Discover how to accelerate Python data science workflows using GPU-accelerated libraries like cuDF, cuML, and cuGraph for faster data processing and model training.

NVIDIA unveils CUTLASS 4.0, introducing a Python interface to enhance GPU performance for deep learning and high-performance computing, utilizing CUDA Tensors and Spatial Microkernels.

Hands-On GPU Programming with Python and CUDA hits the ground running: you’ll start by learning how to apply Amdahl’s Law, use a code profiler to identify bottlenecks in your Python code, and set up ...

Erfahren Sie, wie Sie die GPU-Beschleunigung in den Python-Bibliotheken für maschinelles Lernen nutzen können, um ein schnelleres Modelltraining und eine verbesserte Leistung zu erzielen.

Keywords: GPU, high-performance computing, parallel computing, benchmarking, computational neuroscience, spiking neural networks, python Citation: Knight JC, Komissarov A and Nowotny T (2021) PyGeNN: ...

HPC benchmarks for Python This is a suite of benchmarks to test the sequential CPU and GPU performance of various computational backends with Python frontends. Specifically, we want to test which high ...

Triton uses Python’s syntax to compile to GPU-native code, without the complexities of GPU programming.

An end-to-end data science ecosystem, open source RAPIDS gives you Python dataframes, graphs, and machine learning on Nvidia GPU hardware ...