high performance computing on graphics processing units: hgpu.org

Mark S. Friedrichs, Peter Eastman, Vishal Vaidyanathan, Mike Houston, Scott Legrand, Adam L. Beberg, Daniel L. Ensign, Christopher M. Bruns, Vijay S. Pande

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Source codes

Tags: ATI, ATI Radeon HD 4870, ATI Stream, Biology, Brook, Chemistry, Computational chemistry, Molecular dynamics, nVidia, nVidia GeForce GTX 280, Package, Solvent effects

October 30, 2010 by hgpu

Rise of the Graphics Processor

D. Blythe

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Tags: Computer science, Review

October 30, 2010 by hgpu

Solving quadratic assignment problems by genetic algorithms with GPU computation: a case study

Shigeyoshi Tsutsui, Noriyuki Fujimoto

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Tags: Algorithms, Artificial intelligence, Computer science, CUDA, nVidia, nVidia GeForce GTX 285, Programming techniques

October 30, 2010 by hgpu

Parallel Processing of Matrix Multiplication in a CPU and GPU Heterogeneous Environment

Satoshi Ohshima, Kenji Kise, Takahiro Katagiri, Toshitsugu Yuba

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Tags: Algorithms, Computer science, Linear Algebra, nVidia GeForce 6600 GT, nVidia GeForce 7800 GTX

October 29, 2010 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

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high performance computing on graphics processing units: hgpu.org

Applications

A hardware redundancy and recovery mechanism for reliable scientific computation on graphics processors

Performance enhancement of MAGIC FDTD-PIC plasma-wave simulations using GPU processing

Optimizing data intensive GPGPU computations for DNA sequence alignment

Accelerating molecular modeling applications with graphics processors

Mars: a MapReduce framework on graphics processors

Exploiting graphics processing units for computational biology and bioinformatics

High performance direct gravitational N-body simulations on graphics processing units II: An implementation in CUDA

A performance study of general-purpose applications on graphics processors using CUDA

Accelerating molecular dynamic simulation on graphics processing units

Rise of the Graphics Processor

Solving quadratic assignment problems by genetic algorithms with GPU computation: a case study

Parallel Processing of Matrix Multiplication in a CPU and GPU Heterogeneous Environment

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

Most viewed papers (last 30 days)