CompoundingKernel Explorer placeholder

README.md

@@ -1,10 +1,27 @@
 ---
 title: CompoundingKernel Explorer
-emoji: 📈
+emoji: 🧩
 colorFrom: indigo
-colorTo: red
+colorTo: green
 sdk: static
+app_file: index.html
 pinned: false
+license: cc-by-nc-4.0
+short_description: SOL-ExecBench problems. Kernels are on the way.
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# CompoundingKernel Explorer
+
+CompoundingKernel is a self-improving system that writes and tunes GPU kernels for NVIDIA B200.
+This Space lists the 235 [SOL-ExecBench](https://research.nvidia.com/benchmarks/sol-execbench/leaderboard/collection/1/B200)
+problems it works on, grouped by leaderboard (L1, L2, Quant, FlashInfer) and by operation type.
+
+**Paper, code and kernels are on the way.** No kernels are published here yet.
+
+Problem names come from the public benchmark. The operation groups are our own labels.
+SOL-ExecBench and its leaderboards are by NVIDIA.
+
+## Files
+
+- `index.html`: the whole page (no external requests; the data is embedded).
+- `data.json`: the same problem list as plain JSON.

data.json

@@ -0,0 +1,2354 @@
+{
+"problems": [
+{
+"board": "L1",
+"id": "001_attention_softmax_dropout_value_matmul_backward",
+"name": "Attention softmax dropout value matmul backward",
+"group": "Backward pass",
+"op": "attention softmax + dropout + PV backward (GQA 80/8/128)",
+"direction": "backward",
+"model": "ByteDance/Seed-Seed-OSS-36B-Instruct",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "002_vae_conv3x3_groupnorm_silu_residual_fused",
+"name": "VAE conv3x3 GroupNorm SiLU residual fused",
+"group": "Image and video",
+"op": "VAE conv3x3 + GroupNorm + SiLU residual block",
+"direction": "forward",
+"model": "Efficient-Large-Model/Sana-Sprint-1.6B-1024px",
+"workloads": 20
+},
+{
+"board": "L1",
+"id": "003_lm_head_projection_with_logit_slicing",
+"name": "LM head projection with logit slicing",
+"group": "Projections and GEMM",
+"op": "LM head GEMM on the last N positions (logit slicing)",
+"direction": "forward",
+"model": "LGAI-EXAONE/EXAONE-4.0-1.2B",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "004_attention_output_projection_with_reshape_backward",
+"name": "Attention output projection with reshape backward",
+"group": "Backward pass",
+"op": "attention o_proj + reshape backward",
+"direction": "backward",
+"model": "LiquidAI/LFM2-1.2B",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "005_conv_gated_projection_with_causal_conv",
+"name": "Conv gated projection with causal conv",
+"group": "State space and conv",
+"op": "LFM2 short-conv block: in-proj, gate, causal depthwise conv1d, out-proj",
+"direction": "forward",
+"model": "LiquidAI/LFM2-1.2B",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "006_hyena_depthwise_conv1d_split_gate",
+"name": "Hyena depthwise Conv1D split gate",
+"group": "State space and conv",
+"op": "Hyena depthwise conv1d + split + gate",
+"direction": "forward",
+"model": "LongSafari/hyenadna-small-32k-seqlen-hf",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "007_hyena_fft_size_padding_rfft",
+"name": "Hyena FFT size padding RFFT",
+"group": "State space and conv",
+"op": "Hyena FFT padding + rfft",
+"direction": "forward",
+"model": "LongSafari/hyenadna-small-32k-seqlen-hf",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "008_expert_output_weighted_index_add_accumulation",
+"name": "Expert output weighted index add accumulation",
+"group": "MLP and MoE",
+"op": "MoE combine: weighted index_add of expert outputs",
+"direction": "forward",
+"model": "MiniMaxAI/MiniMax-M2",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "009_expert_token_scatter_with_weighted_forward_backward",
+"name": "Expert token scatter with weighted forward backward",
+"group": "Backward pass",
+"op": "MoE token scatter, weighted, forward + backward",
+"direction": "backward",
+"model": "MiniMaxAI/MiniMax-M2",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "010_attention_value_projection_with_transpose",
+"name": "Attention value projection with transpose",
+"group": "Projections and GEMM",
+"op": "V projection (5120 -> 1024) + reshape / transpose",
+"direction": "forward",
+"model": "NousResearch/DeepHermes-3-Mistral-24B-Preview",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "011_rotary_position_embedding",
+"name": "Rotary position embedding",
+"group": "Attention",
+"op": "RoPE cos/sin from positions with llama3 scaling",
+"direction": "forward",
+"model": "NousResearch/Hermes-4-70B",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "012_fused_cos_sin_embedding_generation",
+"name": "Fused cos sin embedding generation",
+"group": "Attention",
+"op": "RoPE cos/sin from frequencies with attention scaling",
+"direction": "forward",
+"model": "NousResearch/Hermes-4-405B",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "013_fused_residual_rms_norm_backward",
+"name": "Fused residual RMS norm backward",
+"group": "Backward pass",
+"op": "fused residual + RMSNorm backward",
+"direction": "backward",
+"model": "Qwen/Qwen3-4B-Instruct-2507",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "014_rotary_embedding_with_attention_scaling_backward",
+"name": "Rotary embedding with attention scaling backward",
+"group": "Backward pass",
+"op": "RoPE with attention scaling backward",
+"direction": "backward",
+"model": "Qwen/Qwen3-4B-Instruct-2507",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "015_grouped_query_attention_with_rope_and_qk_norm",
+"name": "Grouped query attention with RoPE and QK norm",
+"group": "Attention",
+"op": "GQA attention block: QKV proj, QK-RMSNorm, RoPE, causal SDPA, o_proj",
+"direction": "forward",
+"model": "Qwen/Qwen3-8B",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "016_rope_inverse_frequency_computation",
+"name": "RoPE inverse frequency computation",
+"group": "Attention",
+"op": "RoPE inverse-frequency table",
+"direction": "forward",
+"model": "Qwen/Qwen3-8B",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "017_moe_expert_swiglu_with_down_projection_backward",
+"name": "MoE expert SwiGLU with down projection backward",
+"group": "Backward pass",
+"op": "MoE expert SwiGLU + down projection backward",
+"direction": "backward",
+"model": "Qwen/Qwen3-30B-A3B-Instruct-2507",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "018_fused_rope_with_qk_norm_and_kv_cache_update",
+"name": "Fused RoPE with QK norm and KV cache update",
+"group": "Attention",
+"op": "fused QK-RMSNorm + RoPE + KV-cache write",
+"direction": "forward",
+"model": "Qwen/Qwen3-Coder-480B-A35B-Instruct",
+"workloads": 13
+},
+{
+"board": "L1",
+"id": "019_vision_3d_rotary_embedding_with_spatial_merge_indexing_backward",
+"name": "Vision 3D rotary embedding with spatial merge indexing backward",
+"group": "Backward pass",
+"op": "vision 3D RoPE with spatial-merge indexing backward",
+"direction": "backward",
+"model": "Qwen/Qwen3-Omni-30B-A3B-Instruct",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "020_vision_patch_merger_spatial_shuffle_mlp",
+"name": "Vision patch merger spatial shuffle MLP",
+"group": "Image and video",
+"op": "vision patch merger: 2x2 shuffle + LayerNorm + GELU MLP",
+"direction": "forward",
+"model": "Qwen/Qwen3-Omni-30B-A3B-Instruct",
+"workloads": 15
+},
+{
+"board": "L1",
+"id": "021_vision_cu_seqlens_variable_length_attention",
+"name": "Vision cu seqlens variable length attention",
+"group": "Image and video",
+"op": "vision varlen attention (cu_seqlens) with QKV proj + RoPE",
+"direction": "forward",
+"model": "Qwen/Qwen3-VL-8B-Instruct",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "022_vision_mlp_gelu_backward",
+"name": "Vision MLP GELU backward",
+"group": "Backward pass",
+"op": "vision MLP GELU backward",
+"direction": "backward",
+"model": "Qwen/Qwen3-VL-8B-Instruct",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "023_multimodal_rope_position_computation_with_grid_based_indexing",
+"name": "Multimodal RoPE position computation with grid based indexing",
+"group": "Image and video",
+"op": "multimodal RoPE positions + learned position-embedding interpolation",
+"direction": "forward",
