high performance computing on graphics processing units: hgpu.org

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Static and Dynamic Analyses for Efficient GPU Execution

Philip Munksgaard

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Tags: AMD Radeon Instinct MI100, ATI, Benchmarking, Computer science, nVidia, nVidia A100, OpenCL, Performance, Thesis

August 13, 2023 by hgpu

A portable C++ library for memory and compute abstraction on multi-core CPUs and GPUs

Pietro Incardona, Aryaman Gupta, Serhii Yaskovets, Ivo F. Sbalzarini

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Tags: AMD RX Vega 64, ATI, Computer science, CUDA, nVidia, nVidia A100, nVidia GeForce RTX 3090, OpenACC, OpenCL, OpenMP, Package, Performance, performance portability, SYCL

July 30, 2023 by hgpu

Fast Knowledge Graph Completion using Graphics Processing Units

Chun-Hee Lee, Dong-oh Kang, Hwa Jeon Song

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Tags: AI, Algorithms, Computer science, CUDA, Databases, Graph theory, nVidia, nVidia A100

July 30, 2023 by hgpu

qecGPT: decoding Quantum Error-correcting Codes with Generative Pre-trained Transformers

Hanyan Cao, Feng Pan, Yijia Wang, Pan Zhang

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Tags: Artificial intelligence, Computer science, Neural networks, nVidia, nVidia A100, Package

July 24, 2023 by hgpu

Maximizing Parallelism and GPU Utilization For Direct GPU Compilation Through Ensemble Execution

Shilei Tian, Barbara Chapman, Johannes Doerfert

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Tags: Benchmarking, Computer science, CUDA, LLVM, nVidia, nVidia A100, OpenMP, Performance

July 24, 2023 by hgpu

Safe, Seamless, And Scalable Integration Of Asynchronous GPU Streams In PETSc

Jacob Faibussowitsch, Mark F. Adams, Richard Tran Mills, Stefano Zampini, Junchao Zhang

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Tags: Computer science, CUDA, nVidia, nVidia A100, Package, Performance

July 9, 2023 by hgpu

Modeling Parallel Programs using Large Language Models

Daniel Nichols, Aniruddha Marathe, Harshitha Menon, Todd Gamblin, Abhinav Bhatele

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Tags: Code generation, Computer science, HPC, MPI, nVidia, nVidia A100, OpenMP

July 9, 2023 by hgpu

cuSLINK: Single-linkage Agglomerative Clustering on the GPU

Corey J. Nolet, Divye Gala, Alex Fender, Mahesh Doijade, Joe Eaton, Edward Raff, John Zedlewski, Brad Rees, Tim Oates

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Tags: Algorithms, Cluster analysis, Clustering, Computer science, CUDA, Hierarchical clustering, Machine learning, Nearest neighbour, nVidia, nVidia A100, nVidia DGX-1, Package

July 2, 2023 by hgpu

SYCL compute kernels for ExaHyPE

Chung Ming Loi, Tobias Weinzierl

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Tags: Benchmarking, Computer science, Data parallelism, nVidia, nVidia A100, Package, Programming techniques, SYCL

July 2, 2023 by hgpu

DGEMM on Integer Matrix Multiplication Unit

Hiroyuki Ootomo, Katsuhisa Ozaki, Rio Yokota

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Tags: Computer science, CUBLAS, CUDA, Deep learning, Linear Algebra, Machine learning, Matrix multiplication, nVidia, nVidia A100, nVidia Jetson AGX Orin, nVidia RTX 6000 Ada, nVidia Titan RTX, Package

June 25, 2023 by hgpu

GPU First – Execution of Legacy CPU Codes on GPUs

Shilei Tian, Tom Scogland, Barbara Chapman, Johannes Doerfert

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Tags: Benchmarking, Compilers, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia A100, OpenMP

June 25, 2023 by hgpu

Improving Performance of Iterative Applications through Interleaved Execution of Approximated CUDA Kernels

Gabriel Freytag

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Tags: Computer science, CUDA, Machine learning, Mixed precision, Neural networks, nVidia, nVidia A100, nVidia P100, Thesis

June 18, 2023 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

Static and Dynamic Analyses for Efficient GPU Execution

A portable C++ library for memory and compute abstraction on multi-core CPUs and GPUs

Fast Knowledge Graph Completion using Graphics Processing Units

qecGPT: decoding Quantum Error-correcting Codes with Generative Pre-trained Transformers

Maximizing Parallelism and GPU Utilization For Direct GPU Compilation Through Ensemble Execution

Safe, Seamless, And Scalable Integration Of Asynchronous GPU Streams In PETSc

Modeling Parallel Programs using Large Language Models

cuSLINK: Single-linkage Agglomerative Clustering on the GPU

SYCL compute kernels for ExaHyPE

DGEMM on Integer Matrix Multiplication Unit

GPU First – Execution of Legacy CPU Codes on GPUs

Improving Performance of Iterative Applications through Interleaved Execution of Approximated CUDA Kernels

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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