high performance computing on graphics processing units: hgpu.org

hgpu.org » CUDA

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

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

Robert G. Belleman, Jeroen Bedorf, Simon F. Portegies Zwart

View

Download (PDF)

Tags: Astrophysics, CUDA, Gravitation, N-body simulation, nVidia, nVidia GeForce 8800 GTX, Stellar dynamics

October 30, 2010 by hgpu

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

S. Che, M. Boyer, J. Meng, D. Tarjan, J. Sheaffer, K. Skadron

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 260, OpenMP, Package, Performance

October 30, 2010 by hgpu

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

Shigeyoshi Tsutsui, Noriyuki Fujimoto

View

Download (PDF)

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

October 30, 2010 by hgpu

Ultrasound goes GPU: real-time simulation using CUDA

Tobias Reichl, Josh Passenger, Oscar Acosta, Olivier Salvado

View

Download (PDF)

Tags: CUDA, Image-guided therapy, Medicine, nVidia, nVidia GeForce 8800 GTX, Surgical simulation, Ultrasound, Visualization

October 29, 2010 by hgpu

High-order finite-element seismic wave propagation modeling with MPI on a large GPU cluster

Dimitri Komatitsch, Gordon Erlebacher, Dominik Goddeke, David Michea

View

Download (PDF)

Tags: Computational Physics, CUDA, Finite element method, Geoscience, GPU cluster, MPI, Numerical simulation, nVidia, Physics, Seismic modeling, Spectral elements, Tesla S1060, Tesla S1070

October 29, 2010 by hgpu

Accelerating Image Retrieval Using Factorial Correspondence Analysis on GPU

Nguyen-Khang Pham, Annie Morin, Patrick Gros

Tags: CUBLAS, CUDA, Factorial correspondence analysis, Image processing, nVidia, SIFT

October 29, 2010 by hgpu

CUSA and CUDE: GPU-accelerated methods for estimating solvent accessible surface area and desolvation

David Dynerman, Erick Butzlaff, Julie C. Mitchell

View

Download (PDF)

Tags: Bioinformatics, Biology, Biophysics, Chemistry, Computational biology, CUDA, Molecular dynamics, nVidia, nVidia GeForce GTX 280, Solvent effects

October 29, 2010 by hgpu

An analytical model for a GPU architecture with memory-level and thread-level parallelism awareness

Sunpyo Hong, Hyesoon Kim

View

Download (PDF)

Tags: Analytical model, Computer science, CUDA, Memory level parallelism, nVidia, nVidia GeForce 8800 GT, nVidia GeForce 8800 GTX, nVidia Quadro FX 5600, Warp level parallelism

October 29, 2010 by hgpu

GPU acceleration of cutoff pair potentials for molecular modeling applications

Christopher I. Rodrigues, David J. Hardy, John E. Stone, Klaus Schulten, Wen Mei

View

Download (PDF)

Tags: Algorithms, Biology, Chemistry, CUDA, Medicine, Molecular dynamics, nVidia, Tesla C870

October 28, 2010 by hgpu

AUTO-GC: Automatic translation of data mining applications to GPU clusters

Wenjing Ma, Gagan Agrawal

Tags: Code generation, Computer science, CUDA, GPU cluster, nVidia

October 28, 2010 by hgpu

CFAL-bench

Comparing Parallel Functional Array Languages: Programming and Performance

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

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

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

Ultrasound goes GPU: real-time simulation using CUDA

High-order finite-element seismic wave propagation modeling with MPI on a large GPU cluster

Accelerating Image Retrieval Using Factorial Correspondence Analysis on GPU

CUSA and CUDE: GPU-accelerated methods for estimating solvent accessible surface area and desolvation

An analytical model for a GPU architecture with memory-level and thread-level parallelism awareness

GPU acceleration of cutoff pair potentials for molecular modeling applications

AUTO-GC: Automatic translation of data mining applications to GPU clusters

Recent source codes

CFAL-bench

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

Most viewed papers (last 30 days)