TODO: Implement a stable 4-bit LSD radix sort with thread coarsening (COARSENING_FACTOR).
// Sort must be in-place on `data` (you may use a temp buffer internally).

// Choose your radix size and coarsening factor
#define RADIX_SIZE (1 << RADIX_BITS)  // 2^RADIX_BITS buckets
#define RADIX_MASK (RADIX_SIZE - 1)

extern "C" __global__
void radix_sort_coarsened_kernel(unsigned int* __restrict__ data,
                                unsigned int* __restrict__ temp,
                                int n,
                                int shift)
{
    // TODO: Implement coarsened multi-radix sort pass
    // 1. Each thread loads COARSENING_FACTOR elements
    // 2. Count elements for each bucket using coarsened loading
    // 3. Compute prefix sums to find output positions for each bucket
    // 4. Scatter elements to correct positions based on radix value
    // 5. Ensure stable sorting and efficient memory access patterns
}

extern "C"
void radix_sort_coarsened_host(unsigned int* data, int n)
{
    if(n <= 1) return;
    // Placeholder: shallow copy (intentionally insufficient so tests fail until implemented)
    unsigned int* tmp=nullptr;
    CK(cudaMalloc(&tmp, n*sizeof(unsigned int)), "malloc tmp");
    CK(cudaMemcpy(tmp, data, n*sizeof(unsigned int), cudaMemcpyDeviceToDevice), "copy to tmp");
    CK(cudaMemcpy(data, tmp, n*sizeof(unsigned int), cudaMemcpyDeviceToDevice), "copy back");
    cudaFree(tmp);
}
```


Question:
Task: ch13-radix-multiradix-fullsort-single
Target file: student_kernel.cu

Here is the current skeleton you must complete:

```cuda
// student_kernel.cu
#include <cuda_runtime.h>
#include <stdint.h>
#include <cstdio>

static inline void CK(cudaError_t e, const char* m){
    if(e != cudaSuccess){
        std::fprintf(stderr, "CUDA %s: %s\n", m, cudaGetErrorString(e));
        std::exit(2);
    }
}

// TODO: Implement a multiradix (RADIX_BITS=4) stable radix sort in-place.
// Contract:
//   extern "C" void radix_sort_multiradix_host(unsigned int* data, int n);
// Requirements:
//   - Sort ascending, stable per pass (4-bit buckets, 8 passes total).
//   - Arbitrary n (including 0 / non-multiples of block size).
//   - No OOB writes (tests use guarded buffers).
//   - In-place on `data` (you may use an internal device temp buffer).

// Choose your radix size (recommended: 2-bit or 4-bit)
#define RADIX_BITS 2
#define RADIX_SIZE (1 << RADIX_BITS)  // 2^RADIX_BITS buckets
#define RADIX_MASK (RADIX_SIZE - 1)

extern "C" __global__
void radix_sort_multiradix_kernel(unsigned int* __restrict__ data,
                                 unsigned int* __restrict__ temp,
                                 int n,
                                 int shift)
{
    // TODO: Implement multi-radix sort pass
    // 1. Count elements for each bucket (RADIX_SIZE buckets) using shared memory
    // 2. Compute prefix sums to find output positions for each bucket
    // 3. Scatter elements to correct positions based on radix value
    // 4. Ensure stable sorting (preserve relative order for equal keys)
}

extern "C"
void radix_sort_multiradix_host(unsigned int* data, int n)
{
    if (n <= 1) return;
    // Starter behavior: shallow copy  will fail non-trivial tests.
    unsigned int* tmp = nullptr;
    CK(cudaMalloc(&tmp, n*sizeof(unsigned int)), "malloc tmp");
    CK(cudaMemcpy(tmp, data, n*sizeof(unsigned int), cudaMemcpyDeviceToDevice), "copy tmp");
    CK(cudaMemcpy(data, tmp, n*sizeof(unsigned int), cudaMemcpyDeviceToDevice), "copy back");
    cudaFree(tmp);
}
```


Question:
Task: ch13-radix-naive-1bit-fullsort-single
Target file: student_kernel.cu

Here is the current skeleton you must complete:

```cuda
// student_kernel.cu
// TODO: Implement a naive 1-bit parallel radix sort that fully sorts 32-bit keys
// across 32 passes (LSB -> MSB), stable per pass.
//
// Requirements:
//  - API: extern "C" void radix_sort_1bit_host(unsigned int* data, int n)
//  - In-place: modify data in-place
//  - Stability: within each bit-partition, preserve relative order (use zerosBefore(i) / onesBefore(i))
//  - Multi-pass orchestration: 32 passes, swap ping-pong buffers every pass
//  - Correct for arbitrary n (n can be 0)
//  - No OOB writes: only write within [0..n)
//
// Hints (not mandatory, but aligned with the reference):
//  - kFlagZeros: flagsZero[i] = 1 if ((x >> bit)&1)==0 else 0
//  - kBlockExclusiveScan + host scan over block sums for robustness
//  - kAddBlockOffsets to turn per-block exclusive scans into global exclusive scan
//  - kScatter uses stable positions:
//        if bit==0: pos = zerosBefore(i)
//        else      : pos = totalZeros + (i - zerosBefore(i))

#include <cuda_runtime.h>

// TODO: Implement naive 1-bit radix sort using LSD approach.
// Contract summary:
//  - Stable: equal elements maintain relative order
//  - Process 1 bit per pass, 32 passes total for uint32_t
//  - Use parallel counting, prefix sum, and scattering
//  - Sort in ascending order

extern "C" __global__
void radix_sort_1bit_kernel(unsigned int* __restrict__ data,
                           unsigned int* __restrict__ temp,
                           int n,
                           int bit)
{
    // TODO: Implement single-bit radix sort pass
    // 1. Count elements with bit=0 and bit=1 using shared memory
    // 2. Compute prefix sums to find output positions
    // 3. Scatter elements to correct positions based on bit value
    // 4. Ensure stable sorting (preserve relative order for equal keys)
}

extern "C"
void radix_sort_1bit_host(unsigned int* data, int n)
{
    // TODO: Implement host function that orchestrates 32 sorting passes
    // 1. Allocate temporary buffer
    // 2. For each bit position (0 to 31):
    //    - Launch radix_sort_1bit_kernel
    //    - Swap data and temp pointers
    // 3. Ensure final result is in original data array
    // 4. Clean up temporary buffer
}
```


Question:
Task: ch13-radix-onepass-multiradix-single
Target file: student_kernel.cu

Here is the current skeleton you must complete:

```cuda
// ch13-radix-onepass-multiradix-single / student_kernel.cu
#include <cuda_runtime.h>
#include <stdint.h>

// CONTRACT:
// Implement one *stable* multiradix pass over keys.
// - keys_d:  input keys (length n)
// - out_d:   output keys (length n)
// - n:       number of elements
// - r:       bits per pass (1, 2, or 4)
// - shift:   bit shift for the digit (e.g., 0, r, 2r, ...)
// Approach expected (typical):
//   1) extract digits (0..(2^r - 1))
//   2) per-block histogram -> global array [grid x buckets]
//   3) host exclusive scan to get global bases & per-block bucket bases
//   4) stable scatter into out_d using digit, globalBase[b], blockBase[block,b], and local offset within block
// NOTE: Stability means equal digits preserve the original order.

extern "C" void radix_onepass_multiradix(
    const uint32_t* keys_d, uint32_t* out_d,
    int n, int r, int shift);

