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CUTLASS 3.x APIs: Orthogonal, Reusable, and Composable Abstractions for GEMM Kernel Design (External)
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In this blog post presented on the NVIDIA technical blog, we give a concise introduction to the CUTLASS 3.x APIs, focusing on the collective, kernel, and device layers and the functionality of the collective builders. This post was authored in conjunction with members of the CUTLASS team.
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CUTLASS Tutorial: Sub-byte GEMM on NVIDIA® Blackwell GPUs
Welcome to part 3 of our series investigating GEMM on the NVIDIA Blackwell architecture. In parts 1 and 2, we looked at the Tensory Memory and 2 SM capabilities of the new Blackwell Tensor Core UMMA instructions and how to work with them in CUTLASS. In this part, we introduce low-precision computation and then discuss…
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CUTLASS Tutorial: GEMM with Thread Block Clusters on NVIDIA® Blackwell GPUs
Welcome to part two of our series investigating GEMM on the NVIDIA Blackwell architecture. In part 1, we introduced some key new features available on NVIDIA Blackwell GPUs, including Tensor Memory, and went over how to write a simple CUTLASS GEMM kernel that uses the new UMMA instructions (tcgen05.mma) to target the Blackwell Tensor Cores.…
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CUTLASS Tutorial: Writing GEMM Kernels Using Tensor Memory For NVIDIA® Blackwell GPUs
The NVIDIA Blackwell architecture introduces some new features that significantly change the shape of a GEMM kernel. In this series of posts, we explore the new features available on Blackwell and examine how to write CUTLASS GEMM kernels that utilize these new features by drawing on the CuTe tutorial examples. The goal of this series…
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DeepSeek-R1 and FP8 Mixed-Precision Training
DeepSeek has shocked the world with the release of their reasoning model DeepSeek-R1. Similar to OpenAI’s o1 and Google Gemini’s Flash Thinking, the R1 model aims to improve the quality of its replies by generating a “chain of thought” before responding to a prompt. The excitement around R1 stems from it achieving parity with o1…
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CUTLASS Tutorial: Persistent Kernels and Stream-K
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Welcome to Part 3 of our tutorial series on GEMM (GEneral Matrix Multiplication). In Parts 1 and 2, we discussed GEMM at length from the perspective of a single threadblock, introducing the WGMMA matmul primitive, pipelining, and warp specialization. In this part, we will examine GEMM from the perspective of the entire grid. At this…
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