Author: Colfax Research
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NVFP4 Blockscaled GEMM on NVIDIA RTX Pro Blackwell GPUs (SM12x)
In this article, we explore hardware-supported NVFP4 blockscaled GEMM on SM12x GPUs, such as the NVIDIA RTX Pro 6000 Blackwell Server Edition (SM120) or NVIDIA DGX Spark (SM121). We will first discuss features of these GPUs and their kernel programming paradigm, situating them relative to SM10x (e.g. B200 or B300) and SM8x (Ampere/Ada). Then, we will… Go to article…
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Dynamic persistent tile scheduling with Cluster Launch Control (CLC) on NVIDIA Blackwell GPUs
This blog post discusses Cluster Launch Control (CLC), a hardware-supported feature on NVIDIA Blackwell GPUs that facilitates optimal tile scheduling, in particular with respect to load balancing. To provide context, we first survey a few common scheduling strategies and the deficiencies CLC is designed to address. We then walk through the implementation-level details of using… Go to article…
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FlexAttention + FlashAttention-4: Fast and Flexible (External)
In this PyTorch blog on which we collaborated, we explain the FlexAttention extension to FlashAttention-4 (or from another point of view, the incorporation of FA-4 as an attention backend for the PyTorch FlexAttention API). Go to article…
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CUTLASS Tutorial: Hardware-supported Block-scaling with NVIDIA Blackwell GPUs
Welcome to part 4 of our series investigating GEMM on the NVIDIA Blackwell architecture. So far we have discussed the capabilities of the new Blackwell Tensor Core UMMA instructions, including handling sub-byte data types, and how to work with them in CUTLASS. In this part, we will continue our exploration of low-precision computation by discussing… Go to article…
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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… Go to article…
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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.… Go to article…
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CUTLASS Tutorial: Persistent Kernels and Stream-K
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… Go to article…
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GPU Mode: CUTLASS and FlashAttention-3
In this GPU Mode lecture, Jay Shah presents his joint work on FlashAttention-3 and how to implement the main compute loop in the algorithm using CUTLASS. The code discussed in this lecture can be found at this commit in the FlashAttention-3 codebase. Note: Slides adapted from a talk given by Tri Dao. Go to article…
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Epilogue Fusion in CUTLASS with Epilogue Visitor Trees
Welcome to a supplemental article for our tutorial series on GEMM (GEneral Matrix Multiplication). Posts in the main series (1, 2) have discussed performant implementations of GEMM on NVIDIA GPUs by looking at the mainloop, the part responsible for the actual GEMM computation. But the mainloop is only a part of the CUTLASS workload. In… Go to article…
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CUTLASS Tutorial: Efficient GEMM kernel designs with Pipelining
Welcome to Part 2 of our tutorial series on GEMM (GEneral Matrix Multiplication). In Part 1, we discussed the computational side of GEMM by going over WGMMA, which is the primitive instruction to multiply small matrix tiles on GPUs based on the NVIDIA® Hopper™ architecture. In this part, we turn our focus to the memory… Go to article…