// TODO: provide your implementation
extern "C" void radix_onepass_multiradix(
    const uint32_t* keys_d, uint32_t* out_d,
    int n, int r, int shift)
{
    // Implement kernels + host prefix here.
    // You may choose blockDim=256 and compute grid from n.
    // (Any correct stable implementation passes.)
    (void)keys_d; (void)out_d; (void)n; (void)r; (void)shift;
}
```


Question:
Task: ch14-spmv-csr-thread-per-row-single
            Target file: student_kernel.cu

            Implementation requirements:
- Assign one thread per row using a grid-stride loop.
- Accumulate vals[j] * x[colIdx[j]] for j in [rowPtr[row], rowPtr[row+1]) and write y[row].
- Avoid atomics and guard rows >= m before reading rowPtr[row+1].

            Here is the current student_kernel.cu:

            ```cuda
            // ch14-spmv-csr-thread-per-row-single / student_kernel.cu
#include <cuda_runtime.h>

extern "C" __global__
void spmv_csr_kernel(const int* __restrict__ rowPtr,
                     const int* __restrict__ colIdx,
                     const float* __restrict__ vals,
                     const float* __restrict__ x,
                     float* __restrict__ y,
                     int m)
{
    // TODO: Assign one thread per row, iterate over the row's nonzeros, and
    // write y[row] without using atomics.

    int row = blockIdx.x * blockDim.x + threadIdx.x;
    if (row < m) {
        int start = rowPtr[row];
        int end   = rowPtr[row + 1];
        float scratch = 0.0f;
        for (int j = start; j < end; ++j) {
            (void)colIdx[j];
            (void)vals[j];
        }
        if (y) {
            y[row] = scratch; // placeholder leaves output unchanged (all zeros)
        }
    }
}
            ```


Question:
Task: ch14-spmv-coo-single
            Target file: student_kernel.cu

            Implementation requirements:
- Launch a grid-stride loop over nnz entries.
- For each entry, accumulate with atomicAdd(&y[row], vals[k] * x[col]) after bounds checks.
- Do not modify the input arrays.

            Here is the current student_kernel.cu:

            ```cuda
            // ch14-spmv-coo-single / student_kernel.cu
#include <cuda_runtime.h>

extern "C" __global__
void spmv_coo_kernel(const int* __restrict__ row_idx,
                     const int* __restrict__ col_idx,
                     const float* __restrict__ vals,
                     const float* __restrict__ x,
                     float* __restrict__ y,
                     int nnz)
{
    // TODO: Implement COO-format SpMV using a grid-stride loop and atomicAdd on
    // y[row] to handle duplicate entries.

    int k = blockIdx.x * blockDim.x + threadIdx.x;
    if (k < nnz) {
        int r = row_idx[k];
        int c = col_idx[k];
        (void)r;
        (void)c;
        (void)vals[k];
        (void)x;
        (void)y;
    }
}
            ```


Question:
Task: ch14-spmv-ell-single
            Target file: student_kernel.cu

            Implementation requirements:
- Assign one thread per row using a grid-stride loop.
- Skip padded slots with colIdx < 0 and accumulate remaining entries into y[row].
- Initialize each row result before accumulation.

            Here is the current student_kernel.cu:

            ```cuda
            // ch14-spmv-ell-single / student_kernel.cu
#include <cuda_runtime.h>

extern "C" __global__
void spmv_ell_kernel(const int* __restrict__ colIdx,
                     const float* __restrict__ vals,
                     const float* __restrict__ x,
                     float* __restrict__ y,
                     int m, int K)
{
    // TODO: Assign one thread per row, iterate across the K padded entries,
    // skip negative column indices, and accumulate into y[row].
    //
    // DATA LAYOUT: Row-major ELL storage
    // For row i, the K entries are stored at indices [i*K, i*K+1, ..., i*K+K-1]
    // Example:
    //   int base = row * K;
    //   for (int t = 0; t < K; ++t) {
    //       int col = colIdx[base + t];
    //       if (col >= 0) {
    //           // accumulate vals[base + t] * x[col]
    //       }
    //   }

    int row = blockIdx.x * blockDim.x + threadIdx.x;
    if (row < m) {
        (void)colIdx;
        (void)vals;
        (void)x;
        if (y) {
            y[row] = 0.0f;
        }
    }

    (void)K;
}
            ```


Question:
Task: ch14-spmv-jds-single
            Target file: student_kernel.cu

            Implementation requirements:
- Implement spmv_jds_kernel to walk jagged diagonals, checking if the current row participates before accumulating.
- Write results back to original row order using the permutation array.
- Implement spmv_jds host wrapper to configure and launch the kernel.

            Here is the current student_kernel.cu:

            ```cuda
            // ch14-spmv-jds-single / student_kernel.cu
#include <cuda_runtime.h>

extern "C" __global__
void spmv_jds_kernel(const int* __restrict__ colJds,
                     const float* __restrict__ valJds,
                     const int* __restrict__ permute,
                     const int* __restrict__ jdPtr,
                     const float* __restrict__ x,
                     float* __restrict__ y,
                     int m, int maxJ)
{
    // TODO: Traverse the jagged diagonals, accumulate per permuted row, and
    // write results back to the original ordering via permute[]
    if (colJds && valJds && permute && jdPtr && x && y) {
        (void)m;
        (void)maxJ;
    }
}

extern "C" void spmv_jds(const int* colJds, const float* valJds,
                         const int* permute, const int* jdPtr,
                         const float* x, float* y, int m, int maxJ)
{
    (void)colJds; (void)valJds; (void)permute; (void)jdPtr;
    (void)x; (void)y; (void)m; (void)maxJ;

    // TODO: Configure launch dimensions and invoke spmv_jds_kernel.
}
            ```


Question:
Task: ch14-spmv-hyb-single
            Target file: student_kernel.cu

            Implementation requirements:
- Implement spmv_ell_rows_kernel to compute ELL rows without atomics.
- Implement spmv_coo_accum_kernel as a grid-stride loop that atomicAdds COO overflow contributions.
- In spmv_hyb, launch the ELL kernel, synchronize, then launch the COO accumulation.

            Here is the current student_kernel.cu:

            ```cuda
            // ch14-spmv-hyb-single / student_kernel.cu
#include <cuda_runtime.h>

extern "C" __global__
void spmv_ell_rows_kernel(const int* __restrict__ colEll,
                          const float* __restrict__ valEll,
                          const float* __restrict__ x,
                          float* __restrict__ y,
                          int m, int K)
{
    // TODO: Compute the ELL portion of the HYB product row-by-row.
    //
    // DATA LAYOUT: Row-major ELL storage
    // For row i, the K entries are stored at indices [i*K, i*K+1, ..., i*K+K-1]
    // Example:
    //   int row = blockIdx.x * blockDim.x + threadIdx.x;
    //   if (row < m) {
    //       float sum = 0.0f;
    //       int base = row * K;
    //       for (int t = 0; t < K; ++t) {
    //           int col = colEll[base + t];
    //           if (col >= 0) sum += valEll[base + t] * x[col];
    //       }
    //       y[row] = sum;
    //   }
    if (colEll && valEll && x && y) {
        (void)m;
        (void)K;
    }
}

extern "C" __global__
void spmv_coo_accum_kernel(const int* __restrict__ rowCoo,
                           const int* __restrict__ colCoo,
                           const float* __restrict__ valCoo,
                           const float* __restrict__ x,
                           float* __restrict__ y,
                           int nnzC)
{
    // TODO: Accumulate the COO tail using atomicAdd.
    if (rowCoo && colCoo && valCoo && x && y) {
        (void)nnzC;
    }
}

extern "C" void spmv_hyb(const int* colEll, const float* valEll, int m, int K,
                         const int* rowCoo, const int* colCoo, const float* valCoo, int nnzC,
                         const float* x, float* y)
{
    (void)colEll; (void)valEll; (void)m; (void)K;
    (void)rowCoo; (void)colCoo; (void)valCoo; (void)nnzC;
    (void)x; (void)y;

    // TODO: Launch spmv_ell_rows_kernel followed by spmv_coo_accum_kernel to
    // form the full HYB SpMV result.
}
            ```


Question:
Task: ch14-coo-to-csr-single
            Target file: student_kernel.cu

            Implementation requirements:
- Build rowCounts on the device via a grid-stride histogram of row indices.
- Perform an exclusive scan of rowCounts to obtain rowPtr and ensure rowPtr[m] == nnz.
- Use a per-row cursor (rowNext) to scatter COO entries into CSR while preserving input order.
- Handle empty rows and invalid rows defensively; do not combine duplicate entries.
- Keep the input COO arrays unchanged and avoid out-of-bounds writes.

            Here is the current student_kernel.cu:

            ```cuda
            // ch14-coo-to-csr-single / student_kernel.cu
#include <cuda_runtime.h>
#include <cstdio>

// Convert an unsorted COO triple (row/col/val) into CSR form.  The reference
// shows one correct solution; this stub leaves the heavy lifting to the model.

extern "C" __global__
void k_hist_rows(const int* __restrict__ row, int nnz, int m, int* __restrict__ rowCounts)
{
    // TODO: Grid-stride histogram of row indices into rowCounts without
    // accessing out-of-range rows.

    if (blockIdx.x == 0 && threadIdx.x == 0 && nnz > 0 && m > 0) {
        atomicAdd(&rowCounts[0], 0);
    }
}

extern "C" __global__
void k_stable_scatter_single(const int* __restrict__ row,
                             const int* __restrict__ col,
                             const float* __restrict__ val,
                             int nnz, int m,
                             int* __restrict__ rowNext,
                             int* __restrict__ colCSR,
                             float* __restrict__ valCSR)
{
    // TODO: Walk COO entries in order, writing to CSR using per-row cursors.
    if (blockIdx.x == 0 && threadIdx.x == 0 && nnz > 0 && m > 0) {
        rowNext[0] += 0;
        colCSR[0] += 0;
        valCSR[0] += 0.0f;
    }
}

extern "C" void coo_to_csr(const int* d_row, const int* d_col, const float* d_val,
                           int nnz, int m, int /*n*/,
                           int* d_rowPtr, int* d_colCSR, float* d_valCSR)
{
    // TODO: Allocate row counts, launch k_hist_rows, perform an exclusive scan
    // on the host (or device), then launch k_stable_scatter_single to emit the
    // CSR structure.  Ensure rowPtr[m] == nnz and input ordering within each
    // row is preserved.

    if (m <= 0) {
        int zero = 0;
        cudaMemcpy(d_rowPtr, &zero, sizeof(int), cudaMemcpyHostToDevice);
        return;
    }

    // Placeholder touch so the stub compiles when invoked by tests.
    cudaMemset(d_colCSR, 0, nnz * sizeof(int));
    cudaMemset(d_valCSR, 0, nnz * sizeof(float));
}
            ```


Question:
Task: ch15-bfs-push-single
            Target file: student_kernel.cu

            Implementation requirements:
- Initialize levels to INF_LVL, set level[src] = 0, and seed the frontier array.
- In each iteration, run a push kernel that traverses neighbors with atomicCAS discovery and atomicAdd enqueue.
- Swap frontier buffers between iterations until the frontier is empty, then free temporary buffers.

            Here is the current student_kernel.cu:

            ```cuda
            // ch15-bfs-push-single / student_kernel.cu
#include <cuda_runtime.h>
#include <limits.h>

#ifndef INF_LVL
#define INF_LVL 0x3f3f3f3f
#endif

__global__ void _init_levels(int* __restrict__ level, int V, int src){
    int i = blockIdx.x * blockDim.x + threadIdx.x;
    if (i < V) {
        level[i] = INF_LVL;
    }
    if (src >= 0 && src < V && i == src) {
        level[i] = 0;
    }
}

__global__ void bfs_push_kernel(const int* __restrict__ row_ptr,
                                const int* __restrict__ col_idx,
                                const int* __restrict__ frontier,
                                int frontier_size,
                                int* __restrict__ next_frontier,
                                int* __restrict__ next_frontier_size,
                                int* __restrict__ level,
                                int cur_level)
{
    // TODO: Visit outgoing edges of the frontier, atomically claim newly
