Cutlass convolution. Let’s try it with a familiar example. 5 Jan 28, 2026 · cutlass/ # SYCL Templates for Linear Algebra Subroutines and Solvers - headers only arch/ # direct exposure of Intel GPU architecture features (including instruction-level GEMMs) conv/ # code specialized for convolution on Intel GPUs. It incorporates strategies for hierarchical decomposition and data movement. The CUTLASS library provides a collection of CUDA C++ template abstractions that enable high-performance matrix-multiplication at various levels within CUDA, incorporating strategies similar to those Jan 6, 2026 · CUTLASS Device-level Convolution Operator # CUTLASS defines CUDA C++ templates accepting numerous template arguments to specialize the resulting kernel by operation, data type, tile configuration, math instruction, and fused output operation. Furthermore, CUTLASS demonstrates warp-synchronous matrix multiply operations targeting the programmable, high-throughput Tensor Cores implemented by NVIDIA's Volta, Turing, and Ampere architectures. In addition to GEMMs, CUTLASS implements high-performance convolution via the implicit GEMM algorithm. 1 - Feb 2026 CUTLASS is a collection of abstractions for implementing high-performance matrix-matrix multiplication (GEMM) and related computations at all levels and scales within CUDA. 40_cutlass_py example demonstrating CUTLASS with Python interface 41_multi_head_attention example demonstrating attention example with non-fixed sequence length input 42_ampere_tensorop_group_conv example demonstrating how to run group convolution kernels using functions and data structures provided by CUTLASS using tensor cores 43_ell_block Jan 26, 2024 · I have a hard time understanding CUTLASS. Additionaly, CUTLASS implements high-performance convolution (implicit GEMM). Mar 5, 2026 · Implicit GEMM reformulates convolution operations as matrix multiplications (GEMM), enabling CUTLASS to leverage its modular and highly optimized GEMM pipeline. dtfyo qfloiwh khxlvro kpjj ejhns hygf jdpq qjq yzxmy hrxqv