+"model": "Qwen/Qwen3-VL-235B-A22B-Instruct",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "024_vision_rotary_position_embedding_generation_backward",
+"name": "Vision rotary position embedding generation backward",
+"group": "Backward pass",
+"op": "vision RoPE generation backward",
+"direction": "backward",
+"model": "Qwen/Qwen-Image-Edit-2509",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "025_video_latent_gelu_activation",
+"name": "Video latent GELU activation",
+"group": "Image and video",
+"op": "GELU-tanh elementwise (video latents)",
+"direction": "forward",
+"model": "Wan-AI/Wan2.2-T2V-A14B",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "026_video_patch_embedding_projection",
+"name": "Video patch embedding projection",
+"group": "Image and video",
+"op": "Conv3D video patch embed + position embed + LayerNorm",
+"direction": "forward",
+"model": "Wan-AI/Wan2.2-T2V-A14B",
+"workloads": 4
+},
+{
+"board": "L1",
+"id": "027_video_spatial_attention_with_rope_3d",
+"name": "Video spatial attention with RoPE 3D",
+"group": "Image and video",
+"op": "video spatial attention with 3D RoPE",
+"direction": "forward",
+"model": "Wan-AI/Wan2.2-T2V-A14B",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "028_hybrid_attention_mask_preparation",
+"name": "Hybrid attention mask preparation",
+"group": "Attention",
+"op": "causal + sliding-window mask construction",
+"direction": "forward",
+"model": "XiaomiMiMo/MiMo-V2-Flash",
+"workloads": 12
+},
+{
+"board": "L1",
+"id": "029_mamba_conv1d_with_gating",
+"name": "Mamba Conv1D with gating",
+"group": "State space and conv",
+"op": "Mamba in-proj + causal depthwise conv1d + SiLU gate",
+"direction": "forward",
+"model": "ai21labs/AI21-Jamba-Large-1.7",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "030_attention_output_projection_with_residual",
+"name": "Attention output projection with residual",
+"group": "Projections and GEMM",
+"op": "o_proj GEMM (2560x2560) + residual add",
+"direction": "forward",
+"model": "ai21labs/AI21-Jamba-Reasoning-3B",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "031_repeat_kv_attention_matmul",
+"name": "Repeat KV attention matmul",
+"group": "Attention",
+"op": "GQA repeat_kv + Q@K^T scores",
+"direction": "forward",
+"model": "allenai/Olmo-3-7B-Think",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "032_attention_weights_matmul_with_value_projection",
+"name": "Attention weights matmul with value projection",
+"group": "Attention",
+"op": "attention weights @ V + transpose / reshape",
+"direction": "forward",
+"model": "allenai/Olmo-3-32B-Think",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "033_post_norm_residual",
+"name": "Post norm residual",
+"group": "Normalization",
+"op": "post-norm residual: residual + RMSNorm(x)",
+"direction": "forward",
+"model": "allenai/Olmo-3-1025-7B",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "034_flux_multi_axis_rope_frequency_computation",
+"name": "Flux multi axis RoPE frequency computation",
+"group": "Image and video",
+"op": "FLUX multi-axis RoPE frequencies",
+"direction": "forward",
+"model": "black-forest-labs/FLUX.1-Fill-dev",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "035_flux_ada_layer_norm_zero_modulation_extraction",
+"name": "Flux ada layer norm zero modulation extraction",
+"group": "Image and video",
+"op": "FLUX AdaLN-Zero modulation linear + chunk",
+"direction": "forward",
+"model": "black-forest-labs/FLUX.1-Kontext-dev",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "036_flux_output_norm_projection_chain",
+"name": "Flux output norm projection chain",
+"group": "Image and video",
+"op": "FLUX final AdaLN + output projection",
+"direction": "forward",
+"model": "black-forest-labs/FLUX.1-Kontext-dev",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "037_flux_feedforward_gelu_approximate",
+"name": "Flux feedforward GELU approximate",
+"group": "Image and video",
+"op": "FLUX feed-forward with GELU-tanh",
+"direction": "forward",
+"model": "black-forest-labs/FLUX.1-dev",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "038_flux_multi_head_rmsnorm_qk",
+"name": "Flux multi head RMSNorm QK",
+"group": "Image and video",
+"op": "FLUX.2 per-head QK RMSNorm",
+"direction": "forward",
+"model": "black-forest-labs/FLUX.2-dev",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "039_adaptive_instance_normalization_2d_backward",
+"name": "Adaptive instance normalization 2D backward",
+"group": "Backward pass",
+"op": "adaptive instance norm 2D backward",
+"direction": "backward",
+"model": "chuxiaojie/NAFNet",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "040_conv2d_residual_block",
+"name": "Conv2D residual block",
+"group": "Image and video",
+"op": "NAFNet conv residual block",
+"direction": "forward",
+"model": "chuxiaojie/NAFNet",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "041_strided_conv2d_downsampling",
+"name": "Strided Conv2D downsampling",
+"group": "Image and video",
+"op": "NAFNet strided conv2d downsampling",
+"direction": "forward",
+"model": "chuxiaojie/NAFNet",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "042_moe_expert_load_balancing_and_token_capacity_backward",
+"name": "MoE expert load balancing and token capacity backward",
+"group": "Backward pass",
+"op": "MoE load-balancing loss / capacity backward",
+"direction": "backward",
+"model": "deepseek-ai/DeepSeek-R1-0528",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "043_mla_fused_qkv_rope_split",
+"name": "MLA fused QKV RoPE split",
+"group": "Projections and GEMM",
+"op": "MLA latent projections: q_a -> RMSNorm -> q_b, kv_a + split",
+"direction": "forward",
+"model": "deepseek-ai/DeepSeek-V3",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "044_moe_expert_computation",
+"name": "MoE expert computation",
+"group": "MLP and MoE",
+"op": "MoE experts: SwiGLU per expert from given ids / weights",
+"direction": "forward",
+"model": "deepseek-ai/DeepSeek-V3",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "045_fused_linear_gelu_grn_linear",
+"name": "Fused linear GELU GRN linear",
+"group": "Image and video",
+"op": "ConvNeXtV2 MLP + GRN",
+"direction": "forward",
+"model": "facebook/convnextv2-base-22k-224",
+"workloads": 14
+},
+{
+"board": "L1",
+"id": "046_attention_softmax_with_softcapping_and_dropout",
+"name": "Attention softmax with softcapping and dropout",
+"group": "Attention",
+"op": "attention softmax with logit soft-cap + dropout",
+"direction": "forward",
+"model": "google/gemma-3-1b-it",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "047_attention_with_qk_norm_and_rope",
+"name": "Attention with QK norm and RoPE",
+"group": "Attention",
+"op": "Gemma3 attention block: QKV/O proj, QK-RMSNorm, RoPE, soft-cap, causal",
+"direction": "forward",
+"model": "google/gemma-3-12b-it",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "048_fused_gate_up_projection_with_swiglu",
+"name": "Fused gate up projection with SwiGLU",
+"group": "MLP and MoE",
+"op": "fused gate+up GEMM with GELU-tanh gating (no down proj)",
+"direction": "forward",
+"model": "google/gemma-3-12b-it",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "049_attention_qk_matmul_with_gqa_repeat_and_scaling",
+"name": "Attention QK matmul with GQA repeat and scaling",
+"group": "Attention",
+"op": "GQA repeat + Q@K^T + scale",
+"direction": "forward",
+"model": "google/gemma-3-270m",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "050_attention_qkv_projection_with_bias_and_reshape",
+"name": "Attention QKV projection with bias and reshape",
+"group": "Projections and GEMM",
+"op": "QKV projection with bias + reshape",
+"direction": "forward",
+"model": "google/gemma-3-270m",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "051_attention_qkv_with_qk_norm_single_kernel_backward",
+"name": "Attention QKV with QK norm single kernel backward",
+"group": "Backward pass",
+"op": "QKV + QK-norm backward",
+"direction": "backward",
+"model": "google/gemma-3-270m",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "052_altup_hidden_state_collapse_with_magnitude_normalization",
+"name": "AltUp hidden state collapse with magnitude normalization",