    // discovered vertices, and append them to next_frontier.
    if (row_ptr && col_idx && frontier && next_frontier && next_frontier_size && level) {
        (void)frontier_size;
        (void)cur_level;
    }
}

extern "C" void bfs_push_gpu(const int* d_row_ptr,
                             const int* d_col_idx,
                             int V, int E,
                             int src,
                             int* d_level)
{
    (void)d_row_ptr;
    (void)d_col_idx;
    (void)E;

    if (V <= 0) {
        return;
    }

    dim3 b(256), g((V + b.x - 1) / b.x);
    _init_levels<<<g, b>>>(d_level, V, src);
    // TODO: Allocate frontier buffers, iterate until the frontier is empty, and
    // free any resources allocated on the device.
}
            ```


Question:
Task: ch15-bfs-pull-single
            Target file: student_kernel.cu

            Implementation requirements:
- Initialize levels to INF_LVL, set level[src] = 0, and seed the in_frontier bitmap.
- Each iteration clears out_frontier, runs a pull kernel that scans neighbors of undiscovered vertices, and counts discoveries.
- Swap bitmaps when progress occurs and terminate when no new vertices are found.
- Release all temporary device allocations at exit.

            Here is the current student_kernel.cu:

            ```cuda
            // ch15-bfs-pull-single / student_kernel.cu
#include <cuda_runtime.h>
#include <limits.h>

#ifndef INF_LVL
#define INF_LVL 0x3f3f3f3f
#endif

__global__ void _init_levels_pull(int* __restrict__ level, int V, int src){
    // TODO: Initialize the level array and set the source distance to zero.
    int i = blockIdx.x * blockDim.x + threadIdx.x;
    if (i < V) {
        level[i] = INF_LVL;
    }
    if (src >= 0 && src < V && i == src) {
        level[i] = 0;
    }
}

__global__ void _clear_bitmap(unsigned char* __restrict__ bm, int V){
    int i = blockIdx.x * blockDim.x + threadIdx.x;
    if (i < V) {
        bm[i] = 0;
    }
}

__global__ void _set_single(unsigned char* __restrict__ bm, int idx){
    if (threadIdx.x == 0 && blockIdx.x == 0 && idx >= 0) {
        bm[idx] = 1;
    }
}

__global__ void bfs_pull_kernel(const int* __restrict__ row_ptr,
                                const int* __restrict__ col_idx,
                                const unsigned char* __restrict__ in_frontier,
                                unsigned char* __restrict__ out_frontier,
                                int* __restrict__ level,
                                int cur_level,
                                int V,
                                int* __restrict__ next_count)
{
    // TODO: For undiscovered vertices, scan neighbors to detect if any reside
    // in the current frontier bitmap.
    int v = blockIdx.x * blockDim.x + threadIdx.x;
    if (v < V) {
        (void)row_ptr;
        (void)col_idx;
        (void)in_frontier;
        (void)out_frontier;
        (void)level;
        (void)cur_level;
        (void)next_count;
    }
}

extern "C" void bfs_pull_gpu(const int* d_row_ptr,
                             const int* d_col_idx,
                             int V, int E,
                             int src,
                             int* d_level)
{
    (void)d_row_ptr;
    (void)d_col_idx;
    (void)E;

    if (V <= 0) {
        return;
    }

    dim3 b(256), g((V + b.x - 1) / b.x);
    _init_levels_pull<<<g, b>>>(d_level, V, src);
    // TODO: Allocate bitmaps, iterate levels until no new vertices are found.
}
            ```


Question:
Task: ch15-bfs-direction-optimized-single
            Target file: student_kernel.cu

            Implementation requirements:
- Initialize levels to INF_LVL (except src) and allocate frontier buffers plus a bitmap.
- Implement push and pull kernels that discover neighbors with atomicCAS/bitmap checks.
- Switch to pull mode when frontier_size > V/16 and back to push when frontier_size < V/64.
- Loop until the frontier is empty, freeing temporary buffers afterwards.

            Here is the current student_kernel.cu:

            ```cuda
            // ch15-bfs-direction-optimized-single / student_kernel.cu
#include <cuda_runtime.h>
#include <limits.h>

#ifndef INF_LVL
#define INF_LVL 0x3f3f3f3f
#endif

__global__ void _init_levels(int* __restrict__ level, int V){
    // TODO: Initialize levels to INF_LVL.
    int i = blockIdx.x * blockDim.x + threadIdx.x;
    if (i < V) {
        level[i] = INF_LVL;
    }
}

__global__ void _clear_bitmap(unsigned char* __restrict__ bm, int V){
    // TODO: Zero bitmap entries in parallel.
    int i = blockIdx.x * blockDim.x + threadIdx.x;
    if (i < V) {
        bm[i] = 0;
    }
}

__global__ void _mark_bitmap_from_list(const int* __restrict__ list, int n,
                                       unsigned char* __restrict__ bm){
    // TODO: Mark current frontier vertices in the bitmap.
    int i = blockIdx.x * blockDim.x + threadIdx.x;
    if (i < n) {
        (void)list[i];
        (void)bm;
    }
}

__global__ void k_push(const int* __restrict__ row_ptr,
                       const int* __restrict__ col_idx,
                       const int* __restrict__ frontier,
                       int frontier_size,
                       int* __restrict__ next_frontier,
                       int* __restrict__ next_size,
                       int* __restrict__ level,
                       int cur_level)
{
    // TODO: Push-based relaxation using atomicCAS/atomicAdd.
    if (row_ptr && col_idx && frontier && next_frontier && next_size && level) {
        (void)frontier_size;
        (void)cur_level;
    }
}

__global__ void k_pull(const int* __restrict__ row_ptr,
                       const int* __restrict__ col_idx,
                       const unsigned char* __restrict__ in_frontier,
                       int* __restrict__ next_frontier,
                       int* __restrict__ next_size,
                       int* __restrict__ level,
                       int cur_level,
                       int V)
{
    // TODO: Pull-based relaxation that scans undiscovered vertices.
    if (row_ptr && col_idx && in_frontier && next_frontier && next_size && level) {
        (void)cur_level;
        (void)V;
    }
}

extern "C" void bfs_direction_optimized_gpu(const int* d_row_ptr,
                                            const int* d_col_idx,
                                            int V, int E,
                                            int src,
                                            int* d_level)
{
    // TODO: Alternate between push and pull phases based on the frontier size
    // thresholds described in the README.
    if (V <= 0 || E < 0) {
        return;
    }

    dim3 b(256), g((V + b.x - 1) / b.x);
    _init_levels<<<g, b>>>(d_level, V);
    if (src >= 0 && src < V) {
        cudaMemcpy(d_level + src, &src, sizeof(int), cudaMemcpyHostToDevice);
    }
}
            ```


Question:
Task: ch15-bfs-edge-centric-single
            Target file: student_kernel.cu

            Implementation requirements:
- Initialize levels to INF_LVL with level[src] = 0.
- Optionally precompute an edge->vertex mapping for efficient edge traversal.
- Iteratively launch an edge-centric kernel that marks newly discovered vertices and sets an activity flag.
- Stop when no edge discovers new vertices; CSR arrays must remain unchanged.

            Here is the current student_kernel.cu:

            ```cuda
            // ch15-bfs-edge-centric-single / student_kernel.cu
#include <cuda_runtime.h>
#include <limits.h>

#ifndef INF_LVL
#define INF_LVL 0x3f3f3f3f
#endif

__global__ void _init_levels_edge(int* __restrict__ level, int V, int src){
    // TODO: Initialize the level array and seed the source vertex.
    int i = blockIdx.x * blockDim.x + threadIdx.x;
    if (i < V) {
        level[i] = INF_LVL;
    }
    if (i == src) {
        level[i] = 0;
    }
}

__global__ void bfs_edge_centric_kernel(const int* __restrict__ row_ptr,
                                        const int* __restrict__ col_idx,
                                        int* __restrict__ level,
                                        int cur_level,
                                        int E,
                                        int* __restrict__ active_found)
{
    // TODO: Iterate one edge per thread, discover newly reached vertices, and
    // signal progress via active_found.
    if (blockIdx.x * blockDim.x + threadIdx.x < E) {
        (void)row_ptr;
        (void)col_idx;
        (void)level;
        (void)cur_level;
        (void)active_found;
    }
}

extern "C" void bfs_edge_centric_gpu(const int* d_row_ptr,
                                     const int* d_col_idx,
                                     int V, int E,
                                     int src,
                                     int* d_level)
{
    // TODO: Alternate between edge-centric relaxations until no progress is
    // made.  Maintain device-side flags and handle empty graphs gracefully.
    if (V <= 0 || E < 0) {
        return;
    }

    dim3 b(128);
    dim3 g((V + b.x - 1) / b.x);
    _init_levels_edge<<<g, b>>>(d_level, V, src);
}
            ```


Question:
Task: ch16-conv2d-forward-single
            Target file: student_kernel.cu

            Implementation requirements:
- Decode each thread id into (n, oc, oh, ow) for NCHW output layout.
- Accumulate input[n,c,ih,iw] * weight[oc,c,kh,kw] over channels and kernel, skipping out-of-bounds taps.
- Add bias[oc] if provided and write to output; guard threads with tid >= total outputs.

            Here is the current student_kernel.cu:

            ```cuda
            // ch16-conv2d-forward-single / student_kernel.cu
#include <cuda_runtime.h>

extern "C" __global__
void conv2d_forward_kernel(
    const float* __restrict__ input,
    const float* __restrict__ weight,
    const float* __restrict__ bias,
    float* __restrict__ output,
    int N, int C, int H, int W,
    int OC, int kernel_h, int kernel_w,
    int stride_h, int stride_w,
    int out_h, int out_w)
{
    (void)input;
    (void)weight;
    (void)bias;
    (void)N; (void)C; (void)H; (void)W;
    (void)OC; (void)kernel_h; (void)kernel_w;
    (void)stride_h; (void)stride_w;
    (void)out_h; (void)out_w;

    // TODO: Map each thread to a unique (n, oc, oh, ow) output element and
    // perform the convolution accumulation over the input tensor.

    int tid = blockIdx.x * blockDim.x + threadIdx.x;
    long long total = 1LL * N * OC * out_h * out_w;
    if ((long long)tid >= total) {
        return;
    }

    if (output) {
        output[tid] = 0.0f; // placeholder value so the stub compiles
    }
}
            ```


Question:
Task: ch16-maxpool2d-forward-single
            Target file: student_kernel.cu

            Implementation requirements:
- Assign one thread per output element (n, c, oh, ow).
- Scan the pooling window, tracking both maximum value and argmax index (kh*kernel_w + kw).
- Store the max into output and argmax into indices; initialize indices to -1.
- Guard threads with tid >= total outputs.

            Here is the current student_kernel.cu:

            ```cuda
            // ch16-maxpool2d-forward-single / student_kernel.cu
#include <cuda_runtime.h>

extern "C" __global__
void maxpool2d_forward_kernel(const float* input, float* output, int* indices,
                              int batch_size, int channels,
                              int height, int width,
                              int kernel_h, int kernel_w,
                              int stride_h, int stride_w,
                              int out_h, int out_w)
{
    (void)input;
    (void)batch_size; (void)channels;
    (void)height; (void)width;
    (void)kernel_h; (void)kernel_w;
    (void)stride_h; (void)stride_w;
    (void)out_h; (void)out_w;

    // TODO: For each thread, compute the maximum value within the pooling
    // window and track the argmax (flattened kernel index).

    int tid = blockIdx.x * blockDim.x + threadIdx.x;
    long long total = 1LL * batch_size * channels * out_h * out_w;
    if ((long long)tid >= total) {
        return;
    }

    if (output) {
        output[tid] = 0.0f;
    }
    if (indices) {
        indices[tid] = -1;
    }
}
            ```


Question:
Task: ch17-fhd-accumulate-single
Target file: student_kernel.cu

Here is the current skeleton you must complete:

