Knowledge Base Resources
Contributed by cyberinfrastructure professionals (researchers, research computing facilitators, research software engineers and HPC system administrators), these resources are shared through the ConnectCI community platform. Add resources you find helpful!
Examples of Thrust code for GPU Parallelization
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Some examples for writing Thrust code. To compile, download the CUDA compiler from NVIDIA. This code was tested with CUDA 9.2 but is likely compatible with other versions. Before compiling change extension from thrust_ex.txt to thrust_ex.cu. Any code on the device (GPU) that is run through a Thrust transform is automatically parallelized on the GPU. Host (CPU) code will not be. Thrust code can also be compiled to run on a CPU for practice.
GPU Acceleration in Python
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This tutorial explains how to use Python for GPU acceleration with libraries like CuPy, PyOpenCL, and PyCUDA. It shows how these libraries can speed up tasks like array operations and matrix multiplication by using the GPU. Examples include replacing NumPy with CuPy for large datasets and using PyOpenCL or PyCUDA for more control with custom GPU kernels. It focuses on practical steps to integrate GPU acceleration into Python programs.
Introduction to Parallel Programming for GPUs with CUDA
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This tutorial provides a comprehensive introduction to CUDA programming, focusing on essential concepts such as CUDA thread hierarchy, data parallel programming, host-device heterogeneous programming model, CUDA kernel syntax, GPU memory hierarchy, and memory optimization techniques like global memory coalescing and shared memory bank conflicts. Aimed at researchers, students, and practitioners, the tutorial equips participants with the skills needed to leverage GPU acceleration for scalable computation, particularly in the context of AI.