+"group": "Other",
+"op": "Gemma3n AltUp stream collapse",
+"direction": "forward",
+"model": "google/gemma-3n-E2B",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "053_gaussian_topk_sparse_activation",
+"name": "Gaussian topk sparse activation",
+"group": "Other",
+"op": "Gemma3n Gaussian top-k sparse activation",
+"direction": "forward",
+"model": "google/gemma-3n-E2B",
+"workloads": 12
+},
+{
+"board": "L1",
+"id": "054_audio_attention_qkv_projection_with_normalization",
+"name": "Audio attention QKV projection with normalization",
+"group": "Audio",
+"op": "audio attention QKV proj + per-head RMSNorm",
+"direction": "forward",
+"model": "google/gemma-3n-E4B",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "055_attention_qkv_projection_with_gqa_reshape",
+"name": "Attention QKV projection with GQA reshape",
+"group": "Projections and GEMM",
+"op": "QKV projection + GQA reshape",
+"direction": "forward",
+"model": "ibm-granite/granite-4.0-1b",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "056_mamba_ssm_dt_projection_softplus_clamp_backward",
+"name": "Mamba SSM dt projection softplus clamp backward",
+"group": "Backward pass",
+"op": "Mamba dt projection + softplus + clamp backward",
+"direction": "backward",
+"model": "ibm-granite/granite-4.0-micro",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "057_mtp_shifted_embedding_with_dual_rms_norm_fusion",
+"name": "MTP shifted embedding with dual RMS norm fusion",
+"group": "Full decoder layers",
+"op": "MTP input: shifted embedding + dual RMSNorm + concat + proj",
+"direction": "forward",
+"model": "inclusionAI/Ling-flash-2.0",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "058_moe_expert_token_radix_sort_with_prefix_sum",
+"name": "MoE expert token radix sort with prefix sum",
+"group": "MLP and MoE",
+"op": "MoE token sort by expert (counting sort + prefix sum)",
+"direction": "forward",
+"model": "inclusionAI/Ling-mini-2.0",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "059_moe_group_score_aggregation_and_masking",
+"name": "MoE group score aggregation and masking",
+"group": "MLP and MoE",
+"op": "MoE group-limited routing step: top-2 group sums, top-4 groups, mask",
+"direction": "forward",
+"model": "inclusionAI/Ling-mini-2.0",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "060_fused_attention_qk_matmul_scale_mask_softmax_backward",
+"name": "Fused attention QK matmul scale mask softmax backward",
+"group": "Backward pass",
+"op": "attention QK scale mask softmax backward",
+"direction": "backward",
+"model": "meta-llama/Llama-3.1-405B",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "061_tanh_gated_residual_add_backward",
+"name": "Tanh gated residual add backward",
+"group": "Backward pass",
+"op": "tanh-gated residual add backward",
+"direction": "backward",
+"model": "meta-llama/Llama-3.2-11B-Vision",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "062_kv_cache_update_with_rope_backward",
+"name": "KV cache update with RoPE backward",
+"group": "Backward pass",
+"op": "KV-cache update with RoPE backward",
+"direction": "backward",
+"model": "mistralai/Devstral-Small-2507",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "063_attention_output_reshape_and_projection",
+"name": "Attention output reshape and projection",
+"group": "Projections and GEMM",
+"op": "attention output reshape + o_proj (heads*v_head_dim -> hidden)",
+"direction": "forward",
+"model": "moonshotai/Kimi-K2-Instruct-0905",
+"workloads": 16
+},
+{
+"board": "L1",
+"id": "064_latent_kv_expansion_with_split",
+"name": "Latent KV expansion with split",
+"group": "Projections and GEMM",
+"op": "MLA latent KV expansion: RMSNorm + kv_b_proj + split K_nope / V",
+"direction": "forward",
+"model": "moonshotai/Kimi-K2-Instruct-0905",
+"workloads": 16
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+"group": "Backward pass",
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+"group": "Audio",
+"op": "encoder final RMSNorm + cross-attention K/V projection",
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+"group": "MLP and MoE",
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+"group": "Audio",
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+"board": "L1",
+"id": "077_whisper_decoder_output_projection",
+"name": "Whisper decoder output projection",
+"group": "Projections and GEMM",
+"op": "Whisper LM head GEMM",
+"direction": "forward",
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+"workloads": 16
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+"id": "078_group_norm_fusion",
+"name": "Group norm fusion",
+"group": "Image and video",
+"op": "GroupNorm (SD UNet)",
+"direction": "forward",
+"model": "runwayml/stable-diffusion-v1-5",
+"workloads": 16
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+"board": "L1",
+"id": "079_ImageNet_83.6_ssm_output_projection_gate_multiply_backward",
+"name": "ImageNet 83.6 SSM output projection gate multiply backward",
+"group": "Backward pass",
+"op": "SSM output projection gate multiply backward",
+"direction": "backward",
+"model": "saurabhati/VMamba",
+"workloads": 16
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+"board": "L1",
+"id": "080_adaptive_layernorm_continuous_with_modulation",
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+"group": "Image and video",
+"op": "SD3.5 AdaLayerNormContinuous",
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+"workloads": 16
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+"board": "L1",
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+"op": "SD3.5 joint-attention context projection",
+"direction": "forward",
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+"board": "L1",
+"id": "082_qk_norm_scaled_dot_product_attention",
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+"op": "SD3.5 QK-LayerNorm SDPA",
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+"board": "L1",
+"id": "083_attention_score_value_matmul",
+"name": "Attention score value matmul",
+"group": "Image and video",
+"op": "SDXL attention weights @ V",
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+"model": "stabilityai/stable-diffusion-xl-base-1.0",
+"workloads": 16
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+"board": "L1",
+"id": "084_silu_activation_backward",
+"name": "SiLU activation backward",
+"group": "Backward pass",
+"op": "SiLU backward",
+"direction": "backward",
+"model": "stabilityai/stable-diffusion-xl-base-1.0",
+"workloads": 16
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+"board": "L1",
+"id": "085_geglu_activation",
+"name": "GEGLU activation",
+"group": "Image and video",
+"op": "GEGLU activation",
+"direction": "forward",
+"model": "stabilityai/stable-diffusion-xl-refiner-1.0",
+"workloads": 16
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+"board": "L1",
+"id": "086_sam_hq_mask_decoder_iou_hypernetwork_fusion",
+"name": "SAM hq mask decoder IoU hypernetwork fusion",
+"group": "Image and video",
+"op": "SAM-HQ IoU head + hypernetwork MLPs",
+"direction": "forward",
+"model": "syscv-community/sam-hq-vit-base",
+"workloads": 16
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+{
+"board": "L1",
+"id": "087_embedding_with_initial_layernorm_backward",
+"name": "Embedding with initial LayerNorm backward",
+"group": "Backward pass",
+"op": "embedding + LayerNorm backward",
+"direction": "backward",
+"model": "taozi555/jamba-mini-1.7-rp",
+"workloads": 16
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+"board": "L1",
+"id": "088_rotary_position_embedding_application",
+"name": "Rotary position embedding application",
+"group": "Image and video",
+"op": "RoPE application to Q/K (HunyuanImage)",
+"direction": "forward",
+"model": "tencent/HunyuanImage-2.1",
+"workloads": 16
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+"board": "L1",
+"id": "089_vae_attention_block_with_groupnorm",
+"name": "VAE attention block with GroupNorm",
+"group": "Image and video",
+"op": "VAE attention block with GroupNorm",
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+"workloads": 16
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+"board": "L1",