```cuda
// ch17-fhd-accumulate-single/student_kernel.cu
#include <cuda_runtime.h>
#include <math_constants.h>

extern "C" __global__
void fhd_accumulate_kernel(const float* __restrict__ rPhi,
                           const float* __restrict__ iPhi,
                           const float* __restrict__ rD,
                           const float* __restrict__ iD,
                           const float* __restrict__ kx,
                           const float* __restrict__ ky,
                           const float* __restrict__ kz,
                           const float* __restrict__ x,
                           const float* __restrict__ y,
                           const float* __restrict__ z,
                           int M, int N,
                           float* __restrict__ rFhD,
                           float* __restrict__ iFhD)
{
    // TODO:
    // - 1 thread per n (global id = blockIdx.x*blockDim.x + threadIdx.x)
    // - Guard: if (n >= N) return;
    // - Load x[n], y[n], z[n] into registers
    // - Accumulate over m=0..M-1:
    //      rmu = rPhi[m]*rD[m] + iPhi[m]*iD[m]
    //      imu = rPhi[m]*iD[m] - iPhi[m]*rD[m]
    //      ang = 2*pi*(kx[m]*xn + ky[m]*yn + kz[m]*zn)
    //      c = cosf(ang); s = sinf(ang)
    //      r_acc += rmu*c - imu*s
    //      i_acc += imu*c + rmu*s
    // - Finally: rFhD[n] += r_acc; iFhD[n] += i_acc;

    // TODO: Implement the FHD accumulation kernel here
}
```


Question:
Task: ch17-fhd-fission-two-kernels-single
Target file: student_kernel.cu

Goal: Split the fissioned FHD computation into two kernels. First, compute the complex products `(rMu, iMu)` for every frequency `m`. Second, accumulate those precomputed values across all `m` for each spatial sample `n`, matching the fused reference implementation.

Key requirements:
- Keep the inputs read-only and accumulate results into `rFhD` / `iFhD` using `+=`.
- Use the provided TWO_PI constant when computing `ang`.
- Launch `compute_mu_kernel` with one thread per `m`; launch `fhd_accumulate_mu_kernel` with one thread per `n`.
- Preserve the maths shown in the comments and guard index bounds.

Skeleton:
```cuda
// ch17-fhd-fission-two-kernels-single/student_kernel.cu
#include <cuda_runtime.h>

// TODO (A): compute_mu_kernel
// Each thread handles one m (if m < M):
//   rMu[m] = rPhi[m]*rD[m] + iPhi[m]*iD[m]
//   iMu[m] = rPhi[m]*iD[m] - iPhi[m]*rD[m]
extern "C" __global__
void compute_mu_kernel(const float* __restrict__ rPhi,
                       const float* __restrict__ iPhi,
                       const float* __restrict__ rD,
                       const float* __restrict__ iD,
                       int M,
                       float* __restrict__ rMu,
                       float* __restrict__ iMu)
{
    // TODO: Implement complex multiplication for each m
    // Hint: int m = blockIdx.x * blockDim.x + threadIdx.x;
}

