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Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

Jiyuan Wang

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Tags: Benchmarking, Computer science, FPGA, Heterogeneous systems, oneAPI, Package, Python, Thesis

March 30, 2025 by hgpu

WgPy: GPU-accelerated NumPy-like array library for web browsers

Masatoshi Hidaka, Tatsuya Harada

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce RTX 4070, Python

March 10, 2025 by hgpu

pyATF: Constraint-Based Auto-Tuning in Python

Richard Schulze, Sergei Gorlatch, Ari Rasch

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Tags: Auto-Tuning, Compilers, Computer science, CUDA, nVidia A100, OpenCL, Package, Performance, Python

March 3, 2025 by hgpu

TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators

Jianling Li, Shangzhan Li, Zhenye Gao, Qi Shi, Yuxuan Li, Zefan Wang, Jiacheng Huang, Haojie Wang, Jianrong Wang, Xu Han, Zhiyuan Liu, Maosong Sun

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Tags: Benchmarking, Code generation, Computer science, CUDA, Deep learning, LLM, nVidia, nVidia A100, Package, Python

March 3, 2025 by hgpu

Keras Sig: Efficient Path Signature Computation on GPU in Keras 3

Rémi Genet, Hugo Inzirillo

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Tags: Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce RTX 4090, Package, Python, TensorFlow

January 20, 2025 by hgpu

A User’s Guide to KSig: GPU-Accelerated Computation of the Signature Kernel

Csaba Tóth, Danilo Jr Dela Cruz, Harald Oberhauser

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Tags: Computer science, CUDA, Machine learning, nVidia, nVidia A100, Package, Python

January 20, 2025 by hgpu

CGP-Tuning: Structure-Aware Soft Prompt Tuning for Code Vulnerability Detection

Ruijun Feng, Hammond Pearce, Pietro Liguori, Yulei Sui

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Tags: Computer science, CUDA, LLM, nVidia, nVidia H100, Python, PyTorch, Security

January 13, 2025 by hgpu

LeetDecoding: A PyTorch Library for Exponentially Decaying Causal Linear Attention with CUDA Implementations

Jiaping Wang, Simiao Zhang, Qiao-Chu He, Yifan Chen

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Tags: Benchmarking, Computer science, CUDA, LLM, Machine learning, nVidia, nVidia A100, nVidia RTX A6000, Package, Python, PyTorch

January 13, 2025 by hgpu

TorchQC – A framework for efficiently integrating machine and deep learning methods in quantum dynamics and control

Dimitris Koutromanos, Dionisis Stefanatos, Emmanuel Paspalakis

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Tags: Deep learning, Machine learning, Package, Physics, Python, PyTorch, Quantum Physics

December 29, 2024 by hgpu

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

Simone Balducci

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Tags: Astrophysics, ATI, Clustering, CUDA, FPGA, HIP, Machine learning, nVidia, Package, performance portability, Physics, Python, Tesla T4, Thesis

December 1, 2024 by hgpu

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

Giorgis Georgakoudis, Todd A. Anderson, Stuart Archibald, Bronis de Supinski,Timothy G. Mattson

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Tags: Benchmarking, Computer science, nVidia, nVidia A100, OpenMP, Package, Python

December 1, 2024 by hgpu

Bringing Auto-tuning to HIP: Analysis of Tuning Impact and Difficulty on AMD and Nvidia GPUs

Milo Lurati, Stijn Heldens, Alessio Sclocco, Ben van Werkhoven

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Tags: AMD Radeon Instinct MI250X, AMD Radeon Pro W6600, ATI, Computer science, CUDA, HIP, nVidia, nVidia A100, nVidia RTX A4000, Package, Performance, Python

July 28, 2024 by hgpu

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

DuoReduce: MLIR's benchmark

Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

Shamrock: Multi-GPU hydrodynamics for astrophysics

The Shamrock code: I- Smoothed Particle Hydrodynamics on GPUs

LLMPerf: GPU Performance Modeling meets Large Language Models

Hercules: A Compiler for Productive Programming of Heterogeneous Systems

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high performance computing on graphics processing units: hgpu.org

Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

WgPy: GPU-accelerated NumPy-like array library for web browsers

pyATF: Constraint-Based Auto-Tuning in Python

TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators

Keras Sig: Efficient Path Signature Computation on GPU in Keras 3

A User’s Guide to KSig: GPU-Accelerated Computation of the Signature Kernel

CGP-Tuning: Structure-Aware Soft Prompt Tuning for Code Vulnerability Detection

LeetDecoding: A PyTorch Library for Exponentially Decaying Causal Linear Attention with CUDA Implementations

TorchQC – A framework for efficiently integrating machine and deep learning methods in quantum dynamics and control

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

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

Bringing Auto-tuning to HIP: Analysis of Tuning Impact and Difficulty on AMD and Nvidia GPUs

Recent source codes

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

DuoReduce: MLIR's benchmark

Shamrock: Multi-GPU hydrodynamics for astrophysics

LLMPerf: GPU Performance Modeling meets Large Language Models

Hercules: A Compiler for Productive Programming of Heterogeneous Systems

Celerity Runtime: High-level C++ for Accelerator Clusters

wgpy: WebGL accelerated numpy-compatible array library for web browser

Microbenchmarking OpenMP target offload with Catch2

SUperman: Highly Efficient Permanent Computation Library

TransCL: An Automatic CUDA-to-OpenCL Programs Transformation Framework

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