high performance computing on graphics processing units: hgpu.org

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Programming with Explicit Dependencies. A Framework for Portable Parallel Programming

Eva Burrows

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 9600 GT, nVidia GeForce GTX 250, Programming techniques, Sorting, Thesis

October 17, 2011 by hgpu

A High Performance Parallel Sparse Linear Equation Solver Using CUDA

Andrew J. Martin

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Tags: Algorithms, Computer science, CUDA, Factorization, Linear Algebra, nVidia, nVidia GeForce GTX 570, Thesis

October 17, 2011 by hgpu

Asynchronous Communication for Finite-Difference Simulations on GPU Clusters using CUDA and MPI

D.P. Playne, K.A. Hawick

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Tags: Algorithms, Computer science, CUDA, GPU cluster, MPI, Numerical simulation, nVidia, nVidia GeForce GTX 470, nVidia GeForce GTX 480

October 16, 2011 by hgpu

An Auto-tuned Method for Solving Large Tridiagonal Systems on the GPU

Andrew Davidson, Yao Zhang, John D. Owens

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 280, nVidia GeForce GTX 470

October 16, 2011 by hgpu

GPU-to-CPU callbacks

Jeff A. Stuart, Michael Cox, John D. Owens

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 280, Performance, Programming techniques

October 16, 2011 by hgpu

Data Layout Pruning on GPU

Xinbiao Gan, Zhiying Wang, Li Shen, Qi Zhu

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Tags: Computer science, CUDA, Memory, Memory model, nVidia, nVidia GeForce GTX 280, Performance

October 16, 2011 by hgpu

Skeleton Programming for Heterogeneous GPU-based Systems

Usman Dastgeer

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, Performance, Tesla C1060, Thesis

October 16, 2011 by hgpu

Physis: An Implicitly Parallel Programming Model for Stencil Computations on Large-Scale GPU-Accelerated Supercomputers

Naoya Maruyama, Tatsuo Nomura, Kento Sato, Satoshi Matsuoka

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Source codes

Tags: Computer science, CUDA, MPI, nVidia, Optimization, Package, Performance, Programming Languages, Programming techniques, Tesla M2050

October 15, 2011 by hgpu

Operating Systems Challenges for GPU Resource Management

Shinpei Kato, Scott Brandt, Yutaka Ishikawa, Ragunathan (Raj) Rajkumar

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Tags: Computer science, CUDA, GPU cluster, nVidia, OpenCL, Operating systems, Virtualization

October 15, 2011 by hgpu

Towards Utilizing Remote GPUs for CUDA Program Execution

Xiaonan Ji, Spencer Davis, Erikson Hardesty, Xu Liang, Sabuj Saha, Hai Jiang

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Tags: Algorithms, Computer science, CUDA, GPU cluster, Networks, nVidia

October 15, 2011 by hgpu

Dymaxion: Optimizing Memory Access Patterns for Heterogeneous Systems

Shuai Che, Jeremy W. Sheaffer, Kevin Skadron

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Tags: Computer science, CUDA, Heterogeneous systems, Memory, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, Optimization, Performance

October 15, 2011 by hgpu

Effects of compression on data intensive algorithms

Ahmed Adnan Aqrawi

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Tags: Algorithms, Compression, Computer science, CUDA, Filtering, Image processing, nVidia, Seismology, Tesla C1060, Tesla C2050, Thesis

October 15, 2011 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

Applications

Programming with Explicit Dependencies. A Framework for Portable Parallel Programming

A High Performance Parallel Sparse Linear Equation Solver Using CUDA

Asynchronous Communication for Finite-Difference Simulations on GPU Clusters using CUDA and MPI

An Auto-tuned Method for Solving Large Tridiagonal Systems on the GPU

GPU-to-CPU callbacks

Data Layout Pruning on GPU

Skeleton Programming for Heterogeneous GPU-based Systems

Physis: An Implicitly Parallel Programming Model for Stencil Computations on Large-Scale GPU-Accelerated Supercomputers

Operating Systems Challenges for GPU Resource Management

Towards Utilizing Remote GPUs for CUDA Program Execution

Dymaxion: Optimizing Memory Access Patterns for Heterogeneous Systems

Effects of compression on data intensive algorithms

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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