high performance computing on graphics processing units: hgpu.org

Luis Pineda, Taosha Fan, Maurizio Monge, Shobha Venkataraman, Paloma Sodhi, Ricky Chen, Joseph Ortiz, Daniel DeTone, Austin Wang, Stuart Anderson, Jing Dong, Brandon Amos, Mustafa Mukadam

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Tags: Computer science, CUDA, Deep learning, Nonlinear optimization, nVidia, Package, Tesla V100

July 24, 2022 by hgpu

Optimizing the Performance of Parallel and Concurrent Applications Based on Asynchronous Many-Task Runtimes

Weile Wei

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, Optimization, Package, QMC, Tesla V100, Thesis

July 3, 2022 by hgpu

TPU-KNN: K Nearest Neighbor Search at Peak FLOP/s

Felix Chern, Blake Hechtman, Andy Davis, Ruiqi Guo, David Majnemer, Sanjiv Kumar

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Tags: Algorithms, Computer science, Nearest neighbour, nVidia, nVidia A100, Tesla V100, TPU

July 3, 2022 by hgpu

PILC: Practical Image Lossless Compression with an End-to-end GPU Oriented Neural Framework

Ning Kang, Shanzhao Qiu, Shifeng Zhang, Zhenguo Li, Shutao Xia

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Tags: Algorithms, Compression, Image processing, nVidia, Tesla V100

June 19, 2022 by hgpu

MapReduce for Counting Word Frequencies with MPI and GPUs

Nithin Kavi

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Tags: Algorithms, Computer science, CUDA, Julia, MapReduce, MPI, NLP, nVidia, Optimization, Package, Tesla V100

June 19, 2022 by hgpu

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build & Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build & Test Pipeline of Code Repositories on ANY Language and ANY Platform

CONCUR: a benchmark designed to evaluate multithreaded Java code generated by LLMs

CONCUR: Benchmarking LLMs for Concurrent Code Generation

HIPRT: Ray Tracing using HIP

Ray Tracing using HIP

MXFP4 Training Support Codebase

Practical FP4 Training for Large-Scale MoE Models on Hopper GPUs

CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation

CUDABench: Benchmarking LLMs for Text-to-CUDA Generation

CL4SE: A Context Learning Benchmark For Software Engineering Tasks

CodeScaler: Scaling Code LLM Training and Test-Time Inference via Execution-Free Reward Models

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

See all packages

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high performance computing on graphics processing units: hgpu.org

Understanding the Power of Evolutionary Computation for GPU Code Optimization

Fully Concurrent GPU Data Structures

Long Code for Code Search

Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training

A Container-Based Workflow for Distributed Training of Deep Learning Algorithms in HPC Clusters

Real-Time High-Performance Computing for Embedded Control Systems

Theseus: A Library for Differentiable Nonlinear Optimization

Optimizing the Performance of Parallel and Concurrent Applications Based on Asynchronous Many-Task Runtimes

TPU-KNN: K Nearest Neighbor Search at Peak FLOP/s

PILC: Practical Image Lossless Compression with an End-to-end GPU Oriented Neural Framework

MapReduce for Counting Word Frequencies with MPI and GPUs

Recent source codes

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

CONCUR: a benchmark designed to evaluate multithreaded Java code generated by LLMs

HIPRT: Ray Tracing using HIP

MXFP4 Training Support Codebase

CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation

CUDABench: Benchmarking LLMs for Text-to-CUDA Generation

CL4SE: A Context Learning Benchmark For Software Engineering Tasks

CodeScaler: Scaling Code LLM Training and Test-Time Inference via Execution-Free Reward Models

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

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