OpenCL integration for Python, plus shiny features
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Updated
Nov 14, 2024 - Python
OpenCL integration for Python, plus shiny features
High-Performance Computing: CPU Instructions, GPU OpenCL & CUDA, etc. ☀️
We present an algorithm to dynamically adjust the data assigned for each worker at every epoch during the training in a heterogeneous cluster. We empirically evaluate the performance of the dynamic partitioning by training deep neural networks on the CIFAR10 dataset.
To accompany the paper "An efficient new static scheduling heuristic for accelerated architectures".
GloVe representation with parallelization
Simulating ADC background calibration algorithms in OpenCL
Distributed_compy is a distributed computing library that offers multi-threading, heterogeneous (CPU + mult-GPU), and multi-node support
Convolutional Nets implemented in pyCuda.
PyroParallel : A Parallel Computing Framework for Efficient Mesh Processing and Optimization
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