high performance computing on graphics processing units: hgpu.org

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GPU-based fast Monte Carlo simulation for radiotherapy dose calculation

Xun Jia, Xuejun Gu, Yan Jiang Graves, Michael Folkerts, Steve B. Jiang

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Tags: CUDA, Medical Physics, Monte Carlo simulation, nVidia, Physics, Radiotherapy, Tesla C2050

July 19, 2011 by hgpu

ForOpenCL: Transformations Exploiting Array Syntax in Fortran for Accelerator Programming

Matthew J. Sottile, Craig E Rasmussen, Wayne N. Weseloh, Robert W. Robey, Daniel Quinlan, Jeffrey Overbey

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Tags: Computer science, Fortran, nVidia, OpenCL, Package, Programming Languages, Tesla C2050

July 14, 2011 by hgpu

Accelerating the Nussinov RNA folding algorithm with CUDA/GPU

Dar-Jen Chang, C. Kimmer, Ming Ouyang

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Tags: Biology, CUDA, Macromolecule, nVidia, Tesla C1060, Tesla C2050, Tesla C870

July 12, 2011 by hgpu

Design and performance evaluation of a digital wideband receiver on a hybrid computing platform

Kiran George, Chien-In Henry Chen

Tags: FPGA, nVidia, Signal processing, Tesla C2050

July 12, 2011 by hgpu

Frameworks for GPU Accelerators: A comprehensive evaluation using 2D/3D image registration

Richard Membarth, Frank Hannig, Jurgen Teich, Mario Korner, Wieland Eckert

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Tags: CUDA, Image processing, Image registration, nVidia, nVidia Quadro FX 5800, OpenCL, Performance, RapidMind, Tesla C2050

July 9, 2011 by hgpu

A Hybrid Computing Platform Digital Wideband Receiver Design and Performance Measurement

K. George, C.-I. H. Chen

Tags: FPGA, nVidia, Performance, Signal processing, Tesla C2050

June 30, 2011 by hgpu

QCD simulations with staggered fermions on GPUs

C. Bonati, G. Cossu, M. D'Elia, P. Incardona

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Tags: ATI, ATI Radeon HD 5870, Computational Physics, CUDA, High Energy Physics - Lattice, nVidia, nVidia GeForce GT 430, OpenCL, Physics, Tesla C1060, Tesla C2050, Tesla C2070

June 29, 2011 by hgpu

Auto-tuning Dense Matrix Multiplication for GPGPU with Cache

Xiang Cui, Yifeng Chen, Changyou Zhang, Hong Mei

Tags: Computer science, CUDA, Linear Algebra, Matrix multiplication, nVidia, Tesla C2050

June 19, 2011 by hgpu

A comparative study of GPU programming models and architectures using neural networks

Vivek Pallipuram, Mohammad Bhuiyan, Melissa Smith

Tags: ATI, Computer science, CUDA, Neural networks, nVidia, OpenCL, Performance, Tesla C2050

June 14, 2011 by hgpu

A sparse octree gravitational N-body code that runs entirely on the GPU processor

Jeroen Bedorf, Evghenii Gaburov, Simon Portegies Zwart

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Tags: Astrophysics, CUDA, Gravitation, Instrumentation and Methods for Astrophysics, N-body simulation, nVidia, nVidia GeForce 8800 GTS, nVidia GeForce GTX 285, nVidia GeForce GTX 480, Package, Tesla C1060, Tesla C2050

June 14, 2011 by hgpu

Performance Analysis of the OP2 Framework on Many-core Architectures

M.B. Giles, G.R. Mudalige, Z. Sharif, G. Markall and P.H.J Kelly

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Tags: CUDA, Fluid dynamics, GPU cluster, nVidia, nVidia GeForce GTX 260, OpenMP, Package, Performance, Tesla C2050

June 5, 2011 by hgpu

Performance Acceleration of Kernel Polynomial Method Applying Graphics Processing Units

Shixun Zhang, Shinichi Yamagiwa, Masahiko Okumura, Seiji Yunoki

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Tags: Computational Physics, Condensed matter, CUDA, nVidia, Performance, Physics, Tesla C2050

May 30, 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

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high performance computing on graphics processing units: hgpu.org

GPU-based fast Monte Carlo simulation for radiotherapy dose calculation

ForOpenCL: Transformations Exploiting Array Syntax in Fortran for Accelerator Programming

Accelerating the Nussinov RNA folding algorithm with CUDA/GPU

Design and performance evaluation of a digital wideband receiver on a hybrid computing platform

Frameworks for GPU Accelerators: A comprehensive evaluation using 2D/3D image registration

A Hybrid Computing Platform Digital Wideband Receiver Design and Performance Measurement

QCD simulations with staggered fermions on GPUs

Auto-tuning Dense Matrix Multiplication for GPGPU with Cache

A comparative study of GPU programming models and architectures using neural networks

A sparse octree gravitational N-body code that runs entirely on the GPU processor

Performance Analysis of the OP2 Framework on Many-core Architectures

Performance Acceleration of Kernel Polynomial Method Applying Graphics Processing Units

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

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