套件：libcutlass-dev（3.1.0+ds-2） [contrib]
libcutlass-dev 的相關連結
Debian 的資源：
下載原始碼套件 nvidia-cutlass：
- [nvidia-cutlass_3.1.0+ds-2.dsc]
- [nvidia-cutlass_3.1.0+ds.orig.tar.xz]
- [nvidia-cutlass_3.1.0+ds-2.debian.tar.xz]
維護小組：
外部的資源：
- 主頁 [github.com]
相似套件：
CUDA Templates for Linear Algebra Subroutines
CUTLASS is a collection of CUDA C++ template abstractions for implementing high-performance matrix-matrix multiplication (GEMM) and related computations at all levels and scales within CUDA. It incorporates strategies for hierarchical decomposition and data movement similar to those used to implement cuBLAS and cuDNN. CUTLASS decomposes these "moving parts" into reusable, modular software components abstracted by C++ template classes. Primitives for different levels of a conceptual parallelization hierarchy can be specialized and tuned via custom tiling sizes, data types, and other algorithmic policy. The resulting flexibility simplifies their use as building blocks within custom kernels and applications.
To support a wide variety of applications, CUTLASS provides extensive support for mixed-precision computations, providing specialized data-movement and multiply-accumulate abstractions for half-precision floating point (FP16), BFloat16 (BF16), Tensor Float 32 (TF32), single-precision floating point (FP32), FP32 emulation via tensor core instruction, double-precision floating point (FP64) types, integer data types (4b and 8b), and binary data types (1b). CUTLASS demonstrates warp-synchronous matrix multiply operations targeting the programmable, high-throughput Tensor Cores implemented by NVIDIA's Volta, Turing, Ampere, and Hopper architectures.
This is a header-only library.