// TODO (B): fhd_accumulate_mu_kernel
// One thread per n; loop over m using precomputed rMu/iMu.
// Accumulate into rFhD[n], iFhD[n] using Fourier transform formula.
//
// IMPORTANT FORMULAS:
// 1. Angle calculation must include TWO_PI (2pi):
//    const float TWO_PI = 6.2831853071795864769f;
//    float ang = TWO_PI * (kx[m]*xn + ky[m]*yn + kz[m]*zn);
//
// 2. Use ACCUMULATION (+=), not assignment (=), because this kernel may be
//    called multiple times:
//    rFhD[n] += r_acc;  // NOT rFhD[n] = r_acc;
//    iFhD[n] += i_acc;  // NOT iFhD[n] = i_acc;
//
// Algorithm:
//   int n = blockIdx.x * blockDim.x + threadIdx.x;
//   if (n < N) {
//       float r_acc = 0.0f, i_acc = 0.0f;
//       for (int m = 0; m < M; ++m) {
//           float ang = TWO_PI * (kx[m]*x[n] + ky[m]*y[n] + kz[m]*z[n]);
//           float c = cosf(ang), s = sinf(ang);
//           r_acc += rMu[m] * c - iMu[m] * s;
//           i_acc += iMu[m] * c + rMu[m] * s;
//       }
//       rFhD[n] += r_acc;
//       iFhD[n] += i_acc;
//   }
extern "C" __global__
void fhd_accumulate_mu_kernel(const float* __restrict__ rMu,
                              const float* __restrict__ iMu,
                              const float* __restrict__ kx,
                              const float* __restrict__ ky,
                              const float* __restrict__ kz,
                              const float* __restrict__ x,
                              const float* __restrict__ y,
                              const float* __restrict__ z,
                              int M, int N,
                              float* __restrict__ rFhD,
                              float* __restrict__ iFhD)
{
    // TODO: Implement accumulation using precomputed rMu, iMu (see formula above)
}
```


Question:
Task: ch18-energy-scatter-single
Target file: student_kernel.cu

Here is the current skeleton you must complete:

```cuda
// ch18-energy-scatter-single / student_kernel.cu
//
// Implement Fig. 18.5 (SCATTER): one thread per atom, looping over all (i,j)
// grid points on a fixed z-slice and ATOMICALLY accumulating into energygrid.
//
// CONTRACT
// - Constant memory holds a *chunk* of atoms: __constant__ float atoms[CHUNK_SIZE*4]
//   as (x,y,z,charge) AoS, 4 floats per atom.
// - The test harness uploads atoms chunk-by-chunk via cudaMemcpyToSymbol and then
//   launches the kernel once per chunk, accumulating into the same output slice.
// - Kernel params:
//     energygrid    : pointer to [grid.x * grid.y * grid.z] floats
//     grid          : logical 3D grid dimensions (x,y,z) for indexing
//     gridspacing   : spacing used to compute x = i*gridspacing, y = j*gridspacing
//     z             : world-space z coordinate of the slice (must be multiple of gridspacing)
//     atoms_in_chunk: number of atoms loaded in constant memory for this launch (<= CHUNK_SIZE)
//     start_atom    : global offset of first atom of the chunk (not needed for correct math;
//                     included so your signature matches reference; you may ignore it)
// - Must use atomicAdd on energygrid writes (scatter means threads collide on same cell).
// - Bounds: guard i in [0,grid.x), j in [0,grid.y).
//
// HINTS
// - Compute k = int(z / gridspacing) once per kernel; assume z aligns.
// - Thread id tid covers one atom in [0, atoms_in_chunk).
// - Load (ax,ay,az,q) from constant memory as atoms[4*tid + {0,1,2,3}].
// - For each j row, precompute y and (y-ay), (z-az) and reuse (dy*dy + dz*dz).
// - Use sqrtf for single-precision.
// - Use 1D blocks and grids for atom threads (e.g., blockDim.x = 256).
//
// PERFORMANCE is not graded here-correctness, safety (no OOB), and atomicity are.

#ifndef CHUNK_SIZE
#define CHUNK_SIZE 256
#endif

__constant__ float atoms[CHUNK_SIZE * 4];

extern "C" __global__
void cenergyScatterKernel(float* __restrict__ energygrid,
                          dim3 grid,
                          float gridspacing,
                          float z,
                          int atoms_in_chunk,
                          int /*start_atom_unused*/) {
    // TODO: Implement scatter kernel (one thread per atom)
    // 1. Get thread ID and bounds check
    // 2. Load atom data from constant memory
    // 3. Compute z-slice index k
    // 4. Loop over all (i,j) grid points in the slice
    // 5. For each grid point, compute distance and contribution
    // 6. Use atomicAdd to accumulate into energygrid
}
```


Question:
Task: ch18-energy-gather-single
Target file: student_kernel.cu

Goal: Implement the gather variant of the molecular energy kernel. Each thread owns one grid cell, loops over the atoms in the constant-memory chunk, accumulates a private sum, and writes exactly once to the output grid.

Key requirements:
- Use a 2D launch (threadIdx.x/y) to cover the grid tile and guard indices.
- Loop over all atoms in the uploaded chunk and accumulate into a private `energy` value.
- Write exactly one `+=` update per grid cell (no atomics).
- Follow the provided formulas for indexing and distance accumulation.

Skeleton:
```cuda
// ch18-energy-gather-single / student_kernel.cu
//
// Implement Fig. 18.6 (GATHER): one thread per grid point (on a fixed z-slice).
// Each thread loops over all atoms in the current constant-memory chunk and
// accumulates a private sum, then writes exactly once to energygrid (+=).
//
// CONTRACT
// - Constant memory holds a *chunk* of atoms: __constant__ float atoms[CHUNK_SIZE*4].
// - Kernel params:
//     energygrid    : pointer to [grid.x * grid.y * grid.z] floats
//     grid          : logical 3D grid dimensions (x,y,z) for indexing
//     gridspacing   : spacing for x=i*h, y=j*h
//     z             : world-space z of the slice
//     atoms_in_chunk: number of atoms currently loaded (<= CHUNK_SIZE)
//     start_atom    : global offset of first atom in the chunk (not needed here)
// - NO atomics: each thread owns its output cell and does `energygrid[idx] += local_sum`.
// - 2D launch is expected (e.g., block=(16,16)).
//
// HINTS
// - Compute (dy*dy + dz*dz) per row and reuse where reasonable.
// - Use sqrtf and a small denominator clamp to avoid division by zero.

