high performance computing on graphics processing units: hgpu.org

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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

Out-of-the-box library support for DBMS operations on GPUs

Harish Kumar Harihara Subramanian, Bala Gurumurthy, Gabriel Campero Durand, David Broneske, Gunter Saake

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Tags: Computer science, Databases, Hashing, Heterogeneous systems, nVidia, nVidia GeForce RTX 2080 Ti, nVidia V100, OpenCL, Package, Performance

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

Managing, Profiling, and Optimizing Heterogeneous GPU Workloads

James Gleeson

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Tags: Computer science, CUDA, Heterogeneous systems, Neural networks, nVidia, nVidia GeForce GTX 480, nVidia GeForce RTX 2080 Ti, nVidia Grid K1, Optimization, Package, Performance, Tesla V100, Thesis, Virtualization

July 2, 2023 by hgpu

Deep Language Models for Software Testing and Optimisation

Foivos Tsimpourlas

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Tags: Benchmarking, Computer science, Deep learning, nVidia, nVidia GeForce GTX 1080, nVidia GeForce Titan X, OpenCL, Package, Software Engineering, Thesis

June 25, 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

Compilation and Design Space Exploration of Dataflow Programs for Heterogeneous CPU-GPU Platforms

Aurélien François Gilbert Bloch

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Tags: Compilers, Computer science, CUDA, Design space exploration, DSP, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GTX 1660, nVidia GeForce RTX 3080 Ti, Package, Thesis

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

ACC Saturator: Automatic Kernel Optimization for Directive-Based GPU Code

Kazuaki Matsumura, Simon Garcia De Gonzalo, Antonio J. Peña

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Tags: Algorithms, Benchmarking, Code generation, Compilers, Computer science, nVidia, OpenACC, OpenMP, Tesla A100

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

Reducing branch divergence to speed up parallel execution of unit testing on GPUs

Taghreed Bagies, Wei Le, Jeremy Sheafer, Ali Jannesari

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

June 18, 2023 by hgpu

Efficient GPU implementation of a class of array permutations

Mathis Bouverot-Dupuis, Mary Sheeran

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Tags: Algorithms, AMD Radeon Instinct MI100, ATI, Computer science, CUDA, Haskell, nVidia, nVidia GeForce RTX 4090, Package

June 18, 2023 by hgpu

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

Nsight Python: a Python kernel profiling interface based on NVIDIA Nsight Tools

Nsight Python: A Python-First Profiling Toolkit for Seamless GPU Kernel Analysis (Tool)

Awesome LLM-Driven Kernel Generation

Towards Automated Kernel Generation in the Era of LLMs

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high performance computing on graphics processing units: hgpu.org

cuSLINK: Single-linkage Agglomerative Clustering on the GPU

Out-of-the-box library support for DBMS operations on GPUs

SYCL compute kernels for ExaHyPE

Managing, Profiling, and Optimizing Heterogeneous GPU Workloads

Deep Language Models for Software Testing and Optimisation

DGEMM on Integer Matrix Multiplication Unit

Compilation and Design Space Exploration of Dataflow Programs for Heterogeneous CPU-GPU Platforms

GPU First – Execution of Legacy CPU Codes on GPUs

ACC Saturator: Automatic Kernel Optimization for Directive-Based GPU Code

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

Reducing branch divergence to speed up parallel execution of unit testing on GPUs

Efficient GPU implementation of a class of array permutations

Recent source codes

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

Nsight Python: a Python kernel profiling interface based on NVIDIA Nsight Tools

Awesome LLM-Driven Kernel Generation

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