+"id": "090_batched_2d_rope_position_encoding_backward",
+"name": "Batched 2D RoPE position encoding backward",
+"group": "Backward pass",
+"op": "batched 2D RoPE backward",
+"direction": "backward",
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+"workloads": 16
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+"board": "L1",
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+"name": "Fused GQA QKV RoPE QK norm backward",
+"group": "Backward pass",
+"op": "fused GQA QKV + RoPE + QK-norm backward",
+"direction": "backward",
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+"workloads": 16
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+"board": "L1",
+"id": "092_gqa_attention_with_qk_norm",
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+"group": "Attention",
+"op": "GQA attention block: QKV proj (bias), QK-RMSNorm, RoPE, causal, o_proj",
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+"model": "zai-org/GLM-4.5-Air",
+"workloads": 16
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+"board": "L1",
+"id": "093_grouped_topk_moe_routing_backward",
+"name": "Grouped topk MoE routing backward",
+"group": "Backward pass",
+"op": "grouped top-k MoE routing backward",
+"direction": "backward",
+"model": "zai-org/GLM-4.7",
+"workloads": 16
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+"board": "L1",
+"id": "094_time_decay_exponential_stabilization",
+"name": "Time decay exponential stabilization",
+"group": "Other",
+"op": "RWKV time-decay exponent with stabilisation",
+"direction": "forward",
+"model": "zhiyuan8/RWKV-v7-1.5B-G1-GGUF",
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+"board": "L2",
+"id": "001_fused_vision_multihead_attention_with_norms_backward",
+"name": "Fused vision multihead attention with norms backward",
+"group": "Backward pass",
+"op": "vision MHA with norms backward",
+"direction": "backward",
+"model": "1024m/Molmo-7B-D-Base",
+"workloads": 16
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+"board": "L2",
+"id": "002_decoder_layer_full_block",
+"name": "Decoder layer full block",
+"group": "Full decoder layers",
+"op": "Llama decoder layer: RMSNorm, GQA + RoPE, residual, RMSNorm, SwiGLU MLP, residual",
+"direction": "forward",
+"model": "NousResearch/DeepHermes-3-Llama-3-8B-Preview",
+"workloads": 18
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+"id": "003_grouped_query_attention_with_rope_backward",
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+"group": "Backward pass",
+"op": "GQA attention with RoPE backward",
+"direction": "backward",
+"model": "NousResearch/DeepHermes-3-Mistral-24B-Preview",
+"workloads": 16
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+"id": "004_fused_residual_rms_mlp",
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+"workloads": 16
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+"board": "L2",
+"id": "005_swiglu_mlp_backward",
+"name": "SwiGLU MLP backward",
+"group": "Backward pass",
+"op": "SwiGLU MLP backward",
+"direction": "backward",
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+"workloads": 18
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+{
+"board": "L2",
+"id": "006_multimodal_rope_position_calculation",
+"name": "Multimodal RoPE position calculation",
+"group": "Attention",
+"op": "multimodal 3D RoPE position-id calculation",
+"direction": "forward",
+"model": "Qwen/Qwen2.5-VL-7B-Instruct",
+"workloads": 16
+},
+{
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+"id": "007_multimodal_rotary_embedding_attention",
+"name": "Multimodal rotary embedding attention",
+"group": "Attention",
+"op": "GQA attention with multimodal 3D RoPE",
+"direction": "forward",
+"model": "Qwen/Qwen2.5-VL-7B-Instruct",
+"workloads": 16
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+"id": "008_moe_sparse_routing_and_dispatch",
+"name": "MoE sparse routing and dispatch",
+"group": "MLP and MoE",
+"op": "MoE block: softmax router + top-8 + SwiGLU experts + weighted sum",
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+"model": "Qwen/Qwen3-30B-A3B-Instruct-2507",
+"workloads": 16
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+"board": "L2",
+"id": "009_decoder_layer_with_residual_connections",
+"name": "Decoder layer with residual connections",
+"group": "Full decoder layers",
+"op": "MoE decoder layer: RMSNorm, GQA + QK-norm, MoE, residuals",
+"direction": "forward",
+"model": "Qwen/Qwen3-Coder-30B-A3B-Instruct",
+"workloads": 16
+},
+{
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+"id": "010_moe_expert_computation_with_weighted_accumulation",
+"name": "MoE expert computation with weighted accumulation",
+"group": "MLP and MoE",
+"op": "MoE experts SwiGLU + weighted accumulation (given top-k)",
+"direction": "forward",
+"model": "Qwen/Qwen3-Coder-30B-A3B-Instruct",
+"workloads": 16
+},
+{
+"board": "L2",
+"id": "011_moe_sparse_routing_and_dispatch_backward",
+"name": "MoE sparse routing and dispatch backward",
+"group": "Backward pass",
+"op": "MoE routing + dispatch backward",
+"direction": "backward",
+"model": "Qwen/Qwen3-Coder-30B-A3B-Instruct",
+"workloads": 16
+},
+{
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+"id": "012_moe_expert_batched_execution_with_capacity_factor",
+"name": "MoE expert batched execution with capacity factor",
+"group": "MLP and MoE",
+"op": "MoE experts with capacity-factor batching",
+"direction": "forward",
+"model": "Qwen/Qwen3-Coder-480B-A35B-Instruct",
+"workloads": 16
+},
+{
+"board": "L2",
+"id": "013_expert_weighted_aggregation_with_shared_expert",
+"name": "Expert weighted aggregation with shared expert",
+"group": "MLP and MoE",
+"op": "MoE experts + weighted sum + sigmoid-gated shared expert",
+"direction": "forward",
+"model": "Qwen/Qwen3-Next-80B-A3B-Instruct",
+"workloads": 16
+},
+{
+"board": "L2",
+"id": "014_audio_encoder_varlen_attention_with_chunking_backward",
+"name": "Audio encoder varlen attention with chunking backward",
+"group": "Backward pass",
+"op": "audio varlen attention with chunking backward",
+"direction": "backward",
+"model": "Qwen/Qwen3-Omni-30B-A3B-Instruct",
+"workloads": 16
+},
+{
+"board": "L2",
+"id": "015_audio_sinusoidal_position_embedding_with_conv_projection",
+"name": "Audio sinusoidal position embedding with conv projection",
+"group": "Audio",
+"op": "audio sinusoidal position embed + Conv2d downsampling + proj",
+"direction": "forward",
+"model": "Qwen/Qwen3-Omni-30B-A3B-Instruct",
+"workloads": 16
+},
+{
+"board": "L2",
+"id": "016_moe_expert_mlp_with_load_balancing",
+"name": "MoE expert MLP with load balancing",
+"group": "MLP and MoE",
+"op": "MoE block: softmax router + top-8 + SwiGLU experts (returns router logits)",
+"direction": "forward",
+"model": "Qwen/Qwen3-Omni-30B-A3B-Instruct",
+"workloads": 16
+},
+{
+"board": "L2",
+"id": "017_fused_vision_cu_seqlens_attention_with_2d_rope_backward",
+"name": "Fused vision cu seqlens attention with 2D RoPE backward",
+"group": "Backward pass",
+"op": "vision varlen attention with 2D RoPE backward",
+"direction": "backward",
+"model": "Qwen/Qwen3-VL-4B-Instruct",
+"workloads": 16
+},
+{
+"board": "L2",
+"id": "018_cu_seqlens_variable_length_vision_attention",
+"name": "Cu seqlens variable length vision attention",
+"group": "Image and video",
+"op": "vision varlen attention (cu_seqlens) with RoPE",
+"direction": "forward",
+"model": "Qwen/Qwen-Image-Edit-2509",
+"workloads": 16
+},
+{
+"board": "L2",
+"id": "019_decoder_layer_fused_attention_mlp",
+"name": "Decoder layer fused attention MLP",
+"group": "Full decoder layers",
+"op": "Qwen2-VL decoder layer (M-RoPE GQA + SwiGLU)",
+"direction": "forward",
+"model": "Qwen/Qwen-Image-Edit-2509",
+"workloads": 16
+},
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+"id": "020_decoder_layer_pre_post_norm_residual",
+"name": "Decoder layer pre post norm residual",
+"group": "Full decoder layers",
+"op": "decoder layer with pre / post norm residuals",
+"direction": "forward",
+"model": "RekaAI/reka-flash-3",
+"workloads": 16
+},
+{
+"board": "L2",
+"id": "021_cross_attention_text_video_conditioning_backward",
