CUTLASS
An open-source, high-performance CUDA C++ template library developed by NVIDIA for implementing efficient GEMM (General Matrix Multiplication) and related linear algebra computation kernels. It is widely used in deep learning inference acceleration.
Timeline (last 90 days)
CUTLASS is a C++ template library open-sourced by NVIDIA in 2017, targeting high-performance linear algebra operations on GPUs.
CUTLASS encapsulates shared memory tiling, register-level data pipelining, Tensor Core scheduling, and asynchronous memory copying in CUDA programming into hierarchical, composable templates.
The full name of CUTLASS is CUDA Templates for Linear Algebra Subroutines and Solvers.
DeepEP corresponds to NCCL, handling communication between multiple GPUs; FlashMLA corresponds to FlashAttention, used for ultra-long text processing; TileKernels corresponds to CUTLASS; DeepSelect corresponds to data pipelines such as DALI.
CuTe DSL is a core subsystem within NVIDIA's CUTLASS library that uses C++ template metaprogramming to provide a unified description of tensors' logical layout and physical storage, and can generate highly specialized kernel code at compile time.
cuBLAS is NVIDIA's official highly optimized linear algebra library, and CuTe DSL is a domain-specific language in the CUTLASS ecosystem used to describe tensor layouts and computations.
The article recommends verifying the benefits and stability of the CUTLASS backend on specific workloads in a test environment before adopting it in production, as the new feature has limited coverage and unstable edge cases in its early stages.
In PyTorch 2.14, NVGEMM brings CUTLASS kernels generated by CuTeDSL into the Inductor compilation backend.
Bringing CUTLASS kernels into Inductor is expected to deliver substantial performance gains for GEMM-intensive workloads such as Transformers and large language model training and inference.
cuBLAS is NVIDIA's black-box implementation for general-purpose scenarios, whereas CUTLASS exposes more tuning knobs in an open, templatized manner.
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CompileIQ 可用于优化 cuBLAS、cuDNN、CUTLASS 等底层库的内核级编译
80%UnverifiedCUTLASS is a high-performance CUDA C++ template library introduced by NVIDIA, specifically designed to implement efficient computation kernels such as GEMM (General Matrix Multiply).
70%UnverifiedCUTLASS encapsulates shared memory tiling, register-level data pipelining, Tensor Core scheduling, and asynchronous memory copying in CUDA programming into hierarchical, composable templates.
50%UnverifiedCUTLASS is a C++ template library open-sourced by NVIDIA in 2017, targeting high-performance linear algebra operations on GPUs.
50%UnverifiedThe full name of CUTLASS is CUDA Templates for Linear Algebra Subroutines and Solvers.
50%UnverifiedDeepEP corresponds to NCCL, handling communication between multiple GPUs; FlashMLA corresponds to FlashAttention, used for ultra-long text processing; TileKernels corresponds to CUTLASS; DeepSelect corresponds to data pipelines such as DALI.
50%UnverifiedCuTe DSL is a core subsystem within NVIDIA's CUTLASS library that uses C++ template metaprogramming to provide a unified description of tensors' logical layout and physical storage, and can generate highly specialized kernel code at compile time.
50%UnverifiedcuBLAS is NVIDIA's official highly optimized linear algebra library, and CuTe DSL is a domain-specific language in the CUTLASS ecosystem used to describe tensor layouts and computations.
50%UnverifiedIn PyTorch 2.14, NVGEMM brings CUTLASS kernels generated by CuTeDSL into the Inductor compilation backend.
50%UnverifiedcuBLAS is NVIDIA's black-box implementation for general-purpose scenarios, whereas CUTLASS exposes more tuning knobs in an open, templatized manner.
50%UnverifiedThe article recommends verifying the benefits and stability of the CUTLASS backend on specific workloads in a test environment before adopting it in production, as the new feature has limited coverage and unstable edge cases in its early stages.
50%UnverifiedVersion v0.29.0rc1 focuses on fixing a defect in the CUTLASS MoE permutation logic, corresponding to PR #54747 "Handle padded routes in CUTLASS MoE permutations".
50%UnverifiedImproper handling of padded routes can cause the output of real tokens to be overwritten or mixed with garbage values, and errors are significantly amplified when batch sizes are small and the number of experts is large.
50%UnverifiedCUTLASS is a template library that allows developers to flexibly customize core matrix operations such as GEMM while maintaining high performance.
50%UnverifiedBringing CUTLASS kernels into Inductor is expected to deliver substantial performance gains for GEMM-intensive workloads such as Transformers and large language model training and inference.
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