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Writing a CUDA kernel requires a shift in mental model. Instead of one fast processor, you manage thousands of tiny threads. Here is the code and the logic explained for Matrix Multiplication.

Ashutosh Maheshwari

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189,009 次观看 • 7 个月前 •via X (Twitter)

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Fast matrix multiplication on GPUs has traditionally meant wrestling with threads, shared memory, and low-level hardware details. This webinar explores how NVIDIA’s CUDA Tile model—and its Julia port, cuTile.jl—makes high-performance GPU programming more accessible. Join Dr. Andy Terrel of NVIDIA and Dr. Tim Besard of JuliaHub to see real examples across linear algebra, AI inference, and HPC. Register here - #JuliaLang #GPUProgramming #CUDA #HPC #AIInfrastructure

Fast matrix multiplication on GPUs has traditionally meant wrestling with threads, shared memory, and low-level hardware details. This webinar explores how NVIDIA’s CUDA Tile model—and its Julia port, cuTile.jl—makes high-performance GPU programming more accessible. Join Dr. Andy Terrel of NVIDIA and Dr. Tim Besard of JuliaHub to see real examples across linear algebra, AI inference, and HPC. Register here - #JuliaLang #GPUProgramming #CUDA #HPC #AIInfrastructure

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