+"name": "Cross attention text video conditioning backward",
+"group": "Backward pass",
+"op": "text-video cross-attention backward",
+"direction": "backward",
+"model": "Wan-AI/Wan2.2-T2V-A14B",
+"workloads": 16
+},
+{
+"board": "L2",
+"id": "022_video_latent_denoising_unet_block",
+"name": "Video latent denoising UNet block",
+"group": "Image and video",
+"op": "video latent denoising block",
+"direction": "forward",
+"model": "Wan-AI/Wan2.2-T2V-A14B",
+"workloads": 16
+},
+{
+"board": "L2",
+"id": "023_video_latent_vae_encoder_downsampling",
+"name": "Video latent VAE encoder downsampling",
+"group": "Image and video",
+"op": "video VAE encoder downsampling",
+"direction": "forward",
+"model": "Wan-AI/Wan2.2-T2V-A14B",
+"workloads": 16
+},
+{
+"board": "L2",
+"id": "024_moe_expert_parallel_execution",
+"name": "MoE expert parallel execution",
+"group": "MLP and MoE",
+"op": "MoE experts (256, given top-k) SwiGLU + scatter-add",
+"direction": "forward",
+"model": "XiaomiMiMo/MiMo-V2-Flash",
+"workloads": 16
+},
+{
+"board": "L2",
+"id": "025_moe_expert_parallel_execution_backward",
+"name": "MoE expert parallel execution backward",
+"group": "Backward pass",
+"op": "MoE experts backward",
+"direction": "backward",
+"model": "XiaomiMiMo/MiMo-V2-Flash",
+"workloads": 16
+},
+{
+"board": "L2",
+"id": "026_moe_expert_parallel_execution_with_weighted_aggregation",
+"name": "MoE expert parallel execution with weighted aggregation",
+"group": "MLP and MoE",
+"op": "MoE experts (16, top-2) with weighted aggregation",
+"direction": "forward",
+"model": "ai21labs/AI21-Jamba-Large-1.7",
+"workloads": 16
+},
+{
+"board": "L2",
+"id": "027_grouped_query_attention_with_yarn_rope_and_qk_norm",
+"name": "Grouped query attention with YaRN RoPE and QK norm",
+"group": "Attention",
+"op": "GQA attention with YaRN RoPE, QK-norm, sliding window",
+"direction": "forward",
+"model": "allenai/Olmo-3-32B-Think",
+"workloads": 16
+},
+{
+"board": "L2",
+"id": "028_gqa_rotary_attention_core_backward",
+"name": "GQA rotary attention core backward",
+"group": "Backward pass",
+"op": "GQA rotary attention core backward",
+"direction": "backward",
+"model": "baidu/ERNIE-4.5-300B-A47B-PT",
+"workloads": 16
+},
+{
+"board": "L2",
+"id": "029_moe_sparse_routing_and_dispatch",
+"name": "MoE sparse routing and dispatch",
+"group": "MLP and MoE",
+"op": "MoE block: biased router, top-k, experts + shared expert",
+"direction": "forward",
+"model": "baidu/ERNIE-4.5-300B-A47B-PT",
+"workloads": 9
+},
+{
+"board": "L2",
+"id": "030_flux_concatenated_sequence_processing_with_split",
+"name": "Flux concatenated sequence processing with split",
+"group": "Image and video",
+"op": "FLUX concat text+image, linear, split",
+"direction": "forward",
+"model": "black-forest-labs/FLUX.1-Kontext-dev",
+"workloads": 16
+},
+{
+"board": "L2",
+"id": "031_flux_timestep_guidance_projection_embedding",
+"name": "Flux timestep guidance projection embedding",
+"group": "Image and video",
+"op": "FLUX timestep + guidance embedding",
+"direction": "forward",
+"model": "black-forest-labs/FLUX.1-dev",
+"workloads": 16
+},
+{
+"board": "L2",
+"id": "032_dual_stream_attention_with_conditional_cross_attention",
+"name": "Dual stream attention with conditional cross attention",
+"group": "Image and video",
+"op": "FIBO dual-stream attention block",
+"direction": "forward",
+"model": "briaai/FIBO",
+"workloads": 16
+},
+{
+"board": "L2",
+"id": "033_multi_scale_feature_pyramid",
+"name": "Multi scale feature pyramid",
+"group": "Image and video",
+"op": "multi-scale feature pyramid (U-Net)",
+"direction": "forward",
+"model": "chuxiaojie/NAFNet",
+"workloads": 16
+},
+{
+"board": "L2",
+"id": "034_vision_language_cross_attention_fusion",
+"name": "Vision language cross attention fusion",
+"group": "Attention",
+"op": "language-to-vision cross-attention with 1D / 3D RoPE",
+"direction": "forward",
+"model": "cybermotaz/Qwen3-VL-32B-Instruct-NVFP4",
+"workloads": 16
+},
+{
+"board": "L2",
+"id": "035_convnextv2_block_with_grn",
+"name": "ConvNeXtV2 block with GRN",
+"group": "Image and video",
+"op": "ConvNeXtV2 block with GRN",
+"direction": "forward",
+"model": "facebook/convnextv2-base-22k-224",
+"workloads": 16
+},
+{
+"board": "L2",
+"id": "036_convnextv2_layer_with_nhwc_persistence_backward",
+"name": "ConvNeXtV2 layer with NHWC persistence backward",
+"group": "Backward pass",
+"op": "ConvNeXtV2 layer backward",
+"direction": "backward",
+"model": "facebook/convnextv2-base-22k-224",
+"workloads": 14
+},
+{
+"board": "L2",
+"id": "037_qk_norm_rope_attention_core_backward",
+"name": "QK norm RoPE attention core backward",
+"group": "Backward pass",
+"op": "QK-norm RoPE attention core backward",
+"direction": "backward",
+"model": "google/gemma-3-4b-it",
+"workloads": 16
+},
+{
+"board": "L2",
+"id": "038_audio_relative_position_attention",
+"name": "Audio relative position attention",
+"group": "Audio",
+"op": "Gemma3n audio relative-position attention",
+"direction": "forward",
+"model": "google/gemma-3n-E2B",
+"workloads": 14
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+"board": "L2",
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+"model": "myshell-ai/OpenVoice",
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+"workloads": 16
+},
+{
+"board": "Quant",
+"id": "025_nvfp4_attention_qkv_projection",
+"name": "NVFP4 attention QKV projection",
+"group": "Projections and GEMM",
+"op": "NVFP4 Q, K, V projections (FP32 input)",
+"direction": "forward",
+"model": "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4",
+"workloads": 16
+},
+{
+"board": "Quant",
+"id": "026_nvfp4_mamba2_out_projection",
+"name": "NVFP4 Mamba2 out projection",
+"group": "State space and conv",
+"op": "NVFP4 Mamba2 out-projection (FP4 act + weight)",
+"direction": "forward",
+"model": "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4",
+"workloads": 16
+},
+{
+"board": "Quant",
+"id": "027_nvfp4_moe_router_projection",
+"name": "NVFP4 MoE router projection",
+"group": "MLP and MoE",
+"op": "NVFP4 MoE router GEMM + sigmoid + bias + top-6",
+"direction": "forward",
+"model": "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4",
+"workloads": 16
+},
+{
+"board": "Quant",
+"id": "028_nvfp4_attention_qkv_projection_with_rope",
+"name": "NVFP4 attention QKV projection with RoPE",
+"group": "Projections and GEMM",
+"op": "NVFP4 QKV projection + QK-RMSNorm + RoPE",
+"direction": "forward",
+"model": "nvidia/Qwen3-8B-NVFP4",
+"workloads": 16
+},
+{
+"board": "Quant",
+"id": "029_nvfp4_fused_mlp_silu_gating",
+"name": "NVFP4 fused MLP SiLU gating",
+"group": "MLP and MoE",
+"op": "NVFP4 SwiGLU MLP (gate, up, down)",
+"direction": "forward",
+"model": "nvidia/Qwen3-8B-NVFP4",
+"workloads": 16
+},
+{
+"board": "Quant",
+"id": "030_nvfp4_fused_decoder_layer_pre_norm_attention",
+"name": "NVFP4 fused decoder layer pre norm attention",
+"group": "Attention",
+"op": "NVFP4 decoder pre-norm + full attention block",
+"direction": "forward",
+"model": "nvidia/Qwen3-14B-NVFP4",
+"workloads": 16
+},
+{
+"board": "Quant",
+"id": "031_nvfp4_grouped_query_attention_with_kv_repeat",
+"name": "NVFP4 grouped query attention with KV repeat",
+"group": "Attention",
+"op": "NVFP4-quantized attention core (FP4 QK^T and PV)",
+"direction": "forward",
+"model": "nvidia/Qwen3-14B-NVFP4",
+"workloads": 16
+},
+{
+"board": "Quant",
+"id": "032_nvfp4_moe_expert_linear_with_gating",
+"name": "NVFP4 MoE expert linear with gating",
+"group": "MLP and MoE",
+"op": "NVFP4 SwiGLU MLP (one expert-sized MLP, 2048 -> 5120)",
+"direction": "forward",
+"model": "nvidia/Qwen3-Next-80B-A3B-Thinking-NVFP4",
+"workloads": 16
+},
+{
+"board": "Quant",
+"id": "033_nvfp4_moe_routing_with_topk_selection",
+"name": "NVFP4 MoE routing with topk selection",
+"group": "MLP and MoE",
+"op": "NVFP4 router GEMM + softmax + top-10",
+"direction": "forward",
+"model": "nvidia/Qwen3-Next-80B-A3B-Thinking-NVFP4",
+"workloads": 16
+},
+{
+"board": "FlashInfer",
+"id": "001_fused_add_rmsnorm_h2048",
+"name": "Fused add RMSNorm h2048",
+"group": "Normalization",
+"op": "fused add + RMSNorm",
+"direction": "forward",
+"model": "flashinfer-trace tag: qwen3-30b-a3b",
