284804ac53
Sources: siboehm/SGEMM_CUDA → upstream_ref/sgemm_siboehm/ (25 files) wangzyon/NVIDIA_SGEMM_PRACTICE → upstream_ref/nvidia_sgemm_practice/ (23 files, filled gaps) edtallison/sgemm-cuda → upstream_ref/sgemm_edtallison/ (41 files) All files cat'd one by one from git clone (no --depth). These are the 3 public SGEMM repos that can compile on CUDA 10.2 + CoreX ivcore10. Key files for BI-V100 porting: kernel 10 (warp tiling) — already proven on device with WARPSIZE=64 kernel 11/12 (double buffering) — next optimization target sgemm.cu + runner.cu — complete build+benchmark harness CMakeLists.txt — build system reference
Upstream Reference: Deep-Spark xllm + vllm (FULL TREE)
Source repos (cloned 2026-08-09, Apache 2.0):
Deep-Spark/xllm— Iluvatar official C++ LLM inference engine (1470 files)Deep-Spark/vllm— Iluvatar official vllm fork (703 files, csrc + model layer)
What's here
xllm/ (complete source minus git/binaries/submodules)
天数智芯官方下一代推理引擎，C++ 原生，多平台(CUDA/ILU/MLU/NPU)。 包含 kernels → layers → models → runtime → scheduler → api_service 完整栈。
Key subtrees:
xllm/core/kernels/ilu/— ixformer API wrappers (ixformer.h是金矿)xllm/core/kernels/cuda/moe/— MoE CUDA kernels (topk_softmax, fused_topk)xllm/core/kernels/cuda/— activation, norm, rope, attention CUDA kernelsxllm/core/layers/ilu/— Iluvatar FusedMoE完整pipelinexllm/core/layers/npu_torch/— GatedDeltaNet C++ implementationxllm/models/llm/qwen3_5.h— Qwen3.5 model definitionxllm/compiler/tilelang/— GDN kernel code generation
ds_vllm/ (csrc + model layers + fused_moe)
天数智芯官方vllm fork，Python + CUDA torch extension。
csrc/— ALL CUDA source (attention, moe, quantization, cache)csrc/libtorch_stable/moe/topk_softmax_kernels.cu— vllm topk_softmaxvllm/_custom_ops.py— Python → torch.ops._moe_C bridgevllm/model_executor/models/qwen3_5.py— ds_vllm的qwen3_5实现vllm/model_executor/layers/fused_moe/— vllm FusedMoE Python layer