high performance computing on graphics processing units: hgpu.org

Xiaojie Wu, Qiming Sun, Zhichen Pu, Tianze Zheng, Wenzhi Ma, Wen Yan, Xia Yu, Zhengxiao Wu, Mian Huo, Xiang Li, Weiluo Ren, Sheng Gong, Yumin Zhang, Weihao Gao

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Tags: Chemical Physics, Chemistry, Computational Physics, CUDA, nVidia, nVidia A100, Package, Python, Quantum Physics

April 21, 2024 by hgpu

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

Erik D. Huckvale, Hunter N.B. Moseley

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Tags: Computer science, CUDA, nVidia, Package, Performance, Profiling, Python

April 7, 2024 by hgpu

Using Intel oneAPI for Multi-hybrid Acceleration Programming with GPU and FPGA Coupling

Wentao Liang, Norihisa Fujita, Ryohei Kobayashi, Taisuke Boku

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Tags: Computer science, CUDA, FPGA, Heterogeneous systems, nVidia, oneAPI, OpenCL, SYCL, Tesla V100

April 7, 2024 by hgpu

Retargeting and Respecializing GPU Workloads for Performance Portability

Ivan R. Ivanov, Oleksandr Zinenko, Jens Domke, Toshio Endo, William S. Moses

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Tags: AMD Radeon Instinct MI210, AMD Radeon RX 6800, ATI, Computer science, CUDA, HIP, HPC, nVidia, nVidia A100, nVidia RTX A4000, Package, performance portability

March 24, 2024 by hgpu

VibeCodeHPC - Multi Agentic Vibe Coding for HPC

VibeCodeHPC: An Agent-Based Iterative Prompting Auto-Tuner for HPC Code Generation Using LLMs

Compile-Time Resource Safety for GPU APIs: A Low-Overhead Typestate Framework

Towards Robust Agentic CUDA Kernel Benchmarking, Verification, and Optimization

HPC Benchmark Survey

An HPC Benchmark Survey and Taxonomy for Characterization

HDM: Home made Diffusion Models

Home-made Diffusion Model from Scratch to Hatch

General Matrix Multiplication (GEMM)

High Performance Matrix Multiplication

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high performance computing on graphics processing units: hgpu.org

Programming

STuning-DL: Model-Driven Autotuning of Sparse GPU Kernels for Deep Learning

Kernel-Centric Optimizations for Deep Neural Networks on GPGPU

Hierarchical Resource Partitioning on Modern GPUs: A Reinforcement Learning Approach

CuPBoP: Making CUDA a Portable Language

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

Optimizing Hardware Resource Partitioning and Job Allocations on Modern GPUs under Power Caps

Automatic BLAS Offloading on Unified Memory Architecture: A Study on NVIDIA Grace-Hopper

Communication-Efficient Large-Scale Distributed Deep Learning: A Comprehensive Survey

Python-Based Quantum Chemistry Calculations with GPU Acceleration

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

Using Intel oneAPI for Multi-hybrid Acceleration Programming with GPU and FPGA Coupling

Retargeting and Respecializing GPU Workloads for Performance Portability

Recent source codes

VibeCodeHPC - Multi Agentic Vibe Coding for HPC

Compile-Time Resource Safety for GPU APIs: A Low-Overhead Typestate Framework

exa-AMD: Exascale Accelerated Materials Discovery

TRUST: a thermalhydraulic software package for CFD simulations

Modular: The Modular Platform (includes MAX & Mojo)

Allo: Accelerator Design Language

Towards Robust Agentic CUDA Kernel Benchmarking, Verification, and Optimization

HPC Benchmark Survey

HDM: Home made Diffusion Models

General Matrix Multiplication (GEMM)

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