+"workloads": 7
+},
+{
+"board": "FlashInfer",
+"id": "002_fused_add_rmsnorm_h4096",
+"name": "Fused add RMSNorm h4096",
+"group": "Normalization",
+"op": "fused add + RMSNorm",
+"direction": "forward",
+"model": "flashinfer-trace tag: llama-3.1-8b",
+"workloads": 14
+},
+{
+"board": "FlashInfer",
+"id": "003_fused_add_rmsnorm_h7168",
+"name": "Fused add RMSNorm h7168",
+"group": "Normalization",
+"op": "fused add + RMSNorm",
+"direction": "forward",
+"model": "flashinfer-trace tag: deepseek-v3, deepseek-r1",
+"workloads": 8
+},
+{
+"board": "FlashInfer",
+"id": "004_gemm_n128_k2048",
+"name": "GEMM n128 k2048",
+"group": "Projections and GEMM",
+"op": "dense GEMM C = A @ B^T (fp16)",
+"direction": "forward",
+"model": "flashinfer-trace tag: qwen3-30b-a3b (Qwen 3 30B A3B moe.gate)",
+"workloads": 25
+},
+{
+"board": "FlashInfer",
+"id": "005_gemm_n256_k7168",
+"name": "GEMM n256 k7168",
+"group": "Projections and GEMM",
+"op": "dense GEMM C = A @ B^T (fp16)",
+"direction": "forward",
+"model": "flashinfer-trace tag: deepseek-v3, deepseek-r1 (DeepSeek-V3 moe.gate)",
+"workloads": 17
+},
+{
+"board": "FlashInfer",
+"id": "006_gemm_n2048_k4096",
+"name": "GEMM n2048 k4096",
+"group": "Projections and GEMM",
+"op": "dense GEMM C = A @ B^T (fp16)",
+"direction": "forward",
+"model": "flashinfer-trace tag: qwen3-30b-a3b (Qwen 3 30B A3B attn.o_proj)",
+"workloads": 29
+},
+{
+"board": "FlashInfer",
+"id": "007_gemm_n4096_k4096",
+"name": "GEMM n4096 k4096",
+"group": "Projections and GEMM",
+"op": "dense GEMM C = A @ B^T (fp16)",
+"direction": "forward",
+"model": "flashinfer-trace tag: llama-3.1-8b (Llama 3.1 8B attn.o_proj)",
+"workloads": 43
+},
+{
+"board": "FlashInfer",
+"id": "008_gemm_n4096_k14336",
+"name": "GEMM n4096 k14336",
+"group": "Projections and GEMM",
+"op": "dense GEMM C = A @ B^T (fp16)",
+"direction": "forward",
+"model": "flashinfer-trace tag: llama-3.1-8b (Llama 3.1 8B mlp.down_proj)",
+"workloads": 43
+},
+{
+"board": "FlashInfer",
+"id": "009_gemm_n5120_k2048",
+"name": "GEMM n5120 k2048",
+"group": "Projections and GEMM",
+"op": "dense GEMM C = A @ B^T (fp16)",
+"direction": "forward",
+"model": "flashinfer-trace tag: qwen3-30b-a3b (Qwen 3 30B A3B attn.qkv_proj)",
+"workloads": 25
+},
+{
+"board": "FlashInfer",
+"id": "010_gemm_n6144_k4096",
+"name": "GEMM n6144 k4096",
+"group": "Projections and GEMM",
+"op": "dense GEMM C = A @ B^T (fp16)",
+"direction": "forward",
+"model": "flashinfer-trace tag: llama-3.1-8b (Llama 3.1 8B attn.qkv_proj)",
+"workloads": 43
+},
+{
+"board": "FlashInfer",
+"id": "011_gemm_n28672_k4096",
+"name": "GEMM n28672 k4096",
+"group": "Projections and GEMM",
+"op": "dense GEMM C = A @ B^T (fp16)",
+"direction": "forward",
+"model": "flashinfer-trace tag: llama-3.1-8b (Llama 3.1 8B mlp.gate_up_proj)",
+"workloads": 43
+},
+{
+"board": "FlashInfer",
+"id": "012_gqa_paged_decode_h32_kv4_d128_ps1",
+"name": "GQA paged decode h32 kv4 d128 ps1",
+"group": "Attention",
+"op": "GQA paged decode attention (page size 1)",
+"direction": "forward",
+"model": "flashinfer-trace tag: qwen3-30b-a3b (Qwen3-30B-A3B)",
+"workloads": 48
+},
+{
+"board": "FlashInfer",
+"id": "013_gqa_paged_decode_h32_kv8_d128_ps1",
+"name": "GQA paged decode h32 kv8 d128 ps1",
+"group": "Attention",
+"op": "GQA paged decode attention (page size 1)",
+"direction": "forward",
+"model": "flashinfer-trace tag: llama-3.1-8b (Llama-3.1-8B)",
+"workloads": 48
+},
+{
+"board": "FlashInfer",
+"id": "014_gqa_paged_prefill_causal_h32_kv4_d128_ps1",
+"name": "GQA paged prefill causal h32 kv4 d128 ps1",
+"group": "Attention",
+"op": "GQA paged prefill attention, causal (page size 1)",
+"direction": "forward",
+"model": "flashinfer-trace tag: qwen3-30b-a3b (Qwen3-30B-A3B during incremental prefill)",
+"workloads": 30
+},
+{
+"board": "FlashInfer",
+"id": "015_gqa_paged_prefill_causal_h32_kv8_d128_ps1",
+"name": "GQA paged prefill causal h32 kv8 d128 ps1",
+"group": "Attention",
+"op": "GQA paged prefill attention, causal (page size 1)",
+"direction": "forward",
+"model": "flashinfer-trace tag: llama-3.1-8b (Llama-3.1-8B during incremental prefill)",
+"workloads": 38
+},
+{
+"board": "FlashInfer",
+"id": "016_gqa_ragged_prefill_causal_h32_kv4_d128",
+"name": "GQA ragged prefill causal h32 kv4 d128",
+"group": "Attention",
+"op": "GQA ragged prefill attention, causal",
+"direction": "forward",
+"model": "flashinfer-trace tag: qwen3-30b-a3b (Qwen3-30B-A3B during total prefill)",
+"workloads": 15
+},
+{
+"board": "FlashInfer",
+"id": "017_gqa_ragged_prefill_causal_h32_kv8_d128",
+"name": "GQA ragged prefill causal h32 kv8 d128",
+"group": "Attention",
+"op": "GQA ragged prefill attention, causal",
+"direction": "forward",
+"model": "flashinfer-trace tag: llama-3.1-8b (Llama-3.1-8B during total prefill)",
+"workloads": 21
+},
+{
+"board": "FlashInfer",
+"id": "018_mla_paged_decode_h16_ckv512_kpe64_ps1",
+"name": "MLA paged decode h16 ckv512 kpe64 ps1",
+"group": "Attention",
+"op": "MLA paged decode attention (absorbed, ckv 512 + kpe 64, page size 1)",
+"direction": "forward",
+"model": "flashinfer-trace tag: deepseek-v3, deepseek-r1 (DeepSeek-V3 with tensor parallel size 8)",
+"workloads": 47
+},
+{
+"board": "FlashInfer",
+"id": "019_mla_paged_prefill_causal_h16_ckv512_kpe64_ps1",
+"name": "MLA paged prefill causal h16 ckv512 kpe64 ps1",
+"group": "Attention",
+"op": "MLA paged prefill attention, causal (absorbed)",
+"direction": "forward",
+"model": "flashinfer-trace tag: deepseek-v3, deepseek-r1 (DeepSeek-V3 during incremental prefill with tensor parallel size 8)",
+"workloads": 38
+},
+{
+"board": "FlashInfer",
+"id": "020_moe_fp8_block_scale_ds_routing_topk8_ng8_kg4_e32_h7168_i2048",
+"name": "MoE FP8 block scale ds routing topk8 ng8 kg4 e32 h7168 i2048",
+"group": "MLP and MoE",
+"op": "FP8 block-scale MoE: DeepSeek routing + 2 grouped GEMMs (32 local experts)",
+"direction": "forward",
+"model": "flashinfer-trace tag: deepseek-v3, deepseek-r1",
+"workloads": 19
+},
+{
+"board": "FlashInfer",
+"id": "021_rmsnorm_h128",
+"name": "RMSNorm h128",
+"group": "Normalization",
+"op": "RMSNorm",
+"direction": "forward",
+"model": "flashinfer-trace tag: qwen3-30b-a3b (Qwen3-30B-A3B. Epsilon is fixed at 1e-6)",
+"workloads": 14
+},
+{
+"board": "FlashInfer",
+"id": "022_rmsnorm_h512",
+"name": "RMSNorm h512",
+"group": "Normalization",
+"op": "RMSNorm",
+"direction": "forward",
+"model": "flashinfer-trace tag: deepseek-v3, deepseek-r1 (DeepSeek-V3/R1. Epsilon is fixed at 1e-6)",
+"workloads": 8
+},
+{
+"board": "FlashInfer",
+"id": "023_rmsnorm_h1536",
+"name": "RMSNorm h1536",
+"group": "Normalization",
+"op": "RMSNorm",
+"direction": "forward",
+"model": "flashinfer-trace tag: deepseek-v3, deepseek-r1 (DeepSeek-V3/R1. Epsilon is fixed at 1e-6)",
+"workloads": 8
+},
+{
+"board": "FlashInfer",
+"id": "024_rmsnorm_h2048",
+"name": "RMSNorm h2048",
+"group": "Normalization",
+"op": "RMSNorm",
+"direction": "forward",
+"model": "flashinfer-trace tag: qwen3-30b-a3b (Qwen3-30B-A3B. Epsilon is fixed at 1e-6)",
+"workloads": 7
+},
+{
+"board": "FlashInfer",
+"id": "025_rmsnorm_h4096",
+"name": "RMSNorm h4096",
+"group": "Normalization",
+"op": "RMSNorm",
+"direction": "forward",
+"model": "flashinfer-trace tag: llama-3.1-8b (Llama-3.1-8B. Epsilon is fixed at 1e-5)",
+"workloads": 14
+},
+{
+"board": "FlashInfer",
+"id": "026_rmsnorm_h7168",
+"name": "RMSNorm h7168",
+"group": "Normalization",
+"op": "RMSNorm",
+"direction": "forward",
+"model": "flashinfer-trace tag: deepseek-v3, deepseek-r1 (DeepSeek-V3/R1. Epsilon is fixed at 1e-6)",
+"workloads": 8
+}
+]
+}

index.html

@@ -1,19 +1,378 @@
 <!doctype html>
-<html>
-	<head>
-		<meta charset="utf-8" />
-		<meta name="viewport" content="width=device-width" />
-		<title>My static Space</title>
-		<link rel="stylesheet" href="style.css" />
-	</head>
-	<body>
-		<div class="card">
-			<h1>Welcome to your static Space!</h1>
-			<p>You can modify this app directly by editing <i>index.html</i> in the Files and versions tab.</p>
-			<p>
-				Also don't forget to check the
-				<a href="https://huggingface.co/docs/hub/spaces" target="_blank">Spaces documentation</a>.
