high performance computing on graphics processing units: hgpu.org

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A control-structure splitting optimization for GPGPU

Snaider Carrillo, Jakob Siegel, Xiaoming Li

Tags: Algorithm optimization, Computer science, CUDA, nVidia, Programming techniques

November 1, 2010 by hgpu

A GPU accelerated storage system

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

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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

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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

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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

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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

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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

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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

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

October 30, 2010 by hgpu

Optimizing data intensive GPGPU computations for DNA sequence alignment

Cole Trapnell, Michael C. Schatz

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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

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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

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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

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

October 30, 2010 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

CuPBoP: Making CUDA a Portable Language

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

See all packages

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high performance computing on graphics processing units: hgpu.org

A control-structure splitting optimization for GPGPU

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

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

CuPBoP-AMD: Extending CUDA to AMD Platforms

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

ROCm's implementation of Gromacs

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

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