high performance computing on graphics processing units: hgpu.org

Applications

hgpu.org » Applications » Computer science

Architecture-Aware Algorithms and Software for Peta and Exascale Computing

Jack J. Dongarra

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Tags: Algorithms, Computer science, Mixed precision, MPI, Review

November 30, 2011 by hgpu

Using CUDA for Exhaustive Password Recovery

Tore Kasper Frederiksen

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce 9400 M, nVidia GeForce GTX 480, Package, Security

November 30, 2011 by hgpu

Optimization of the Particle-based Volume Rendering for GPUs with Hiding Data Transfer Latency

Kyoko Nakao, Erika Matsui, Naoko Yoshii, Masami Takata, Kazuki Joe

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8400 GS, Optimization, Rendering, Tesla C1060

November 30, 2011 by hgpu

Performance and numerical accuracy evaluation of heterogeneous multicore systems for Krylov orthogonal basis computation

Jerome Dubois, Christophe Calvin, Serge Petiton

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Tags: Cell processor, Computer science, CUDA, Heterogeneous systems, Linear Algebra, nVidia, Performance, Tesla C1060

November 30, 2011 by hgpu

Applications Performance on GPGPUs with the Fermi Architecture

Ahmed Mohamed Hassan Abdalla

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 8400 GS, Performance, Tesla C2050, Tesla M1060, Thesis

November 29, 2011 by hgpu

Dynamic Task Parallelism with a GPU Work-Stealing Runtime System

Sanjay Chatterjee, Max Grossman, Alina Sbirlea, Vivek Sarkar

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Tags: Computer science, CUDA, Data parallelism, nVidia, Task scheduling, Tesla C2050

November 29, 2011 by hgpu

Directives Based Programming of GPU Accelerated Systems

Saif Mulla

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Tags: Computer science, CUDA, Molecular dynamics, nVidia, Programming techniques, Tesla C2050, Thesis

November 29, 2011 by hgpu

Enabling Efficient Online Profiling of Homogeneous and Heterogeneous Multicore Systems

Dan Upton

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Tesla C2050, Thesis

November 29, 2011 by hgpu

GPGPU Volume Classification using SimpleOpenCL

Oscar Amoros, Sergio Escalera, Anna Puig, Maria Salamo

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Tags: Computer science, nVidia, nVidia GeForce GTX 470, OpenCL, Package, Visualization

November 29, 2011 by hgpu

Solving Dense Generalized Eigenproblems on Multi-threaded Architectures

Jose I. Aliaga, Paolo Bientinesi, Davor Davidovic, Edoardo Di Napoli, Francisco D. Igual, Enrique S. Quintana-Orti

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Tags: Computer science, CUDA, Linear Algebra, Mathematical Software, Molecular dynamics, nVidia, Performance, Tesla C2050

November 29, 2011 by hgpu

On the numerical sensitivity of computer simulations on hybrid and parallel computing systems

Wenbin Li, Sven Simon, Steffen Kiess

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Tags: Algorithms, Computer science, CUDA, Floating point error, Hybrid computing, nVidia, nVidia GeForce GTX 480

November 28, 2011 by hgpu

Anytime Algorithms for GPU Architectures

Rahul Mangharam, Aminreza Abrahimi Saba

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Tags: Algorithms, Computer science, CUDA, nVidia, Vehicular traffic

November 28, 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

Architecture-Aware Algorithms and Software for Peta and Exascale Computing

Using CUDA for Exhaustive Password Recovery

Optimization of the Particle-based Volume Rendering for GPUs with Hiding Data Transfer Latency

Performance and numerical accuracy evaluation of heterogeneous multicore systems for Krylov orthogonal basis computation

Applications Performance on GPGPUs with the Fermi Architecture

Dynamic Task Parallelism with a GPU Work-Stealing Runtime System

Directives Based Programming of GPU Accelerated Systems

Enabling Efficient Online Profiling of Homogeneous and Heterogeneous Multicore Systems

GPGPU Volume Classification using SimpleOpenCL

Solving Dense Generalized Eigenproblems on Multi-threaded Architectures

On the numerical sensitivity of computer simulations on hybrid and parallel computing systems

Anytime Algorithms for GPU Architectures

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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