#ifndef CHUNK_SIZE
#define CHUNK_SIZE 256
#endif

__constant__ float atoms[CHUNK_SIZE * 4];

extern "C" __global__
void cenergyGatherKernel(float* __restrict__ energygrid,
                         dim3 grid,
                         float gridspacing,
                         float z,
                         int atoms_in_chunk,
                         int /*start_atom_unused*/) {
    // TODO: Implement gather kernel (one thread per grid cell)
    // 1. Get 2D thread indices (i,j) for grid position
    // 2. Bounds check against grid dimensions (grid.x, grid.y)
    // 3. Compute z-slice index and validate:
    //    int k = int(z / gridspacing);
    //    if (k < 0 || k >= (int)grid.z) return;
    // 4. Compute world-space coordinates (x = i*gridspacing, y = j*gridspacing)
    // 5. Loop over all atoms in the current chunk
    // 6. For each atom, compute distance and contribution
    // 7. Compute 3D linearized index into energygrid:
    //    size_t idx = (size_t)grid.x * grid.y * k + (size_t)grid.x * j + (size_t)i;
    // 8. Accumulate private sum, then write once: energygrid[idx] += sum
}
```


Question:
Task: ch18-energy-gather-coarsened-single
            Target file: student_kernel.cu

            Implementation requirements:
- Compute base_i and j from block/thread indices and determine k from z/gridspacing.
- Maintain a register array of size COARSEN_FACTOR to accumulate per-thread contributions.
- Loop over atoms_in_chunk, accumulating q / max(distance, 1e-12f) for owned cells.
- Write each owned grid cell exactly once without atomics, guarding grid bounds.

            Here is the current student_kernel.cu:

            ```cuda
            // ch18-energy-gather-coarsened-single / student_kernel.cu
//
// Implement a coarsened gather kernel (Fig. 18.8).  The reference solution
// shows the full algorithm; here we deliberately leave the core computation as
// a TODO so the student model must fill it in.

#ifndef CHUNK_SIZE
#define CHUNK_SIZE 256
#endif
#ifndef COARSEN_FACTOR
#define COARSEN_FACTOR 8
#endif

__constant__ float atoms[CHUNK_SIZE * 4];

extern "C" __global__
void cenergyCoarsenKernel(float* __restrict__ energygrid,
                          dim3 grid,
                          float gridspacing,
                          float z,
                          int /*atoms_in_chunk*/,
                          int /*start_atom_unused*/) {
    int base_i = blockIdx.x * (blockDim.x * COARSEN_FACTOR) + threadIdx.x * COARSEN_FACTOR;
    int j      = blockIdx.y * blockDim.y + threadIdx.y;

    if (j < 0 || j >= (int)grid.y) {
        return;
    }

    int k = int(z / gridspacing);
    if (k < 0 || k >= (int)grid.z) {
        return;
    }

    // TODO: Implement thread-coarsened energy accumulation:
    // - Each thread processes COARSEN_FACTOR grid points (base_i to base_i + COARSEN_FACTOR - 1)
    // - For each grid point (i, j, k), accumulate energy contributions from all atoms in constant memory
    // - Atoms are stored in constant memory as [x, y, z, charge] in structure-of-arrays layout
    // - Compute distance from each atom to grid point: dx, dy, dz
    // - Energy contribution: charge / sqrt(dx^2 + dy^2 + dz^2)
    // - Add accumulated energy to energygrid[idx] (no atomics needed - each thread owns its points)
    // - Use proper bounds checking for i in [0, grid.x)
    //
    // Hints:
    // - Grid point coordinates: x = i * gridspacing, y = j * gridspacing, z already given
    // - Atom coordinates: atoms[a*4+0], atoms[a*4+1], atoms[a*4+2], atoms[a*4+3] (charge)
    // - 3D array index: idx = k * grid.x * grid.y + j * grid.x + i
}
            ```


Question:
Task: ch18-energy-gather-coarsened-coalesced-single
            Target file: student_kernel.cu

            Implementation requirements:
- Compute base_i, j, and k, and use a register array of length COARSEN_FACTOR to accumulate contributions.
- Stage per-thread results into shared memory for coalesced writes.
- After accumulation, cooperatively flush the shared buffer back to global memory with strided writes.
- Avoid atomics and guard all grid bounds.

            Here is the current student_kernel.cu:

            ```cuda
            // ch18-energy-gather-coarsened-coalesced-single / student_kernel.cu
//
// Implement a coarsened gather kernel that stages results in shared memory and
// flushes them with coalesced writes.  This starter file intentionally leaves the
// body empty so the model must supply the full algorithm.

#ifndef CHUNK_SIZE
#define CHUNK_SIZE 256
#endif
#ifndef COARSEN_FACTOR
#define COARSEN_FACTOR 8
#endif

__constant__ float atoms[CHUNK_SIZE * 4];

extern "C" __global__
void cenergyCoarsenCoalescedKernel(float* __restrict__ energygrid,
                                   dim3 grid,
                                   float gridspacing,
                                   float z,
                                   int atoms_in_chunk,
                                   int /*start_atom_unused*/) {
    int base_i = blockIdx.x * (blockDim.x * COARSEN_FACTOR) + threadIdx.x * COARSEN_FACTOR;
    int j      = blockIdx.y * blockDim.y + threadIdx.y;

    if (j < 0 || j >= (int)grid.y) {
        return;
    }

    int k = int(z / gridspacing);
    if (k < 0 || k >= (int)grid.z) {
        return;
    }

    // TODO: Implement coarsened energy accumulation with shared memory and coalesced writes:
    //
    // IMPORTANT: This kernel is called multiple times (once per atom chunk).
    // You MUST use += to accumulate results, NOT = to overwrite!
    //
    // Step 1: Declare shared memory buffer
    // - Size: blockDim.x * blockDim.y * COARSEN_FACTOR floats
    // - Layout: [threadIdx.y][threadIdx.x * COARSEN_FACTOR + c] for coalesced access
    //
    // Step 2: Accumulate energy for COARSEN_FACTOR grid points
    // - For each c in [0, COARSEN_FACTOR), compute i = base_i + c
    // - For each grid point (i, j, k), loop over atoms_in_chunk atoms (NOT CHUNK_SIZE!)
    // - Compute distance and energy contribution (charge / distance)
    // - Store accumulated energy in shared memory (NOT directly to global memory yet)
    //
    // Step 3: Synchronize threads
    // - __syncthreads() to ensure all threads finished computation
    //
    // Step 4: Coalesced write to global memory with ACCUMULATION
    // - Reorganize shared memory data to enable coalesced writes
    // - Each thread writes COARSEN_FACTOR consecutive values from shared memory
    // - USE += NOT =: energygrid[idx] += value  (kernel called multiple times!)
    // - This ensures warps write contiguous memory locations (coalesced access)
    //
    // Hints:
    // - Shared memory avoids uncoalesced scattered writes
    // - Atoms layout: atoms[a*4+0], atoms[a*4+1], atoms[a*4+2] (x,y,z), atoms[a*4+3] (charge)
    // - Grid coordinates: x = i * gridspacing, y = j * gridspacing, z given
    // - Global index: idx = k * grid.x * grid.y + j * grid.x + i
}
            ```


Question:
Task: ch20-stencil-25pt-single-gpu-single
Target file: student_kernel.cu

Here is the current skeleton you must complete:

```cuda
#include <cuda_runtime.h>

// TODO: Implement a single-GPU axis-aligned 25-point stencil with radius R=4.
// Contract:
//  - Input/Output are dense 3D grids (dimx*dimy*dimz), row-major:
//      idx(i,j,k) = (k*dimy + j)*dimx + i
//  - For interior cells (i,j,k  [4 .. dim-1-4]) compute:
//      out = w0*center + _{d=1..4} w[d] * (d along x + d along y + d along z)
//    with weights: w0=0.5, w1=0.10, w2=0.05, w3=0.025, w4=0.0125
//  - Boundary cells (within 4 of any face) must be copy-through: out=in
//  - Reasonable 3D launch config and synchronization
//
// Suggested steps:
//  1) write idx3 helper
//  2) in-kernel boundary test => copy-through
//  3) accumulate using a small unrolled loop d=1..4
//  4) host wrapper launches kernel

static inline __device__ size_t idx3(int i,int j,int k,int dx,int dy){
    return (size_t(k)*dy + j)*dx + i;
}

__global__ void stencil25_kernel(const float* __restrict__ in,
                                 float* __restrict__ out,
                                 int dimx, int dimy, int dimz)
{
    // TODO
}

extern "C" void stencil25_single_gpu(const float* d_in, float* d_out,
                                     int dimx, int dimy, int dimz)
{
    // TODO: choose block/grid and launch kernel, then cudaDeviceSynchronize()
}
```


Question:
Task: ch20-stencil-25pt-slab-stage1-boundary
Target file: student_kernel.cu

Goal: Update only the Stage-1 boundary planes (the first and last four owned slices) of a 3D 25-point stencil on a slab with halo padding. Leave interior planes untouched; rely on halos that already exist for this phase.

Key requirements:
- Operate on input `d_in` / output `d_out` arranged with four halo planes on each side (`owned z  [4 .. 4+dimz-1]`).
- Compute stencil weights using the provided coefficients for 1..4 offsets in x, y, and z.
- When i or j are within the halo distance, copy-through rather than reading out-of-bounds.
- Do not modify interior or halo planes; only Stage-1 boundary planes should be written.

Skeleton:
```cuda
#include <cuda_runtime.h>

// TODO: Implement Stage-1 boundary update for axis-aligned 25-point stencil (R=4)
// Local z layout has halos: total_z = dimz + 8
// Owned region z  [4 .. 4+dimz-1]
// Stage-1 planes: [4..7] and [4+dimz-4 .. 4+dimz-1]
// Within Stage-1 planes:
//   - For i/j interior (>=4 and <dim-4) compute stencil
//   - For i/j near faces, copy-through
// Do not write interior planes or halos.
//
// STENCIL WEIGHTS: Use distance-based weighted stencil (NOT simple averaging!)
// - w0 = 0.5   (center point)
// - w1 = 0.10  (distance 1 neighbors: 1 in each axis)
// - w2 = 0.05  (distance 2 neighbors: 2 in each axis)
// - w3 = 0.025 (distance 3 neighbors: 3 in each axis)
// - w4 = 0.0125 (distance 4 neighbors: 4 in each axis)
//
// Formula: out[i,j,k] = w0 * in[i,j,k]
//                     + w1 * (in[i1,j,k] + in[i,j1,k] + in[i,j,k1])
//                     + w2 * (in[i2,j,k] + in[i,j2,k] + in[i,j,k2])
//                     + w3 * (in[i3,j,k] + in[i,j3,k] + in[i,j,k3])
//                     + w4 * (in[i4,j,k] + in[i,j4,k] + in[i,j,k4])

static inline __device__ size_t idx3(int i,int j,int k,int dx,int dy){
    return (size_t(k)*dy + j)*dx + i;
}

