Upload kernels/benchmark_satu1.cpp with huggingface_hub

kernels/benchmark_satu1.cpp

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+// benchmark_satu1.cpp — IKNN-Rl1-A1 — Combined benchmark SatU1 AVX2 vs AVX-512
+// Version: v1.0
+// Created: 2026-09-03T17:00:00+07:00
+// Status: PUBLISHABLE — EN ONLY
+// Repo: IKNN-Rl1-A1
+
+#include "satu1_common.h"
+#include <iostream>
+#include <random>
+#include <chrono>
+#include <cmath>
+
+// Declare functions from other translation units (we include via header but need extern)
+// Instead include cpp directly for simplicity in this benchmark
+#include "satu1_avx2.cpp"
+#include "satu1_avx512.cpp"
+
+int main() {
+    using namespace iknn::satu1;
+
+    std::cout << "=== IKNN-Rl1-A1 — SatU1 Kernel Benchmark — M1 ===" << std::endl;
+    std::cout << "Repo: IKNN-Rl1-A1 — Integrated Knowledge-phase Neural Network — Recursive Language Iteration 1 — Architecture 1" << std::endl;
+    std::cout << "Hardware: Xeon AVX-512 (VPOPCNTDQ, VBMI2) + Ryzen5 5650U AVX2 reference" << std::endl;
+    std::cout << "Timestamp: " << __DATE__ << " " << __TIME__ << std::endl;
+
+    std::mt19937_64 rng(12345);
+    const int WORDS = 8; // 512 weights
+    uint64_t act[WORDS], w[WORDS];
+    for (int i = 0; i < WORDS; ++i) {
+        act[i] = rng();
+        w[i] = rng();
+    }
+    float alpha = 0.75f;
+
+    // Correctness
+    float naive = compute_satu1_block_naive(act, w, alpha, WORDS);
+    float avx2 = avx2::compute_satu1_block_avx2(act, w, alpha, WORDS);
+    float avx512 = avx512::compute_satu1_block_avx512(act, w, alpha, WORDS);
+
+    std::cout << "[Correctness] Naive: " << naive << " AVX2: " << avx2 << " AVX-512: " << avx512 << std::endl;
+    bool pass = (std::abs(naive-avx2) < 1e-3f) && (std::abs(naive-avx512) < 1e-3f);
+    std::cout << (pass ? "[PASS] All kernels match naive" : "[FAIL] Mismatch") << std::endl;
+    if (!pass) return 1;
+
+    // Benchmark AVX2
+    {
+        const int ITERS = 2000000;
+        auto start = std::chrono::high_resolution_clock::now();
+        float sum = 0;
+        for (int it = 0; it < ITERS; ++it) {
+            act[0] ^= it;
+            sum += avx2::compute_satu1_block_avx2(act, w, alpha, WORDS);
+        }
+        auto end = std::chrono::high_resolution_clock::now();
+        double ms = std::chrono::duration_cast<std::chrono::milliseconds>(end-start).count();
+        double giga = (double)ITERS * WORDS * 64 / 1e9;
+        std::cout << "[BENCH AVX2] Iters " << ITERS << " Time " << ms << "ms Giga-popcnt/s " << giga/(ms/1000.0) << " sum " << sum << std::endl;
+    }
+
+    // Benchmark AVX-512
+    {
+        const int ITERS = 2000000;
+        auto start = std::chrono::high_resolution_clock::now();
+        float sum = 0;
+        for (int it = 0; it < ITERS; ++it) {
+            act[0] ^= it;
+            sum += avx512::compute_satu1_block_avx512(act, w, alpha, WORDS);
+        }
+        auto end = std::chrono::high_resolution_clock::now();
+        double ms = std::chrono::duration_cast<std::chrono::milliseconds>(end-start).count();
+        double giga = (double)ITERS * WORDS * 64 / 1e9;
+        double tps_equiv = ITERS / (ms/1000.0);
+        std::cout << "[BENCH AVX-512] Iters " << ITERS << " Time " << ms << "ms Giga-popcnt/s " << giga/(ms/1000.0) << " TPS equiv " << tps_equiv << " sum " << sum << std::endl;
+    }
+
+    // Memory bandwidth estimation
+    // Per token: 0.70GB read (SatU1 2.66GB*16% + NoeSA 0.97GB*20%)
+    // DDR4 38GB/s -> 54 TPS upper, DDR5 70GB/s -> 100 TPS upper
+    std::cout << "[BANDWIDTH] Estimated per token read 0.70GB" << std::endl;
+    std::cout << "[BANDWIDTH] Ryzen5 DDR4 38GB/s -> 54 TPS upper bound" << std::endl;
+    std::cout << "[BANDWIDTH] Xeon DDR5 70GB/s -> 100 TPS upper bound" << std::endl;
+    std::cout << "[TARGET] Ryzen5 28-42 / 60-85 TPS, AVX-512 65-90 / 120-165 TPS" << std::endl;
+
+    return 0;
+}