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Parallel Programming with Python
Stanford CS149 I Parallel Computing I 2023 I Lecture 18 - Hardware Specialization
Ipython Parallel Docs
IPython's Task Database
Ipyparallel Examples
- CUDA (NVIDIA only)
- Write C code that runs directly on the GPU
- Higher-level APIs: cuBLAS,cuFFT, cuDNN, etc.
- OpenCL
- Similar to CUDA, but runs on anything
- Usually slower :(
- Udacity: Intro to Parallel Programming
- For deep learning just use existing libraries
Notes above from Li, Karpathy, and Johnson (2016) Lecture 11-92
NVIDIA CUDA Getting Started Guide for Mac OS X Guide for installing CUDA development tools.
NVIDIA GeForce GT 750m Info Page
CUDA Downloads Page
CUDA Quick Start Guide
NVIDIA IndeX Plug-in for ParaView: Analyze and Visualize Large Volume Geophysics Datasets
Jetson TX1 - GIE GPU Inference Engine For inference rather than training.
Getting Started with NVIDIA and Deep Learning Beginner-level tutorials including Caffe, Theano, and Torch.
Array Fire Home ArrayFire is a blazing fast software library for GPU computing. A few lines of code in ArrayFire can replace dozens of lines of raw GPU code.