high performance computing on graphics processing units: hgpu.org

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A Performance Portable Matrix Free Dense MTTKRP in GenTen

Gabriel Kosmacher, Eric T. Phipps, Sivasankaran Rajamanickam

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Tags: Algorithms, Computer science, CUDA, Kokkos, Mathematical Software, nVidia, nVidia H100, OpenMP, Package

October 19, 2025 by hgpu

exa-AMD: An Exascale-Ready Framework for Accelerating the Discovery and Design of Functional Materials

Weiyi Xiaa, Maxim Moraru, Ying Wai Li, Cai-Zhuang Wang

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Tags: Algorithms, Benchmarking, Condensed matter, Materials Science, Package, Physics

October 5, 2025 by hgpu

HPC acceleration of large (min, +) matrix products to compute domination-type parameters in graphs

E.M. Garzón, J.A. Martínez, J.J. Moreno, M.L. Puertas

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Tags: Algorithms, Computer science, CUDA, Graph theory, HPC, nVidia, OpenMP, Package, Tesla V100

September 29, 2024 by hgpu

Supercharging Federated Learning with Flower and NVIDIA FLARE

Holger R. Roth, Daniel J. Beutel, Yan Cheng, Javier Fernandez Marques, Heng Pan, Chester Chen, Zhihong Zhang, Yuhong Wen, Sean Yang, Isaac (Te-Chung)Yang, Yuan-Ting Hsieh, Ziyue Xu, Daguang Xu, Nicholas D. Lane, Andrew Feng

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Tags: AI, Algorithms, Computer science, nVidia, Package, Security

July 7, 2024 by hgpu

GPU Implementations for Midsize Integer Addition and Multiplication

Cosmin E. Oancea, Stephen M. Watt

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia A100, Performance, Programming Languages

May 26, 2024 by hgpu

Fast Truncated SVD of Sparse and Dense Matrices on Graphics Processors

Andres E. Tomas, Enrique S. Quintana-Orti, Hartwig Anzt

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, nVidia, nVidia A100

March 18, 2024 by hgpu

A Review of the Parallelization Strategies for Iterative Algorithms

Xingxing Zhou, Ming Ling, Shidi Tang, YanXiang Zhu

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Tags: Algorithms, Computational Complexity, Computer science, CUDA, Data parallelism, OpenCL

December 3, 2023 by hgpu

RDMA-Based Algorithms for Sparse Matrix Multiplication on GPUs

Benjamin Brock, Aydın Buluç, Katherine Yelick

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Tags: Algorithms, Computer science, CUDA, Matrix multiplication, MPI, nVidia, nVidia DGX-2, Sparse matrix, Tesla V100

December 3, 2023 by hgpu

APACE: AlphaFold2 and advanced computing as a service for accelerated discovery in biophysics

Hyun Park, Parth Patel, Roland Haas, E. A. Huerta

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Tags: AI, Algorithms, Biology, Biomolecules, Biophysics, nVidia, nVidia A100, nVidia A40, Package

August 20, 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

Tile-based Lightweight Integer Compression in GPU

Anil Shanbhag, Bobbi W. Yogatama, Xiangyao Yu, Samuel Madden

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Tags: Algorithms, Compression, Computer science, CUDA, nVidia, nVidia V100, Package

July 16, 2023 by hgpu

Matrix Multiplication Using Only Addition

Daniel Cussen, Jeffrey D. Ullman

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Tags: Algorithms, Computer science, Matrix multiplication

July 9, 2023 by hgpu

AutoDock-GPU: AutoDock for GPUs and other accelerators

Architecting Tensor Core-Based Reductions for Irregular Molecular Docking Kernels

NCCLX: collective communication framework

Collective Communication for 100k+ GPUs

Tutoring LLM into a Better CUDA Optimizer

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Genten: Software for Generalized Tensor Decompositions by Sandia National Laboratories

A Performance Portable Matrix Free Dense MTTKRP in GenTen

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

Accelerating cosmological simulations on GPUs: a portable approach using OpenMP

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

ConCuR: Conciseness Makes State-of-the-Art Kernel Generation

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high performance computing on graphics processing units: hgpu.org

A Performance Portable Matrix Free Dense MTTKRP in GenTen

exa-AMD: An Exascale-Ready Framework for Accelerating the Discovery and Design of Functional Materials

HPC acceleration of large (min, +) matrix products to compute domination-type parameters in graphs

Supercharging Federated Learning with Flower and NVIDIA FLARE

GPU Implementations for Midsize Integer Addition and Multiplication

Fast Truncated SVD of Sparse and Dense Matrices on Graphics Processors

A Review of the Parallelization Strategies for Iterative Algorithms

RDMA-Based Algorithms for Sparse Matrix Multiplication on GPUs

APACE: AlphaFold2 and advanced computing as a service for accelerated discovery in biophysics

Fast Knowledge Graph Completion using Graphics Processing Units

Tile-based Lightweight Integer Compression in GPU

Matrix Multiplication Using Only Addition

Recent source codes

AutoDock-GPU: AutoDock for GPUs and other accelerators

NCCLX: collective communication framework

Tutoring LLM into a Better CUDA Optimizer

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Kernel Library for LLM Serving

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Genten: Software for Generalized Tensor Decompositions by Sandia National Laboratories

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

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