At Google I/O’24, Laurence Moroney, head of AI Advocacy at Google, announced that RAPIDS cuDF is now integrated into Google Colab. Developers can now instantly…
A collection of accelerated computing packages and AI models designed to streamline and expedite the development of advanced AI robotics applications.
NVIDIA ACE is a suite of technologies that help developers bring digital humans to life.
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…
The NVIDIA Tools Extension (NVTX) library lets developers annotate custom events and ranges within the profiling timelines generated using tools such as the…
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Today I’m happy to announce that the CUDA Toolkit 7.5 Release Candidate is now available. The CUDA Toolkit 7.5 adds support for FP16 storage for up to 2x larger…
This post is the first in a series on CUDA Fortran, which is the Fortran interface to the CUDA parallel computing platform. If you are familiar with CUDA C…
In the previous three posts of this CUDA C & C++ series we laid the groundwork for the major thrust of the series: how to optimize CUDA C/C++ code.
Apache Spark provides capabilities to program entire clusters with implicit data parallelism. With Spark 3.0 and the open source RAPIDS Accelerator for Spark…