high performance computing on graphics processing units: hgpu.org

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A Parallel Twig Join Algorithm for XML Processing using a GPGPU

Lila Shnaiderman, Oded Shmueli

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX 480

September 10, 2012 by hgpu

Architectural Analysis and Performance Characterization of NVIDIA GPUs using Microbenchmarking

Saktheesh Subramoniapillai Ajeetha

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 580, Performance, Tesla C2050, Thesis

September 6, 2012 by hgpu

High performance transcription factor-DNA docking with GPU computing

Jiadong Wu, Bo Hong, Takako Takeda, Jun-tao Guo

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Tags: Algorithms, Benchmarking, Bioinformatics, Biology, CUDA, Genetics, GPU cluster, nVidia, Tesla M2070

August 20, 2012 by hgpu

Performance of FORTRAN and C GPU Extensions for a Benchmark Suite of Fourier Pseudospectral Algorithms

B. Cloutier, B. K. Muite, P. Rigge

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Tags: Benchmarking, Computational Physics, CUDA, FFT, Fortran, Mathematical Software, nVidia, OpenACC, Package, Physics, Spectral methods, Tesla M2070

August 14, 2012 by hgpu

OP2: An Active Library Framework for Solving Unstructured Mesh-based Applications on Multi-Core and Many-Core Architectures

G.R. Mudalige, M.B. Giles, I. Reguly, C. Bertolli, P.H.J Kelly

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Tags: Benchmarking, Computer science, CUDA, GPU cluster, Heterogeneous systems, MPI, nVidia, Performance, Tesla C2050, Tesla C2070

August 11, 2012 by hgpu

Comparison of OpenMP & OpenCL Parallel Processing Technologies

Krishnahari Thouti, S.R.Sathe

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Tags: Benchmarking, Computer science, nVidia, nVidia Quadro FX 3800, OpenCL, OpenMP, Performance

August 6, 2012 by hgpu

Bacon: A GPU Programming System With Just in Time Specialization

Nat Tuck

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Tags: ATI, ATI Radeon HD 5830, Benchmarking, Computer science, Matrix multiplication, OpenCL

June 23, 2012 by hgpu

Branch and Data Herding: Reducing Control and Memory Divergence for Error-tolerant GPU Applications

John Sartori, Rakesh Kumar

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 480

June 17, 2012 by hgpu

Measuring the Impact of Configuration Parameters in CUDA Through Benchmarking

Yuri Torres, Arturo Gonzalez-Escribano, Diego R. Llanos

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 480

June 10, 2012 by hgpu

DMA-Assisted, Intranode Communication in GPU Accelerated Systems

Feng Ji, Ashwin M. Aji, James Dinan, Darius Buntinas, Pavan Balaji, Rajeev Thakur, Wu-Chun Feng, Xiaosong Ma

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Tags: Benchmarking, Computer science, CUDA, MPI, nVidia, Tesla M2070

June 6, 2012 by hgpu

A new parallelisation technique for heterogeneous CPUs

Youssef Omran Gdura

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Tags: Benchmarking, Code generation, Computer science, Heterogeneous systems, Programming techniques, Thesis

June 5, 2012 by hgpu

Towards Distributed Heterogenous High-Performance Computing with ViennaCL

Josef Weinbub, Karl Rupp, Siegfried Selberherr

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Tags: Benchmarking, Computer science, Distributed computing, MPI, nVidia, nVidia GeForce GTX 470, nVidia GeForce GTX 580, OpenCL, Package, Tesla C2050

June 1, 2012 by hgpu

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

hls4ml: Machine learning on FPGAs using HLS

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

Iris: First-Class Multi-GPU Programming Experience in Triton

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

An MLIR pipeline for offloading Fortran to FPGAs via OpenMP

See all packages

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high performance computing on graphics processing units: hgpu.org

A Parallel Twig Join Algorithm for XML Processing using a GPGPU

Architectural Analysis and Performance Characterization of NVIDIA GPUs using Microbenchmarking

High performance transcription factor-DNA docking with GPU computing

Performance of FORTRAN and C GPU Extensions for a Benchmark Suite of Fourier Pseudospectral Algorithms

OP2: An Active Library Framework for Solving Unstructured Mesh-based Applications on Multi-Core and Many-Core Architectures

Comparison of OpenMP & OpenCL Parallel Processing Technologies

Bacon: A GPU Programming System With Just in Time Specialization

Branch and Data Herding: Reducing Control and Memory Divergence for Error-tolerant GPU Applications

Measuring the Impact of Configuration Parameters in CUDA Through Benchmarking

DMA-Assisted, Intranode Communication in GPU Accelerated Systems

A new parallelisation technique for heterogeneous CPUs

Towards Distributed Heterogenous High-Performance Computing with ViennaCL

Recent source codes

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

hls4ml: Machine learning on FPGAs using HLS

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

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