-			</p>
-		</div>
-	</body>
+<html lang="en">
+<head>
+<meta charset="utf-8">
+<meta name="viewport" content="width=device-width, initial-scale=1">
+<title>CompoundingKernel Explorer: SOL-ExecBench on B200</title>
+<meta name="description" content="The 235 SOL-ExecBench problems CompoundingKernel works on. Paper, code and kernels are on the way.">
+<style>
+/* Paper style ("Editorial ink", tech-report/v6/ck-header.tex + blog/src/template.html tokens).
+   Board hues are the paper's; FlashInfer plum and the dark steps are re-stepped to pass the CVD checks. */
+:root {
+  --bg: #FAF9F5; --panel: #FFFFFF; --soft: #F0EEE6; --tint: #F6F4EE;
+  --ink: #1F1E1D; --ink-2: #3D3D3A; --muted: #6B6A64; --rule: #CFCBC0; --hair: #EBE8E0;
+  --clay: #C96442; --clay-ink: #A9502F; --ours: #F8ECE6;
+  --l1: #3466A8; --l2: #B8862A; --quant: #13907F; --fi: #A777ED;
+  --focus: #A9502F;
+  --serif: "Iowan Old Style", "Palatino Linotype", Palatino, "Book Antiqua", "TeX Gyre Pagella", P052, Georgia, serif;
+  --sans: Helvetica, Arial, "Nimbus Sans", "DejaVu Sans", sans-serif;
+  --r: 4px;
+  color-scheme: light;
+}
+:root[data-theme="dark"] {
+  --bg: #262624; --panel: #30302E; --soft: #3A3A37; --tint: #2C2C2A;
+  --ink: #DDD9CE; --ink-2: #C9C6BB; --muted: #AAA89F; --rule: #55544F; --hair: #3D3D3A;
+  --clay: #E08A6D; --clay-ink: #EB9B80; --ours: #4C3128;
+  --l1: #4581EB; --l2: #B4823D; --quant: #3EA58D; --fi: #9D61B2;
+  --focus: #EB9B80;
+  color-scheme: dark;
+}
+* { box-sizing: border-box; }
+html { -webkit-text-size-adjust: 100%; }
+body {
+  margin: 0; background: var(--bg); color: var(--ink);
+  font: 16px/1.6 var(--sans);
+  font-feature-settings: "tnum" 1;
+}
+h1, h2, h3, .serif { font-family: var(--serif); }
+a { color: var(--clay-ink); text-decoration-color: color-mix(in srgb, currentColor 40%, transparent); text-underline-offset: 3px; }
+a:hover { text-decoration-color: currentColor; }
+:focus-visible { outline: 2px solid var(--focus); outline-offset: 2px; border-radius: 2px; }
+.wrap { max-width: 1120px; margin: 0 auto; padding: 0 28px; }
+
+/* top bar: thin rule, like the report's running header */
+header.top { border-bottom: 1px solid var(--rule); }
+header.top .wrap { display: flex; align-items: center; gap: 26px; height: 58px; }
+.brand { display: flex; align-items: center; gap: 10px; font-family: var(--serif); font-weight: 700; font-size: 18px; color: var(--ink); text-decoration: none; }
+.brand svg { flex: none; }
+nav.main { display: flex; gap: 18px; flex: 1; min-width: 0; font-size: 14.5px; }
+nav.main a, nav.main span { text-decoration: none; color: var(--muted); padding: 4px 0; }
+nav.main a[aria-current="page"] { color: var(--ink); border-bottom: 2px solid var(--clay); }
+nav.main span { display: inline-flex; gap: 6px; align-items: baseline; }
+nav.main span small { font-size: 12.5px; font-family: var(--serif); font-style: italic; color: var(--muted); }
+.theme-btn { flex: none; border: 1px solid var(--rule); background: transparent; color: var(--ink); width: 34px; height: 34px; border-radius: 50%; cursor: pointer; display: grid; place-items: center; }
+.theme-btn svg { width: 17px; height: 17px; }
+
+/* hero: report title block */
+.hero { padding: 52px 0 6px; }
+.hero h1 { font-size: clamp(32px, 4.6vw, 50px); line-height: 1.1; letter-spacing: -0.01em; margin: 0 0 16px; font-weight: 600; max-width: 28ch; }
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+.notice b { font-weight: 700; }
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+.boards button::after { content: ""; position: absolute; left: 0; right: 0; bottom: -1px; height: 3px; background: var(--c, var(--clay)); transform: scaleX(0); transform-origin: left; transition: transform .2s; }
+.boards button[aria-pressed="true"] { background: var(--tint); }
+.boards button[aria-pressed="true"]::after { transform: scaleX(1); }
+.boards button:focus-visible { outline: 2px solid var(--focus); outline-offset: -3px; }
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+figure.plan-fig { margin: 0; }
+.plan-card { background: var(--tint); border: 1px solid var(--hair); padding: 14px; }
+.plan-head { display: flex; justify-content: flex-end; margin: 0 2px 10px; }
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+figcaption b { color: var(--ink); }
+#plan { display: grid; grid-template-columns: 1fr 1fr; gap: 10px; }
+.bbox { min-width: 0; border: 1px solid var(--rule); border-top: 3px solid var(--c); background: var(--panel); padding: 10px 10px 12px; }
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+.tip b { display: block; font-weight: 700; }
+
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+.toolbar { display: flex; gap: 10px; margin: 36px 0 12px; flex-wrap: wrap; align-items: center; }
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+
+/* problem list */
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+</style>
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+<body>
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+  <div class="wrap">
+    <a class="brand" href="#">
+      <svg width="26" height="26" viewBox="0 0 26 26" aria-hidden="true">
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+      CompoundingKernel
+    </a>
+    <nav class="main" aria-label="Main">
+      <a href="#" aria-current="page">Explore</a>
+      <span>Paper <small>on the way</small></span>
+      <span>Code <small>on the way</small></span>
+      <span>Kernels <small>on the way</small></span>
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+<main class="wrap">
+  <section class="hero">
+    <h1>GPU kernels for every <span style="white-space: nowrap">SOL-ExecBench</span> problem</h1>
+    <p class="lede">CompoundingKernel is a self-improving system that writes and tunes GPU kernels for NVIDIA B200. This page lists the 235 benchmark problems it works on, grouped by leaderboard and by the kind of operation.</p>
+    <div class="notice" role="status"><span><b>Paper, code and kernels are on the way.</b> For now you can browse the problems.</span></div>
+  </section>
+
+  <div class="boards" role="group" aria-label="Filter by leaderboard" id="boards"></div>
+
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+      <div class="plan-head"><div class="legend" id="legend"></div></div>
+      <div id="plan"></div>
+    </div>
+    <figcaption id="plan-cap"><b>Figure 1.</b> All 235 problems by leaderboard and kind of operation, one square per problem.
