Iteratively optimizing CUDA matrix multiplication
In an article published on February 26, 2026, Simon Boehm describes optimizing a CUDA kernel to approach cuBLAS performance, not to replace it.
Source: Um diário de otimização de multiplicação de matrizes em CUDA (siboehm.com). Text prepared with AI from this source.
What happened and what to do
In an article published on February 26, 2026, Simon Boehm documents the iterative optimization of a CUDA kernel for matrix multiplication. The goal is to understand how to approach cuBLAS performance, not to build a replacement; the diary presents concrete optimization decisions for a common GPU operation.
A company running GPU workloads can apply this approach to benchmark relevant kernels, identify bottlenecks, and test improvements in a controlled way. A benchmarking and monitoring pipeline can track latency, throughput, and regressions across configurations, informing decisions about optimizing custom kernels or using existing libraries.
How the consultancy can help
Wendelmaques can diagnose a GPU workload, define evaluation metrics and scope, and implement benchmarking and monitoring pipelines or kernel optimizations. Operating and evolving the solution can also be included in the scope.
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