high performance computing on graphics processing units: hgpu.org

hgpu.org » Python

Data-efficient LLM Fine-tuning for Code Generation

Weijie Lv, Xuan Xia, Sheng-Jun Huang

View

Download (PDF)

Source codes

Tags: Code generation, Computer science, CUDA, LLM, nVidia, nVidia A100, Package, Python, PyTorch

April 27, 2025 by hgpu

Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

Jiyuan Wang

View

Download (PDF)

Source codes

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

View

Download (PDF)

Source codes

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

View

Download (PDF)

Source codes

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

View

Download (PDF)

Source codes

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

March 3, 2025 by hgpu

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

Csaba Tóth, Danilo Jr Dela Cruz, Harald Oberhauser

View

Download (PDF)

Source codes

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

January 20, 2025 by hgpu

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

Rémi Genet, Hugo Inzirillo

View

Download (PDF)

Source codes

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

January 20, 2025 by hgpu

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

Ruijun Feng, Hammond Pearce, Pietro Liguori, Yulei Sui

View

Download (PDF)

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

View

Download (PDF)

Source codes

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

View

Download (PDF)

Source codes

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

View

Download (PDF)

Source codes

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

View

Download (PDF)

Source codes

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

December 1, 2024 by hgpu

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Data-efficient LLM Fine-tuning for Code Generation

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

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

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

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

Recent source codes

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration

chemtrain: Training Molecular Dynamics Potentials in JAX

microSYCL: SYCL micro-benchmarks repository

XaaS containers

SYCL Container

CASS: Cuda-Amd aSSembly

Cluser of smartphones for edge computing application using TensorFlow

CFAL-bench

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

Most viewed papers (last 30 days)