Matrix transposition in CUDA using shared memory
In a post published on February 26, 2026, Mark Harris on the NVIDIA blog explains how shared memory can improve matrix-transposition performance in CUDA C/C++.
Source: Transposição eficiente de matrizes em CUDA C/C++ (developer.nvidia.com). Text prepared with AI from this source.
What happened and what to do
Mark Harris's post on the NVIDIA blog, published on February 26, 2026, covers matrix transposition in CUDA C/C++. It focuses on using GPU shared memory to improve the performance of this common operation; the information provided here does not give a numerical performance gain.
A company running GPU workloads can assess whether transpositions are a bottleneck, compare implementations with and without shared memory, and monitor latency and resource use across matrix sizes. Wendelmaques can help structure this assessment and integrate measurements into the existing engineering workflow.
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Wendelmaques can diagnose GPU-operation bottlenecks, scope a comparison of CUDA implementations, and deliver the instrumentation and monitoring needed to track performance in operation.
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Send a short description of your GPU workload and observed bottleneck to receive a scoped proposal.
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