high performance computing on graphics processing units: hgpu.org

Programming

hgpu.org » Programming

A GPU accelerated storage system

Abdullah Gharaibeh, Samer A. Kiswany, Sathish Gopalakrishnan, Matei Ripeanu

View

Tags: Computer science, CUDA, nVidia, nVidia GeForce 8600, nVidia GeForce 8800 GT, nVidia GeForce 9800 GX2, Performance, Storage system

November 1, 2010 by hgpu

Fast parallel Particle-To-Grid interpolation for plasma PIC simulations on the GPU

G. Stantchev, W. Dorland, N. Gumerov

View

Download (PDF)

Tags: Computer science, CUDA, Numerical simulation, nVidia, Particle-in-cell methods, Plasma physics

November 1, 2010 by hgpu

CUDA-Lite: Reducing GPU programming complexity

Sain-zee Ueng, Melvin Lathara, Sara S. Baghsorkhi, Wen-mei W. Hwu

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, Programming techniques

November 1, 2010 by hgpu

Program optimization space pruning for a multithreaded gpu

Shane Ryoo, Christopher I. Rodrigues, Sam S. Stone, Sara S. Baghsorkhi, Sain Z. Ueng, John A. Stratton, Wen mei

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, Optimization, Performance, Programming techniques

November 1, 2010 by hgpu

High-throughput sequence alignment using Graphics Processing Units

Michael Schatz, Cole Trapnell, Arthur Delcher, Amitabh Varshney

View

Download (PDF)

Source codes

Tags: Bioinformatics, Biology, Computational biology, CUDA, nVidia, nVidia GeForce 8800 GTX, Package, Sequence alignment

November 1, 2010 by hgpu

Graphics Processing Units and High-Dimensional Optimization

Hua Zhou, Kenneth Lange, Marc A. Suchard

View

Download (PDF)

Tags: Block relaxation, CUBLAS, CUDA, EM and MM algorithms, Mathematics, Multidimensional scaling, Nonnegative matrix factorization, nVidia, nVidia GeForce GTX 280, PET scanning, Statistics

October 30, 2010 by hgpu

Quantum Chemistry on Graphical Processing Units. 1. Strategies for Two-Electron Integral Evaluation

Ivan S. Ufimtsev, Todd J. Martinez

View

Download (PDF)

Tags: Chemistry, Computational chemistry, CUDA, Molecular modeling, nVidia, nVidia GeForce 8800 GTX

October 30, 2010 by hgpu

A Memory Model for Scientific Algorithms on Graphics Processors

Naga K. Govindaraju, Scott Larsen, Jim Gray, Dinesh Manocha

View

Download (PDF)

Tags: Algorithms, Computer science, FFT, Memory model, nVidia, nVidia GeForce 6800 Ultra, nVidia GeForce 7800 GTX, nVidia GeForce 7900 GTX, Sorting

October 30, 2010 by hgpu

Optimizing data intensive GPGPU computations for DNA sequence alignment

Cole Trapnell, Michael C. Schatz

View

Download (PDF)

Source codes

Tags: Bioinformatics, Biology, Computational biology, CUDA, nVidia, nVidia GeForce 8800 GTX, Package, Sequence alignment, Short read mapping, Suffix trees

October 30, 2010 by hgpu

Accelerating molecular modeling applications with graphics processors

John E. Stone, James C. Phillips, Peter L. Freddolino, David J. Hardy, Leonardo G. Trabuco, Klaus Schulten

View

Download (PDF)

Tags: Chemistry, Computational chemistry, CUDA, Electrostatic potential, Molecular dynamics, Molecular modeling, Multilevel summation, nVidia, nVidia GeForce 8800 GTX

October 30, 2010 by hgpu

Mars: a MapReduce framework on graphics processors

Bingsheng He, Wenbin Fang, Qiong Luo, Naga K. Govindaraju, Tuyong Wang

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Data parallelism, MapReduce, nVidia, nVidia GeForce 8800 GTX, Package, Programming techniques, Web Analysis

October 30, 2010 by hgpu

Exploiting graphics processing units for computational biology and bioinformatics

Joshua L. Payne, Nicholas A. Sinnott-Armstrong, Jason H. Moore

View

Download (PDF)

Source codes

Tags: All-Pairs Distance, Bioinformatics, Biology, Computational biology, CUDA, nVidia, nVidia GeForce 9600 M, Package

October 30, 2010 by hgpu

BUDDY MLIR

Compiler Technologies in Deep Learning Co-Design: A Survey

pocl: Portable Computing Language (IWOCL23 release)

Implementation Techniques for SPMD Kernels on CPUs

Genomics-GPU: A Benchmark Suite for GPU-accelerated Genome Analysis

Dragon-Alpha&cu32: A Java-based Tensor Computing Framework With its High-Performance CUDA Library

Beehive SPIR-V Toolkit

Experiences in Building a Composable and Functional API for Runtime SPIR-V Code Generation

TorchBench: a collection of open source benchmarks used to evaluate PyTorch performance

TorchBench: Benchmarking PyTorch with High API Surface Coverage

Descend: an attempt at safe imperative GPU programming

Descend: A Safe GPU Systems Programming Language

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Programming

A GPU accelerated storage system

Fast parallel Particle-To-Grid interpolation for plasma PIC simulations on the GPU

CUDA-Lite: Reducing GPU programming complexity

Program optimization space pruning for a multithreaded gpu

High-throughput sequence alignment using Graphics Processing Units

Graphics Processing Units and High-Dimensional Optimization

Quantum Chemistry on Graphical Processing Units. 1. Strategies for Two-Electron Integral Evaluation

A Memory Model for Scientific Algorithms on Graphics Processors

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

Recent source codes

BUDDY MLIR

pocl: Portable Computing Language (IWOCL23 release)

Genomics-GPU: A Benchmark Suite for GPU-accelerated Genome Analysis

AutoDock for GPUs and other accelerators

spotPython: Sequential Parameter Optimization in Python

Test benchmarks for CELERITY

Dragon-Alpha&cu32: A Java-based Tensor Computing Framework With its High-Performance CUDA Library

Beehive SPIR-V Toolkit

TorchBench: a collection of open source benchmarks used to evaluate PyTorch performance

Descend: an attempt at safe imperative GPU programming

Most viewed papers (last 30 days)