__global__ void stencil25_stage1_kernel(const float* __restrict__ in,
                                        float* __restrict__ out,
                                        int dimx,int dimy,int dimz)
{
    // TODO
}

extern "C" void stencil25_stage1_boundary(const float* d_in, float* d_out,
                                          int dimx,int dimy,int dimz)
{
    // TODO: launch 3D grid over local z  [0 .. dimz+7]
}
```


Question:
Task: ch20-mpi-halo-pack-unpack
Target file: student_kernel.cu

Here is the current skeleton you must complete:

```cuda
#include <cuda_runtime.h>

// TODOs:
// Implement two host wrappers that launch simple 2D grids:
//
// 1) halo_pack_boundaries(d_grid, dimx,dimy,dimz, d_left_send, d_right_send)
//    - Read 4 owned boundary planes and pack into left/right buffers.
//      Owned z range is [4 .. 4+dimz-1]. Pack planes:
//         left : k = 4 + p,           p  [0..3]
//         right: k = (4+dimz-4) + p,  p  [0..3]
//    - Packed layout is plane-major then row-major:
//         pack_idx(p,j,i) = (p*dimy + j)*dimx + i
//
// 2) halo_unpack_to_halos(d_grid, dimx,dimy,dimz, d_left_recv, d_right_recv)
//    - Write left_recv to left halo k = 0..3  (k = 0 + p)
//    - Write right_recv to right halo k = dimz+4 .. dimz+7 (k = dimz+4 + p)
//    - Same pack_idx layout for sources.
//
// Keep the rest of the grid untouched.

static inline __device__ size_t idx3(int i,int j,int k,int dx,int dy){
    return (size_t(k)*dy + j)*dx + i;
}
static inline __device__ size_t pack_idx(int p,int j,int i,int dx,int dy){
    return (size_t(p)*dy + j)*dx + i;
}

__global__ void k_pack_student(const float* __restrict__ grid,
                               int dimx,int dimy,int dimz,
                               float* __restrict__ left_send,
                               float* __restrict__ right_send)
{
    // TODO: implement (mirror reference description above)
}

__global__ void k_unpack_student(float* __restrict__ grid,
                                 int dimx,int dimy,int dimz,
                                 const float* __restrict__ left_recv,
                                 const float* __restrict__ right_recv)
{
    // TODO: implement (mirror reference description above)
}

extern "C" void halo_pack_boundaries(const float* d_grid,
                                     int dimx,int dimy,int dimz,
                                     float* d_left_send,
                                     float* d_right_send)
{
    // TODO: choose block(16,16) grid(ceil) and launch k_pack_student, then sync
}

extern "C" void halo_unpack_to_halos(float* d_grid,
                                     int dimx,int dimy,int dimz,
                                     const float* d_left_recv,
                                     const float* d_right_recv)
{
    // TODO: choose block(16,16) grid(ceil) and launch k_unpack_student, then sync
}
```


Question:
Task: ch20-stencil-25pt-slab-stage2-interior
Target file: student_kernel.cu

Goal: Implement the Stage-2 interior pass for the 3D 25-point stencil. This pass updates only the interior owned planes (excluding the four boundary planes already handled in Stage-1).

Key requirements:
- Owned region indices: `[4 .. 4+dimz-1]`; Stage-2 updates planes `k  [8 .. 4+dimz-5]`.
- Use the same stencil weights (w0..w4) applied to 1..4 offsets in each dimension.
- Respect copy-through behaviour near x/y faces inside the interior range.
- Write nothing outside the designated interior planes.

Skeleton:
```cuda
#include <cuda_runtime.h>

// TODO: Implement Stage-2 interior update for the 25-point stencil (R=4).
// Local z extent = dimz + 8. Owned z = [4 .. 4+dimz-1].
// Stage-2 interior planes: k  [8 .. (4+dimz-1)-4].
// For i/j edges (i<4 || i>=dimx-4 || j<4 || j>=dimy-4) copy-through.
// Do not touch halos or Stage-1 planes.
//
// STENCIL WEIGHTS: Use distance-based weighted stencil (NOT simple averaging!)
// - w0 = 0.5   (center point)
// - w1 = 0.10  (distance 1 neighbors: 1 in each axis)
// - w2 = 0.05  (distance 2 neighbors: 2 in each axis)
// - w3 = 0.025 (distance 3 neighbors: 3 in each axis)
// - w4 = 0.0125 (distance 4 neighbors: 4 in each axis)
//
// Formula: out[i,j,k] = w0 * in[i,j,k]
//                     + w1 * (in[i1,j,k] + in[i,j1,k] + in[i,j,k1])
//                     + w2 * (in[i2,j,k] + in[i,j2,k] + in[i,j,k2])
//                     + w3 * (in[i3,j,k] + in[i,j3,k] + in[i,j,k3])
//                     + w4 * (in[i4,j,k] + in[i,j4,k] + in[i,j,k4])

static inline __device__ size_t idx3(int i,int j,int k,int dx,int dy){
    return (size_t(k)*dy + j)*dx + i;
}

__global__ void stencil25_stage2_kernel(const float* __restrict__ in,
                                        float* __restrict__ out,
                                        int dimx,int dimy,int dimz)
{
    // TODO
}

extern "C" void stencil25_stage2_interior(const float* d_in, float* d_out,
                                          int dimx,int dimy,int dimz)
{
    // TODO: launch 3D grid and synchronize
}
```


Question:
Task: ch20-mpi-stencil-pipeline-naive
Target file: student_kernel.cu

Here is the current skeleton you must complete:

```cuda
#include <cuda_runtime.h>
#include <vector>
#include <cassert>
#include <cstdio>

// ====== Utilities ======
static inline __host__ __device__
size_t idx3(int i,int j,int k,int dx,int dy){ return (size_t(k)*dy + j)*dx + i; }
static inline void ck(cudaError_t e,const char* m){ if(e!=cudaSuccess){fprintf(stderr,"CUDA %s: %s\n",m,cudaGetErrorString(e)); std::exit(2);} }

// ====== Provided stencil kernels & pack/unpack launchers ======
extern "C" void stencil25_stage1_boundary(const float* d_in, float* d_out,
                                          int dimx,int dimy,int dz_local,
                                          int z_global_beg, int dimz_total);
extern "C" void stencil25_stage2_interior(const float* d_in, float* d_out,
                                          int dimx,int dimy,int dz_local);
extern "C" void halo_pack_boundaries(const float* d_slab_out,
                                     int dimx,int dimy,int dz_local,
                                     float* d_left_send, float* d_right_send);
extern "C" void halo_unpack_to_halos(float* d_slab_out,
                                     int dimx,int dimy,int dz_local,
                                     const float* d_left_recv, const float* d_right_recv);

// ====== Small helpers to scatter/gather between full & slab memory ======
__global__ void k_scatter_from_full(const float* __restrict__ d_in_full,
                                    float* __restrict__ d_slab_in,
                                    int dimx,int dimy,int z0,int dz)
{
    int i=blockIdx.x*blockDim.x+threadIdx.x;
    int j=blockIdx.y*blockDim.y+threadIdx.y;
    int t=blockIdx.z*blockDim.z+threadIdx.z; // local owned z [0..dz-1]
    if(i>=dimx||j>=dimy||t>=dz) return;
    int k_local = 4 + t;
    int k_full  = z0 + t;
    d_slab_in[idx3(i,j,k_local,dimx,dimy)] =
        d_in_full[idx3(i,j,k_full,dimx,dimy)];
}

__global__ void k_gather_to_full(const float* __restrict__ d_slab_out,
                                 float* __restrict__ d_out_full,
                                 int dimx,int dimy,int z0,int dz)
{
    int i=blockIdx.x*blockDim.x+threadIdx.x;
    int j=blockIdx.y*blockDim.y+threadIdx.y;
    int t=blockIdx.z*blockDim.z+threadIdx.z; // local owned z [0..dz-1]
    if(i>=dimx||j>=dimy||t>=dz) return;
    int k_local = 4 + t;
    int k_full  = z0 + t;
    d_out_full[idx3(i,j,k_full,dimx,dimy)] =
        d_slab_out[idx3(i,j,k_local,dimx,dimy)];
}

