high performance computing on graphics processing units: hgpu.org

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hgpu.org » paper

Bit-GraphBLAS: Bit-Level Optimizations of Matrix-Centric Graph Processing on GPU

Jou-An Chen, Hsin-Hsuan Sung, Nathan Tallent, Kevin Barker, Xipeng Shen, Ang Li

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Tags: BLAS, Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX Titan V, Package, Sparse

January 30, 2022 by hgpu

A tool set for random number generation on GPUs in R

Ruoyong Xu, Patrick Brown, Pierre L'Ecuyer

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Tags: Computer science, OpenCL, Package, Pseudo-random number generators, R

January 23, 2022 by hgpu

Reusing Auto-Schedules for Efficient DNN Compilation

Perry Gibson, José Cano

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Tags: Algorithms, Computer science, Machine learning, Neural networks, Performance, Programming Languages

January 23, 2022 by hgpu

Multi-hetero Acceleration by GPU and FPGA for Astrophysics Simulation on oneAPI Environment

Ryuta Kashino, Ryohei Kobayashi, Norihisa Fujita, Taisuke Boku

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Tags: Astrophysics, CUDA, FPGA, Heterogeneous systems, nVidia, OpenCL, Physics, SYCL

January 23, 2022 by hgpu

NNP/MM: Fast molecular dynamics simulations with machine learning potentials and molecular mechanics

Raimondas Galvelis, Alejandro Varela-Rial, Stefan Doerr, Roberto Fino, Peter Eastman, Thomas E. Markland, John D. Chodera, Gianni De Fabritiis

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Tags: Biology, Chemistry, CUDA, GeForce RTX 2080 Ti, Machine learning, Molecular dynamics, Molecular simulation, Neural networks, nVidia, Package

January 23, 2022 by hgpu

Building a Performance Model for Deep Learning Recommendation Model Training on GPUs

Zhongyi Lin, Louis Feng, Ehsan K. Ardestani, Jaewon Lee, John Lundell, Changkyu Kim, Arun Kejariwal, John D. Owens

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Tags: Algorithms, Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce GTX Titan XP, Package, Performance, Tesla P100, Tesla V100

January 23, 2022 by hgpu

Dopia: Online Parallelism Management for Integrated CPU/GPU Architectures

Younghyun Cho, Jiyeon Park, Florian Negele, Changyeon Jo, Thomas R. Gross, Bernhard Egger

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Tags: Code generation, Computer science, Machine learning, OpenCL

January 16, 2022 by hgpu

Fancier: A Unified Framework for Java, C, and OpenCL Integration

Sergio Afonso, Francisco Almeida

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Tags: Computer science, Image processing, Java, OpenACC, OpenCL, Package, Performance

January 16, 2022 by hgpu

Research and Development of Porting SYCL on QNX Operating System for High Parallelism

Dengpan Wang

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Operating systems, PTX, SYCL, Thesis

January 16, 2022 by hgpu

A Compiler Framework for Optimizing Dynamic Parallelism on GPUs

Mhd Ghaith Olabi, Juan Gómez Luna, Onur Mutlu, Wen-mei Hwu, Izzat El Hajj

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Tags: Compilers, Computer science, CUDA, nVidia, Package, Performance, Tesla V100

January 16, 2022 by hgpu

Studying the Potential of Automatic Optimizations in the Intel FPGA SDK for OpenCL

Adel Ejjeh, Vikram Adve, Rob Rutenbar

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Tags: Computer science, Design space exploration, FPGA, HLS, OpenCL

January 16, 2022 by hgpu

Reveal training performance mystery between TensorFlow and PyTorch in the single GPU environment

Hulin Dai, Xuan Peng, Xuanhua Shi, Ligang He, Qian Xiong, Hai Jin

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Tags: Benchmarking, Computer science, CUDA, Deep learning, Neural networks, nVidia, Performance, PyTorch, TensorFlow, Tesla P100

January 9, 2022 by hgpu

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

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

KernelGYM & Dr. Kernel: A distributed GPU environment and a collection of RL training methods to support RL for Kernel Generations

Dr. Kernel: Reinforcement Learning Done Right for Triton Kernel Generations

Theorizer: from the paper Generating Literature-Driven Scientific Discoveries at Scale

Generating Literature-Driven Scientific Theories at Scale

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high performance computing on graphics processing units: hgpu.org

Applications

Bit-GraphBLAS: Bit-Level Optimizations of Matrix-Centric Graph Processing on GPU

A tool set for random number generation on GPUs in R

Reusing Auto-Schedules for Efficient DNN Compilation

Multi-hetero Acceleration by GPU and FPGA for Astrophysics Simulation on oneAPI Environment

NNP/MM: Fast molecular dynamics simulations with machine learning potentials and molecular mechanics

Building a Performance Model for Deep Learning Recommendation Model Training on GPUs

Dopia: Online Parallelism Management for Integrated CPU/GPU Architectures

Fancier: A Unified Framework for Java, C, and OpenCL Integration

Research and Development of Porting SYCL on QNX Operating System for High Parallelism

A Compiler Framework for Optimizing Dynamic Parallelism on GPUs

Studying the Potential of Automatic Optimizations in the Intel FPGA SDK for OpenCL

Reveal training performance mystery between TensorFlow and PyTorch in the single GPU environment

Recent source codes

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

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

KernelGYM & Dr. Kernel: A distributed GPU environment and a collection of RL training methods to support RL for Kernel Generations

Vortex-Optimized Light-weight Toolchain (VOLT)

SciDef: Automated Definition Extraction from Scientific Literature

bioagent-bench: Benchmark for evaluating LLM agents in bioinformatics

Benchmark suite for LLM inference on NVIDIA consumer GPUs

Theorizer: from the paper Generating Literature-Driven Scientific Discoveries at Scale

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