high performance computing on graphics processing units: hgpu.org

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hgpu.org » Applications » Computer science

Improving the Performance of the Linear Systems Solvers Using CUDA

Bogdan Oancea, Tudorel Andrei, Raluca Mariana Dragoescu

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Tags: Computer science, CUDA, Factorization, Linear Algebra, nVidia, nVidia GeForce GTX 280

November 24, 2012 by hgpu

Enhancing and Porting the HPC-Lab Snow Simulator to OpenCL on Mobile Platforms

Frederik Magnus Johansen Vestre

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Tags: ATI, ATI Radeon HD 5870, ATI Radeon HD 6490 M, Computer science, nVidia, nVidia GeForce GTX 560 Ti, OpenCL, Sorting, Thesis

November 24, 2012 by hgpu

GPU Isosurface Raycasting of FCC Datasets

Minho Kim

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Tags: Algorithms, Computer science, GLSL, nVidia GeForce GTX 460, OpenCL, OpenGL, Rendering

November 24, 2012 by hgpu

Auto-tuning on the macro scale: high level algorithmic auto-tuning for scientific applications

Cy P Chan

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Tags: Algorithms, Code generation, Computer science, nVidia, nVidia GeForce GTX 280, Optimization, Thesis

November 23, 2012 by hgpu

Evaluation of Two Parallel Finite Element Implementations of the Time-Dependent Advection Diffusion Problem: GPU versus Cluster Considering Time and Energy Consumption

Alberto F. De Souza, Lucas Veronese, Leonardo M. Lima, Claudine Badue, Lucia Catabriga

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Tags: Computer science, CUDA, Finite element method, nVidia, Tesla C1060

November 23, 2012 by hgpu

Scalable Multi-GPU 3-D FFT for TSUBAME 2.0 Supercomputer

Akira Nukada, Kento Sato, Satoshi Matsuoka

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Tags: Computer science, CUDA, FFT, MPI, nVidia, Tesla M2090

November 23, 2012 by hgpu

CoreTSAR: Task Scheduling for Accelerator-aware Runtimes

Thomas R. W. Scogland, Wu-chun Feng, Barry Rountree, Bronis R. de Supinski

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, Task scheduling, Tesla C2070

November 22, 2012 by hgpu

Automatic generation of software pipelines for heterogeneous parallel systems

Jacques A. Pienaar, Srimat Chakradhar, Anand Raghunathan

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, Tesla C1060

November 22, 2012 by hgpu

List Mode PET reconstruction

Balazs Kovacs

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Tags: Computer science, CUDA, nVidia, PET, Tesla C2050

November 22, 2012 by hgpu

Early evaluation of directive-based GPU programming models for productive exascale computing

Seyong Lee, Jeffrey S. Vetter

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Tags: Computer science, CUDA, nVidia, OpenACC, Tesla M2090

November 21, 2012 by hgpu

High-Performance General Solver for Extremely Large-Scale Semidefinite Programming Problems

Katsuki Fujisawa, Hitoshi Sato, Satoshi Matsuoka, Toshio Endo, Makoto Yamashita, Maho Nakata

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Tags: Algorithms, Computer science, CUDA, Factorization, nVidia, Optimization, Tesla M2050

November 21, 2012 by hgpu

ValuePack: value-based scheduling framework for CPU-GPU clusters

Vignesh T. Ravi, Michela Becchi, Gagan Agrawal, Srimat Chakradhar

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Tags: Cloud, Computer science, CUDA, GPU cluster, Heterogeneous systems, nVidia, Tesla C2050

November 21, 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

Applications

Improving the Performance of the Linear Systems Solvers Using CUDA

Enhancing and Porting the HPC-Lab Snow Simulator to OpenCL on Mobile Platforms

GPU Isosurface Raycasting of FCC Datasets

Auto-tuning on the macro scale: high level algorithmic auto-tuning for scientific applications

Evaluation of Two Parallel Finite Element Implementations of the Time-Dependent Advection Diffusion Problem: GPU versus Cluster Considering Time and Energy Consumption

Scalable Multi-GPU 3-D FFT for TSUBAME 2.0 Supercomputer

CoreTSAR: Task Scheduling for Accelerator-aware Runtimes

Automatic generation of software pipelines for heterogeneous parallel systems

List Mode PET reconstruction

Early evaluation of directive-based GPU programming models for productive exascale computing

High-Performance General Solver for Extremely Large-Scale Semidefinite Programming Problems

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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