high performance computing on graphics processing units: hgpu.org

hgpu.org » CUDA

GPU Acceleration of Runge-Kutta Integrators

Lawrence Murray

View

Download (PDF)

Tags: Algorithms, CUDA, Mathematics, nVidia, ODEs, Ordinary differential equations

February 22, 2011 by hgpu

Software-Based ECC for GPUs

Naoya Maruyama, Akira Nukada, Satoshi Matsuoka

View

Download (PDF)

Tags: Computer science, CUDA, Error recovery, FFT, N-body simulation, nVidia, nVidia GeForce 8800 GTS, nVidia GeForce GTX 285, Tesla S1070

February 21, 2011 by hgpu

An Automated Approach for SIMD Kernel Generation for GPU based Software Acceleration

Kanupriya Gulati and Sunil P Khatri

View

Download (PDF)

Tags: Code generation, Compilers, Computer science, CUDA, nVidia, nVidia GeForce GTX 280

February 21, 2011 by hgpu

Assembling large mosaics of electron microscope images using GPU

Kannan Umadevi Venkataraju, Mark Kim, Dan Gerszewski, James R. Anderson, Mary Hall

View

Download (PDF)

Tags: CUDA, Electron microscopy, Image processing, nVidia, nVidia GeForce GTX 280

February 21, 2011 by hgpu

Accelerating the Stochastic Simulation Algorithm

David Jenkins, Gregory D. Peterson

View

Download (PDF)

Tags: Biology, CUDA, Monte Carlo simulation, MPI, nVidia, Stochastic simulation, Tesla C870

February 21, 2011 by hgpu

Acceleration of Binomial Options Pricing via Parallelizing along time-axis on a GPU

Narayan Ganesan, Roger D. Chamberlain, Jeremy Buhler

View

Download (PDF)

Tags: CUDA, Finance, Monte Carlo simulation, nVidia, nVidia GeForce 8600 GT

February 21, 2011 by hgpu

A massively parallel framework using P systems and GPUs

Jose M. Cecilia, Gines D. Guerrero, Jose M. Garcia, Miguel A. Martinez-del-Amor, Ignacio Perez-Hurtado, Mario J. Perez-Jimenez

View

Download (PDF)

Tags: Biology, Computer science, CUDA, nVidia, P systems, Tesla C1060

February 21, 2011 by hgpu

GPU Acceleration of Equations Assembly in Finite Elements Method – Preliminary Results

Jiri Filipovic, Igor Peterlik, Jan Fousek

View

Download (PDF)

Tags: Computer science, CUDA, Finite element method, nVidia, nVidia GeForce GTX 280, Partial differential equations, PDEs

February 21, 2011 by hgpu

Data parallel loop statement extension to CUDA: GpuC

Zeki Bozkus, Rajeev Thakur, William Gropp, Ewing Lusk

View

Download (PDF)

Tags: Compilers, Computer science, CUDA, High-level Languages, nVidia, OpenMP, Optimization

February 21, 2011 by hgpu

Final Project Implementing Extremely Randomized Trees in CUDA

Michael A. Geary, HyoukJoong Lee, Davide Signorelli, Jordan Vaughan

View

Download (PDF)

Tags: Algorithms, Artificial intelligence, Computer science, CUDA, nVidia, OpenMP, Tesla C1060

February 20, 2011 by hgpu

Design Space Exploration for GPU-Based Architecture

Zhenyu Ye

View

Download (PDF)

Tags: Computer science, CUDA, Factorization, Hardware Architecture, Memory model, nVidia, nVidia GeForce 8800 GTX, Review, Thesis

February 20, 2011 by hgpu

Fast Exact String Matching on the GPU

Michael C. Schatz, Cole Trapnell

View

Download (PDF)

Source codes

Tags: Algorithms, Computational biology, Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, Package, Search, String matching, Suffix trees

February 20, 2011 by hgpu

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

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

GPU Acceleration of Runge-Kutta Integrators

Software-Based ECC for GPUs

An Automated Approach for SIMD Kernel Generation for GPU based Software Acceleration

Assembling large mosaics of electron microscope images using GPU

Accelerating the Stochastic Simulation Algorithm

Acceleration of Binomial Options Pricing via Parallelizing along time-axis on a GPU

A massively parallel framework using P systems and GPUs

GPU Acceleration of Equations Assembly in Finite Elements Method – Preliminary Results

Data parallel loop statement extension to CUDA: GpuC

Final Project Implementing Extremely Randomized Trees in CUDA

Design Space Exploration for GPU-Based Architecture

Fast Exact String Matching on the GPU

Recent source codes

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

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

Most viewed papers (last 30 days)