Fast, Flexible Allocation for NVIDIA CUDA with RAPIDS Memory Manager – NVIDIA Technical Blog News and tutorials for developers, data scientists, and IT admins 2025-03-24T16:00:00Z http://www.open-lab.net/blog/feed/ Mark Harris <![CDATA[Fast, Flexible Allocation for NVIDIA CUDA with RAPIDS Memory Manager]]> http://www.open-lab.net/blog/?p=22554 2022-08-21T23:40:48Z 2020-12-08T19:27:00Z When I joined the RAPIDS team in 2018, NVIDIA CUDA device memory allocation was a performance problem. RAPIDS cuDF allocates and deallocates memory at high...]]> When I joined the RAPIDS team in 2018, NVIDIA CUDA device memory allocation was a performance problem. RAPIDS cuDF allocates and deallocates memory at high...Image depicting NVIDIA CEO Jen-Hsun Huang explaining the importance of the RAPIDS launch demo at GTC Europe 2018.

When I joined the RAPIDS team in 2018, NVIDIA CUDA device memory allocation was a performance problem. RAPIDS cuDF allocates and deallocates memory at high frequency, because its APIs generally create new and s rather than modifying them in place. The overhead of and synchronization of was holding RAPIDS back. My first task for RAPIDS was to help with this problem, so I created a rough��

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