// ====== YOUR TASK ======
extern "C" void mpi_stencil_pipeline_naive(const float* d_in_full,
                                           float* d_out_full,
                                           int dimx,int dimy,int dimz_total,
                                           int procs)
{
    // TODO: Implement naive MPI-style pipeline for multi-slab 25-point stencil:
    //
    // Requirements:
    // - Partition z-dimension into `procs` slabs (each slab owns dimz_total/procs planes)
    // - Each slab needs halo regions (4 planes on each side for R=4 stencil)
    // - Allocate per-slab buffers with halo space: dimx * dimy * (local_dz + 8)
    // - Scatter input data from full array to per-slab buffers (use k_scatter_from_full kernel)
    // - Compute boundary planes with stage1 kernel (updates 4 planes on each end)
    // - Pack boundary data and exchange halos between neighboring slabs (simulate MPI with cudaMemcpy)
    // - Unpack received halo data into neighbor regions
    // - Compute interior planes with stage2 kernel
    // - Gather results from per-slab buffers back to full array (use k_gather_to_full kernel)
    // - Free all allocated buffers
    //
    // Hints:
    // - Use provided helper kernels: k_scatter_from_full, k_gather_to_full
    // - Use provided packing functions: halo_pack_boundaries, halo_unpack_to_halos
    // - Stage1 kernel handles boundary planes, Stage2 handles interior
    // - Halo exchange is device-to-device cudaMemcpy (simulates MPI)
    (void)d_in_full; (void)d_out_full; (void)dimx; (void)dimy; (void)dimz_total; (void)procs;
}```


Question:
Task: ch20-mpi-stencil-pipeline-cudaaware
Target file: student_kernel.cu

Here is the current skeleton you must complete:

```cuda
#include <cuda_runtime.h>
#include <vector>
#include <cassert>
#include <cstdio>

static inline __host__ __device__
size_t idx3(int i,int j,int k,int dx,int dy){ return (size_t(k)*dy + j)*dx + i; }
static void ck(cudaError_t e,const char* m){ if(e!=cudaSuccess){fprintf(stderr,"CUDA %s: %s\n",m,cudaGetErrorString(e)); std::exit(2);} }

// Stage 1/2, pack/unpack launchers (provided)
extern "C" void stencil25_stage1_boundary(const float* d_in, float* d_out,
                                          int dimx,int dimy,int dz_local,
                                          int z_global_beg, int dimz_total);
extern "C" void stencil25_stage2_interior(const float* d_in, float* d_out,
                                          int dimx,int dimy,int dz_local);
extern "C" void halo_pack_boundaries(const float* d_slab_out,
                                     int dimx,int dimy,int dz_local,
                                     float* d_left_send, float* d_right_send);
extern "C" void halo_unpack_to_halos(float* d_slab_out,
                                     int dimx,int dimy,int dz_local,
                                     const float* d_left_recv, const float* d_right_recv);

// CUDA-aware sendrecv wrapper (device->device)
extern "C" void mpi_cudaaware_sendrecv_device(const float* d_sendbuf, int sendcount,
                                              float* d_recvbuf, int recvcount);

// scatter/gather kernels (provided)
__global__ void k_scatter_from_full(const float* __restrict__ d_in_full,
                                    float* __restrict__ d_slab_in,
                                    int dimx,int dimy,int z0,int dz)
{
    int i=blockIdx.x*blockDim.x+threadIdx.x;
    int j=blockIdx.y*blockDim.y+threadIdx.y;
    int t=blockIdx.z*blockDim.z+threadIdx.z;
    if(i>=dimx||j>=dimy||t>=dz) return;
    int k_local = 4 + t;
    int k_full  = z0 + t;
    d_slab_in[idx3(i,j,k_local,dimx,dimy)] =
        d_in_full[idx3(i,j,k_full,dimx,dimy)];
}
__global__ void k_gather_to_full(const float* __restrict__ d_slab_out,
                                 float* __restrict__ d_out_full,
                                 int dimx,int dimy,int z0,int dz)
{
    int i=blockIdx.x*blockDim.x+threadIdx.x;
    int j=blockIdx.y*blockDim.y+threadIdx.y;
    int t=blockIdx.z*blockDim.z+threadIdx.z;
    if(i>=dimx||j>=dimy||t>=dz) return;
    int k_local = 4 + t;
    int k_full  = z0 + t;
    d_out_full[idx3(i,j,k_full,dimx,dimy)] =
        d_slab_out[idx3(i,j,k_local,dimx,dimy)];
}

extern "C" void mpi_stencil_pipeline_cudaaware(const float* d_in_full,
                                               float* d_out_full,
                                               int dimx,int dimy,int dimz_total,
                                               int procs)
{
    // TODO: Implement CUDA-aware MPI pipeline for multi-slab 25-point stencil:
    //
    // Requirements:
    // - Partition z-dimension into `procs` slabs (each owns dimz_total/procs planes)
    // - Each slab needs halo regions (4 planes on each side for R=4 stencil)
    // - Allocate per-slab buffers with halo space and exchange buffers
    // - Scatter input data from full array to per-slab buffers (use k_scatter_from_full)
    // - Compute boundary planes with stage1 kernel
    // - Pack boundary data for halo exchange
    // - Use CUDA-aware MPI to exchange halos directly between GPU buffers (mpi_cudaaware_sendrecv_device)
    // - Unpack received halo data into neighbor regions
    // - Compute interior planes with stage2 kernel
    // - Gather results back to full array (use k_gather_to_full)
    // - Free all allocated buffers
    //
    // Hints:
    // - CUDA-aware MPI allows direct GPU-to-GPU transfers without host staging
    // - Use provided helper kernels: k_scatter_from_full, k_gather_to_full
    // - Use provided packing functions: halo_pack_boundaries, halo_unpack_to_halos
    // - mpi_cudaaware_sendrecv_device handles bidirectional exchange between neighbors
    (void)d_in_full; (void)d_out_full; (void)dimx; (void)dimy; (void)dimz_total; (void)procs;
}```


Question:
Task: ch21-bezier-dp-parent-child-single
Target file: student_kernel.cu

Here is the current skeleton you must complete:

```cuda
// ch21-bezier-dp-parent-child-single / student_kernel.cu
#include <cuda_runtime.h>
#include <stdint.h>

// Use CUDA builtin float2
#include <vector_types.h>

struct BezierLine {
  float2 CP[3];        // P0, P1, P2
  float2* vertexPos;   // device buffer (allocated in parent via device malloc)
  int     nVertices;   // chosen per line in parent
};

// --- You implement these -----------------------------------------------------

// Geometric curvature proxy: distance from P1 to line P0-P2 (normalized by |P2-P0|)
// Return non-negative curvature (0 for degenerate segment).
__device__ float curvature_of(const float2 P0, const float2 P1, const float2 P2) {
  // TODO: implement robust point-to-segment distance proxy.
  // Hints:
  //   v = P2 - P0
  //   w = P1 - P0
  //   area2 = |v.x*w.y - v.y*w.x|     (2x triangle area)
  //   base  = sqrt(v.x*v.x + v.y*v.y)
  //   curvature ~ area2 / max(base, 1e-8)
  return 0.0f; // TODO
}

// Child kernel: compute tessellated positions for one line lidx.
__global__ void computeBezierLine_child(int lidx, BezierLine* bLines, int nTess) {
  // TODO:
  //  - idx = blockIdx.x*blockDim.x + threadIdx.x
  //  - if idx >= nTess: return
  //  - u = idx / (nTess-1)   (float)
  //  - B0=(1-u)^2, B1=2u(1-u), B2=u^2
  //  - position = B0*P0 + B1*P1 + B2*P2
  //  - write to bLines[lidx].vertexPos[idx]
}

// Parent kernel: choose tessellation density, allocate vertex buffers, launch child.
__global__ void computeBezierLines_parent(BezierLine* bLines, int nLines, int maxTess) {
  // TODO:
  //  - lidx = blockIdx.x*blockDim.x + threadIdx.x; if (lidx>=nLines) return
  //  - compute curvature_of(...)
  //  - nVerts = clamp( int(curv*16.f)+4, 4, maxTess );
  //  - bLines[lidx].nVertices = nVerts;
  //  - bLines[lidx].vertexPos = (float2*)malloc(nVerts * sizeof(float2));
  //      * if malloc returns nullptr, set nVertices=0 and return.
  //  - launch child: <<< (nVerts+31)/32, 32 >>> computeBezierLine_child(lidx, bLines, nVerts);
}
```


Question:
Task: ch21-bezier-dp-free-child-buffers
Target file: student_kernel.cu

Here is the current skeleton you must complete:

```cuda
// ch21-bezier-dp-free-child-buffers / student_kernel.cu
#include <cuda_runtime.h>
#include <stdint.h>

#include <vector_types.h>

struct BezierLine {
  float2 CP[3];
  float2* vertexPos; // device-heap pointer
  int     nVertices;
};

// TODO: Implement idempotent free:
//  - lidx = blockIdx.x*blockDim.x + threadIdx.x; if (lidx>=nLines) return;
//  - if (bLines[lidx].vertexPos != nullptr) { free(ptr); bLines[lidx].vertexPos = nullptr; }
//  - (optional) bLines[lidx].nVertices = 0;
__global__ void freeVertexMem(BezierLine* bLines, int nLines) {
  // TODO
}
```