high performance computing on graphics processing units: hgpu.org

hgpu.org » CUDA

A simulation suite for lattice Boltzmann based real time CFD applications exploiting multi-level parallelism on modern multi-and many-core architectures

Markus Geveler, Dirk Ribbrock, Sven Mallach, Dominik Goddeke

View

Tags: CUDA, Fluid dynamics, Heterogeneous systems, Lattice Boltzmann model, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 285

December 21, 2011 by hgpu

Warp-Level Parallelism: Enabling Multiple Replications In Parallel on GPU

Jonathan Passerat-Palmbach, Jonathan Caux, Pridi Siregar, Claude Mazel, D.R.C.Hill

View

Download (PDF)

Tags: Benchmarking, Computer science, CUDA, nVidia, Stochastic simulation, Tesla C2050, Warp level parallelism

December 21, 2011 by hgpu

A Normalized Particle Swarm Optimization Algorithm to Price Complex Chooser Option and Accelerating its Performance with GPU

Bhanu Pratap Sharma

View

Download (PDF)

Tags: Algorithms, Computational finance, CUDA, Finance, nVidia, nVidia GeForce GTX 450, nVidia GeForce GTX 550 Ti, Thesis

December 21, 2011 by hgpu

Parallel Inference on Structured Data with CRFs on GPUs

Nico Piatkowski, Katharina Morik

View

Download (PDF)

Source codes

Tags: Algorithms, Computer science, CUDA, Databases, Graph theory, nVidia, Package, Tesla C2050

December 21, 2011 by hgpu

Generating SU(Nc) pure gauge lattice QCD configurations on GPUs with CUDA and OpenMP

Nuno Cardoso, Pedro Bicudo

View

Download (PDF)

Source codes

Tags: Computational Physics, CUDA, High Energy Physics - Lattice, Matrix multiplication, nVidia, nVidia GeForce GTX 295, nVidia GeForce GTX 580, Package, Physics, QCD

December 21, 2011 by hgpu

Performance and Quality of Random Number Generators

V. du Preez, M.G.B. Johnson, A. Leist, K.A. Hawick

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Monte Carlo simulation, Numerical simulation, nVidia, nVidia GeForce GTX 580, Pseudo-random number generators, Review

December 20, 2011 by hgpu

GPU Accelerated PK-means Algorithm for Gene Clustering

Wuchao Situ, Yau-King Lam, Yi Xiao, P.W.M. Tsang, Chi-Sing Leung

View

Download (PDF)

Tags: Algorithms, Biology, Clustering, CUDA, Genetics, nVidia, nVidia GeForce GTX 260

December 20, 2011 by hgpu

A Framework for Genetic Algorithms in Parallel Environments

Tomoyuki Hiroyasu, Ryosuke Yamanaka, Masato Yoshimi, Mitsunori Miki

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Genetic programming, nVidia, nVidia GeForce GTX 250

December 20, 2011 by hgpu

Parallel Contour-Buildup Algorithm for the Molecular Surface

Michael Krone, Sebastian Grottel, Thomas Ertl

View

Download (PDF)

Tags: Algorithms, Chemistry, CUDA, Molecular dynamics, Molecular surface, nVidia, nVidia GeForce GTX 580, Optimization, Pharmaceuticals, Visualization

December 20, 2011 by hgpu

Can CUDA be exposed through web services?

Massimo Becker

View

Download (PDF)

Tags: Computer science, CUDA, Networks, nVidia, OpenCL, Overview, Thesis

December 20, 2011 by hgpu

Parsing in Parallel on Multiple Cores and GPUs

Mark Johnson

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, nVidia, Tesla S2050

December 20, 2011 by hgpu

Floating-point Mixed-radix FFT Core Generation for FPGA and Comparison with GPU and CPU

Bo Duan, Wendi Wang, Xingjian Li, Chunming Zhang, Peiheng Zhang, Ninghui Sun

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, FFT, FPGA, nVidia, nVidia GeForce GTX 480, Optimization, Performance

December 20, 2011 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

* * *

high performance computing on graphics processing units: hgpu.org

A simulation suite for lattice Boltzmann based real time CFD applications exploiting multi-level parallelism on modern multi-and many-core architectures

Warp-Level Parallelism: Enabling Multiple Replications In Parallel on GPU

A Normalized Particle Swarm Optimization Algorithm to Price Complex Chooser Option and Accelerating its Performance with GPU

Parallel Inference on Structured Data with CRFs on GPUs

Generating SU(Nc) pure gauge lattice QCD configurations on GPUs with CUDA and OpenMP

Performance and Quality of Random Number Generators

GPU Accelerated PK-means Algorithm for Gene Clustering

A Framework for Genetic Algorithms in Parallel Environments

Parallel Contour-Buildup Algorithm for the Molecular Surface

Can CUDA be exposed through web services?

Parsing in Parallel on Multiple Cores and GPUs

Floating-point Mixed-radix FFT Core Generation for FPGA and Comparison with GPU and CPU

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

Most viewed papers (last 30 days)