Posts
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Improving FP16/16 matmul accuracy with two-stage accumulation
On Nvidia consumer GPUs such as the RTX 4090, FP16/32 matrix multiplication is limited to run at half the speed of FP16/16, meaning users need to choose between either using tensor core operations that accumulate in FP16 precision or only getting 50% of the GPU’s peak performance.
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Implementing a fast Tensor Core matmul on the Ada Architecture
Using Tensor Cores is now a prerequisite to get anywhere near peak performance on NVIDIA GPUs. In this post we work through the process of developing an efficient Tensor Core matrix multiplication kernel targeting the Ada architecture.
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