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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.

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Ashutosh Maheshwari

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189,009 просмотров • 7 месяцев назад •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
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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

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How many of the big ideas of the past 15 years of AI are downstream of hardware constraints? The big hardware story over that period is that logic has become way cheaper than data transfer. Stacking huge numbers of matrix multiplies was perfect for this hardware regime, because matrix multiplication is logic-intensive but requires less data transfer. And so we got matmul-heavy deep learning. It's interesting to think about what AI would look like in a world where these costs didn't diverge so much.
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How many of the big ideas of the past 15 years of AI are downstream of hardware constraints? The big hardware story over that period is that logic has become way cheaper than data transfer. Stacking huge numbers of matrix multiplies was perfect for this hardware regime, because matrix multiplication is logic-intensive but requires less data transfer. And so we got matmul-heavy deep learning. It's interesting to think about what AI would look like in a world where these costs didn't diverge so much.

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