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TorchOpt: An Efficient Library for Differentiable Optimization

Jie Ren, Xidong Feng, Bo Liu, Xuehai Pan, Yao Fu, Luo Mai, Yaodong Yang

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Tags: Computer science, CUDA, Deep learning, Mathematical Software, nVidia, OpenMP, Package, PyTorch

November 20, 2022 by hgpu

Capturing the Memory Topology of GPUs

Dominik Größler

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia A100, nVidia GeForce 840 M, nVidia GeForce RTX 2080 Ti, nVidia Quadro P 6000, Package, Performance, PTX, Tesla K20, Tesla V100, Thesis

November 13, 2022 by hgpu

A Study on Neural-based Code Summarization in Low-resource Settings

Yang He

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Tags: Computer science, CUDA, Deep learning, nVidia, PyTorch, Software Engineering, Tesla V100, Thesis

November 13, 2022 by hgpu

iGniter: Interference-Aware GPU Resource Provisioning for Predictable DNN Inference in the Cloud

Fei Xu, Jianian Xu, Jiabin Chen, Li Chen, Ruitao Shang, Zhi Zhou, Fangming Liu

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Tags: Cloud, Computer science, CUDA, Deep learning, Neural networks, nVidia, Package, Tesla V100

November 13, 2022 by hgpu

pyGSL: A Graph Structure Learning Toolkit

Max Wasserman, Gonzalo Mateos

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Tags: Computer science, Machine learning, Package, PyTorch, Signal processing

November 13, 2022 by hgpu

Enabling Data Movement and Computation Pipelining in Deep Learning Compiler

Guyue Huang, Yang Bai, Liu Liu, Yuke Wang, Bei Yu, Yufei Ding, Yuan Xie

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Tags: Computer science, CUDA, Deep learning, nVidia, nVidia A100

November 6, 2022 by hgpu

Using scheduling entropy amplification in CUDA/OpenMP code to exhibit non-reproducibility issues

David Defour

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Tags: Computer science, CUDA, Floating point error, nVidia, nVidia GeForce RTX 2060, OpenMP

November 6, 2022 by hgpu

An Open-source FPGA Library for Data Sorting

Ryohei Kobayashi, Kento Miura, Norihisa Fujita, Taisuke Boku, Toshiyuki Amagasa

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Tags: Algorithms, Computer science, FPGA, OpenCL, Package, Sorting

November 6, 2022 by hgpu

Apple Silicon Performance in Scientific Computing

Connor Kenyon, Collin Capano

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Tags: Benchmarking, Computer science, Heterogeneous systems, nVidia, nVidia A100, nVidia V100, OpenCL, Performance

November 6, 2022 by hgpu

The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science

Santiago Miret, Kin Long Kelvin Lee, Carmelo Gonzales, Marcel Nassar, Matthew Spellings

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Tags: Artificial intelligence, Computer science, Deep learning, Machine learning, Materials Science, Neural networks, Package, Python

November 6, 2022 by hgpu

A systematic performance study of the parallel programming framework SkePU 3 using HPC-benchmarks

Erik Tedhamre

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Tags: Benchmarking, Computer science, CUDA, HPC, nVidia, OpenCL, OpenMP, Package, Performance

October 30, 2022 by hgpu

Benchmarking GPU and TPU Performance with Graph Neural Networks

Xiangyang Ju, Yunsong Wang, Daniel Murnane, Nicholas Choma, Steven Farrell, Paolo Calafiura

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Tags: Artificial intelligence, Benchmarking, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia A100, Pattern recognition, Tesla V100, TPU

October 30, 2022 by hgpu

CLUEstering: a high-performance density-based clustering library for scientific computing

PyOMP: OpenMP for Python in Numba

PyOMP: Parallel programming for CPUs and GPUs with OpenMP and Python

GPPerf: Energy-Aware GPU Performance Prediction and Optimization Framework

Understanding GEMM Performance and Energy on NVIDIA Ada Lovelace: A Machine Learning-Based Analytical Approach

HiRep

Scaling SU(2) to 1000 GPUs using HiRep

Gaia AVU-GSR

Performance portability via C++ PSTL, SYCL, OpenMP, and HIP: the Gaia AVU-GSR case study

gpuPairHMM: Ultra-fast GPU-based PairHMM for DNA Variant Calling

gpuPairHMM: High-speed Pair-HMM Forward Algorithm for DNA Variant Calling on GPUs

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high performance computing on graphics processing units: hgpu.org

TorchOpt: An Efficient Library for Differentiable Optimization

Capturing the Memory Topology of GPUs

A Study on Neural-based Code Summarization in Low-resource Settings

pyGSL: A Graph Structure Learning Toolkit

Enabling Data Movement and Computation Pipelining in Deep Learning Compiler

Using scheduling entropy amplification in CUDA/OpenMP code to exhibit non-reproducibility issues

An Open-source FPGA Library for Data Sorting

Apple Silicon Performance in Scientific Computing

The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science

A systematic performance study of the parallel programming framework SkePU 3 using HPC-benchmarks

Benchmarking GPU and TPU Performance with Graph Neural Networks

Recent source codes

tdg-benchs: benchmarks used to test the performance of taskgraph

SW#SYCL

LLOR: Automatic Repair of OpenMP Programs

Solomon: Simple Off-LOading Macros Orchestrating multiple Notations

CLUEstering: a high-performance density-based clustering library for scientific computing

PyOMP: OpenMP for Python in Numba

GPPerf: Energy-Aware GPU Performance Prediction and Optimization Framework

HiRep

Gaia AVU-GSR

gpuPairHMM: Ultra-fast GPU-based PairHMM for DNA Variant Calling

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