+      A hollow square means its kernel is on the way. Click a group to filter the list below.</figcaption>
+  </figure>
+
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+    <label class="search">
+      <svg width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" aria-hidden="true"><circle cx="11" cy="11" r="7"/><path d="m20 20-3.5-3.5"/></svg>
+      <input id="q" type="search" placeholder="Search problems, operations or source models" aria-label="Search problems">
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+    <button class="btn" id="reset" type="button">Clear filters</button>
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+  <div class="wrap">
+    <p>SOL-ExecBench and its leaderboards are by NVIDIA: <a href="https://research.nvidia.com/benchmarks/sol-execbench/leaderboard/collection/1/B200">L1</a>, <a href="https://research.nvidia.com/benchmarks/sol-execbench/leaderboard/collection/2/B200">L2</a>, <a href="https://research.nvidia.com/benchmarks/sol-execbench/leaderboard/collection/3/B200">Quant</a>. Problem names come from the public benchmark. The operation groups are our own labels.</p>
+    <p>No kernels are published on this page yet.</p>
+  </div>
+</footer>
+<div class="tip" id="tip" role="tooltip"></div>
+
+<script type="application/json" id="data">
+{"problems":[{"board":"L1","id":"001_attention_softmax_dropout_value_matmul_backward","name":"Attention softmax dropout value matmul backward","group":"Backward pass","op":"attention softmax + dropout + PV backward (GQA 80/8/128)","direction":"backward","model":"ByteDance/Seed-Seed-OSS-36B-Instruct","workloads":16},{"board":"L1","id":"002_vae_conv3x3_groupnorm_silu_residual_fused","name":"VAE conv3x3 GroupNorm SiLU residual fused","group":"Image and video","op":"VAE conv3x3 + GroupNorm + SiLU residual block","direction":"forward","model":"Efficient-Large-Model/Sana-Sprint-1.6B-1024px","workloads":20},{"board":"L1","id":"003_lm_head_projection_with_logit_slicing","name":"LM head projection with logit slicing","group":"Projections and GEMM","op":"LM head GEMM on the last N positions (logit slicing)","direction":"forward","model":"LGAI-EXAONE/EXAONE-4.0-1.2B","workloads":16},{"board":"L1","id":"004_attention_output_projection_with_reshape_backward","name":"Attention output projection 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+    { key: "L1", label: "L1", desc: "single ops", v: "--l1" },
+    { key: "L2", label: "L2", desc: "fused blocks", v: "--l2" },
+    { key: "Quant", label: "Quant", desc: "low precision", v: "--quant" },
+    { key: "FlashInfer", label: "FlashInfer", desc: "serving ops", v: "--fi" },
+  ];
+  const BY = Object.fromEntries(BOARDS.map(b => [b.key, b]));
+  const nOf = f => DATA.filter(f).length;
+  const GROUPS = [...new Set(DATA.map(p => p.group))].sort((a, b) => nOf(p => p.group === b) - nOf(p => p.group === a));
+  const state = { board: null, group: null, q: "" };
+  const $ = id => document.getElementById(id);
+  const esc = s => String(s).replace(/[&<>"]/g, c => ({ "&": "&amp;", "<": "&lt;", ">": "&gt;", '"': "&quot;" }[c]));
+  const reduced = () => window.matchMedia && matchMedia("(prefers-reduced-motion: reduce)").matches;
+
+  /* theme: system preference first, then the reader's choice */
+  const root = document.documentElement;
+  let stored = null; try { stored = localStorage.getItem("ck-theme"); } catch (e) {}
+  setTheme(stored || (window.matchMedia && matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"), false);
+  function setTheme(t, save) {
+    root.dataset.theme = t;
+    $("theme").setAttribute("aria-label", t === "dark" ? "Switch to light mode" : "Switch to dark mode");
+    if (save) { try { localStorage.setItem("ck-theme", t); } catch (e) {} }
+  }
+  $("theme").addEventListener("click", () => setTheme(root.dataset.theme === "dark" ? "light" : "dark", true));
+
+  /* leaderboard buttons */
+  const boardsEl = $("boards");
+  mkBoardBtn(null, "All problems", DATA.length, "--ink");
+  BOARDS.forEach(b => mkBoardBtn(b.key, b.label + ": " + b.desc, nOf(p => p.board === b.key), b.v));
+  function mkBoardBtn(key, label, n, v) {
+    const btn = document.createElement("button");
+    btn.type = "button"; btn.dataset.board = key || "";
+    btn.style.setProperty("--c", "var(" + v + ")");
+    btn.innerHTML = '<span class="n">' + n + '</span><span class="t">' + (key ? '<span class="sw" aria-hidden="true"></span>' : "") + label + "</span>";
+    btn.addEventListener("click", () => { state.board = key; update(); });
+    boardsEl.appendChild(btn);
+  }
+
+  $("legend").innerHTML = BOARDS.map(b => '<span><i style="background:var(' + b.v + ')"></i>' + b.label + "</span>").join("") +
+    '<span><i class="hollow"></i>Kernel on the way</span><span><i class="solid"></i>Matches your filter</span>';
+
+  /* floorplan */
+  const plan = $("plan"), cells = [], grps = [];
+  BOARDS.forEach(b => {
+    {
+      const k = b.key, probs = DATA.filter(p => p.board === k);
+      const box = document.createElement("div"); box.className = "bbox"; box.style.setProperty("--c", "var(" + b.v + ")");
+      box.innerHTML = "<h3>" + b.label + "<span>" + probs.length + " problems</span></h3>";
+      const wrap = document.createElement("div"); wrap.className = "groups"; box.appendChild(wrap);
+      GROUPS.forEach(g => {
+        const list = probs.filter(p => p.group === g);
+        if (!list.length) return;
+        const cols = Math.min(list.length, Math.max(3, Math.ceil(Math.sqrt(list.length) * 1.25)));
+        const btn = document.createElement("button");
+        btn.type = "button"; btn.className = "grp"; btn.setAttribute("aria-pressed", "false");
+        btn.setAttribute("aria-label", b.label + ", " + g + ": " + list.length + " problems. Filter the list.");
+        btn.innerHTML = '<span class="gl">' + esc(g) + "<b>" + list.length + "</b></span>";
+        const grid = document.createElement("span"); grid.className = "cells"; grid.style.setProperty("--cols", cols);
+        list.forEach(p => {
+          const c = document.createElement("span"); c.className = "cell";
+          c.dataset.tip = "<b>" + esc(p.name) + "</b>" + esc(b.label + ", " + g) + "<br>Kernel on the way";
+          grid.appendChild(c); cells.push({ c, p });
+        });
+        btn.appendChild(grid);
+        btn.addEventListener("click", () => {
+          const same = state.board === k && state.group === g;
+          state.board = same ? null : k; state.group = same ? null : g; update();
+        });
+        wrap.appendChild(btn); grps.push({ btn, board: k, group: g });
+      });
+      plan.appendChild(box);
+    }
+  });
+
+  /* tooltip for single squares */
+  const tip = $("tip");
+  plan.addEventListener("mousemove", e => {
+    const t = e.target.closest(".cell");
+    if (!t) { tip.style.visibility = "hidden"; return; }
+    tip.innerHTML = t.dataset.tip; tip.style.visibility = "visible";
+    const r = tip.getBoundingClientRect();
+    let x = e.clientX + 14, y = e.clientY + 14;
+    if (x + r.width > innerWidth - 8) x = e.clientX - r.width - 14;
+    if (y + r.height > innerHeight - 8) y = e.clientY - r.height - 14;
+    tip.style.transform = "translate(" + Math.max(8, x) + "px," + Math.max(8, y) + "px)";
+  });
+  plan.addEventListener("mouseleave", () => { tip.style.visibility = "hidden"; });
+
+  /* operation chips */
+  const chipsEl = $("chips");
+  [null, ...GROUPS].forEach(g => {
+    const c = document.createElement("button");
+    c.type = "button"; c.className = "chip"; c.dataset.group = g || "";
+    c.textContent = g ? g + " (" + nOf(p => p.group === g) + ")" : "All operations";
+    c.addEventListener("click", () => { state.group = g; update(); });
+    chipsEl.appendChild(c);
+  });
+
+  $("q").addEventListener("input", e => { state.q = e.target.value.trim().toLowerCase(); update(); });
+  $("reset").addEventListener("click", () => { state.board = state.group = null; state.q = ""; $("q").value = ""; update(); });
+  $("random").addEventListener("click", () => {
+    state.board = state.group = null; state.q = ""; $("q").value = "";
+    const p = DATA[Math.floor(Math.random() * DATA.length)];
+    update();
+    const el = document.querySelector('[data-id="' + CSS.escape(p.board + "/" + p.id) + '"]');
+    if (el) {
+      el.scrollIntoView({ behavior: reduced() ? "auto" : "smooth", block: "center" });
+      el.classList.remove("flash"); void el.offsetWidth; el.classList.add("flash");
+      el.focus({ preventScroll: true });
+    }
+  });
+
+  function match(p) {
+    if (state.board && p.board !== state.board) return false;
+    if (state.group && p.group !== state.group) return false;
+    if (state.q) {
+      const hay = (p.name + " " + p.id + " " + p.op + " " + p.model + " " + p.group + " " + p.board).toLowerCase();
+      if (!state.q.split(/\s+/).every(w => hay.includes(w))) return false;
+    }
+    return true;
+  }
+
+  function renderList() {
+    const rows = DATA.filter(match);
+    $("count").textContent = rows.length === DATA.length ? "Showing all " + rows.length + " problems" : "Showing " + rows.length + " of " + DATA.length + " problems";
+    $("list").innerHTML = rows.length ? rows.map(p => {
+      const num = p.id.split("_")[0];
+      return '<article class="prob" tabindex="-1" data-id="' + esc(p.board + "/" + p.id) + '" style="--c:var(' + BY[p.board].v + ')">' +
+        "<h3>" + esc(p.name) + "</h3>" +
+        '<p class="op">' + esc(p.op) + "</p>" +
+        '<div class="meta"><span class="b">' + esc(p.board) + (/^\d+$/.test(num) ? " #" + num : "") + "</span><span>" + esc(p.group) + "</span>" +
+        (p.workloads ? "<span>" + p.workloads + " shapes</span>" : "") + "</div>" +
+        '<div class="foot"><span class="model" title="' + esc("Source model: " + p.model) + '">' + esc(p.model) + '</span><span class="wait">Kernel on the way</span></div>' +
+        "</article>";
+    }).join("") : '<div class="empty">No problems match. Try a shorter search or clear the filters.</div>';
+  }
+
+  function update() {
+    const f = state.board || state.group || state.q;
+    [...boardsEl.children].forEach(b => b.setAttribute("aria-pressed", String((b.dataset.board || null) === state.board)));
+    [...chipsEl.children].forEach(c => c.setAttribute("aria-pressed", String((c.dataset.group || null) === state.group)));
+    grps.forEach(g => g.btn.setAttribute("aria-pressed", String(state.group === g.group && (!state.board || state.board === g.board))));
+    cells.forEach(({ c, p }) => { const m = match(p); c.classList.toggle("hit", !!f && m); c.classList.toggle("dim", !!f && !m); });
+    renderList();
+  }
+  update();
+})();
+</script>
+</body>
 </html>