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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

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

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

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

gSuite: A Flexible and Framework Independent Benchmark Suite for Graph Neural Network Inference on GPUs

Taha Tekdoğan, Serkan Göktaş, Ayse Yilmazer-Metin

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Tags: Benchmarking, Computer science, CUDA, Machine learning, Neural networks, nVidia, Performance, Tesla V100

October 30, 2022 by hgpu

Providing performance portable numerics for Intel GPUs

Yu-Hsiang M. Tsai, Terry Cojean, Hartwig Anzt

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Tags: AMD Radeon Instinct MI100, ATI, Computer science, CUDA, Linear Algebra, nVidia, nVidia A100, OpenCL, Package, performance portability, Sparse, SYCL

October 30, 2022 by hgpu

torchode: A Parallel ODE Solver for PyTorch

Marten Lienen, Stephan Günnemann

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Tags: Computer science, Deep learning, Numerical Analysis, nVidia, nVidia GeForce GTX 1080 Ti, ODEs, Ordinary differential equations, Package, PyTorch

October 30, 2022 by hgpu

Tausch: A halo exchange library for large heterogeneous computing systems using MPI, OpenCL, and CUDA

Lukas Spies, Amanda Bienz, David Moulton, Luke Olson, Andrew Reisner

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Tags: Computer science, CUDA, Heterogeneous systems, MPI, nVidia, OpenCL, Package, Tesla V100

October 23, 2022 by hgpu

Harness Engineering for LLM-Driven GPU Kernel Generation

CANNBot Skills

Compiler-Grounded Hierarchical Diagnosis for LLM-Based Triton Kernel Optimization

NVIDIA Object Oriented Agents

NVIDIA-labs OO Agents: Native Python Object-Oriented Agents

UniCoder: Unified Visual-to-Code Generation via Symbolic Rewards and Reference-Guided Code Optimization

CuFuzz: An API-Knowledge-Graph Coverage-Driven Fuzzing Framework for CUDA Libraries

AutoPass: Evidence-Guided LLM Agents for Compiler Performance Tuning

Probe-and-Refine Tuning of Repository Guidance for AI Coding Agents

Probe-and-Refine Tuning of Repository Guidance for Coding Agents

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high performance computing on graphics processing units: hgpu.org

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

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

Apple Silicon Performance in Scientific Computing

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

Benchmarking GPU and TPU Performance with Graph Neural Networks

gSuite: A Flexible and Framework Independent Benchmark Suite for Graph Neural Network Inference on GPUs

Providing performance portable numerics for Intel GPUs

torchode: A Parallel ODE Solver for PyTorch

Tausch: A halo exchange library for large heterogeneous computing systems using MPI, OpenCL, and CUDA

Recent source codes

TensorCorePTX: PTX Tensor Core GEMM Exploration

FlashPDE: Triton-fused PDE stencil kernels for physics-informed neural networks

PortLBM: Portable GPU-accelerated Lattice Boltzmann Method Simulations

Harness Engineering for LLM-Driven GPU Kernel Generation

CANNBot Skills

NVIDIA Object Oriented Agents

UniCoder: Unified Visual-to-Code Generation via Symbolic Rewards and Reference-Guided Code Optimization

CuFuzz: An API-Knowledge-Graph Coverage-Driven Fuzzing Framework for CUDA Libraries

AutoPass: Evidence-Guided LLM Agents for Compiler Performance Tuning

Probe-and-Refine Tuning of Repository Guidance for AI Coding Agents

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