high performance computing on graphics processing units: hgpu.org

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Energy consumption of Graphic Processing Units with respect to automotive use-cases

L. Stolz, H. Endt, M. Vaaraniemi, D. Zehe, W. Stechele

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Tags: Computational Engineering, Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce 8400 GS, nVidia GeForce 9400 GT, nVidia GeForce GT 220, OpenCL

April 3, 2011 by hgpu

Throughput-Effective On-Chip Networks for Manycore Accelerators

Ali Bakhoda, John Kim, Tor M. Aamodt

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Performance

April 2, 2011 by hgpu

Parallel implementation of the Finite-Difference Time-Domain method in Open Computing Language

T.P. Stefanski, S. Benkler, N. Chavannes, N. Kuster

Tags: CUDA, Electrodynamics, FDTD, Finite-difference time-domain, nVidia, OpenCL, Performance

April 2, 2011 by hgpu

Speeding-up Pearson Correlation Coefficient calculation on graphical processing units

K.Berker Logoglu, Tugrul K. Ates

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Tags: ATI, ATI Radeon HD 5870, CUDA, nVidia, nVidia GeForce 8500 GT, nVidia GeForce 8600 M GT, nVidia GeForce GTX 285, OpenCL, Search, Signal processing

April 2, 2011 by hgpu

A characterization and analysis of PTX kernels

Andrew Kerr, Gregory Diamos, Sudhakar Yalamanchili

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Performance, PTX

April 2, 2011 by hgpu

Parallel computing with CUDA

M. Garland

Tags: Computer science, CUDA, nVidia, Programming techniques

April 2, 2011 by hgpu

Non-intrusive Performance Analysis of Parallel Hardware Accelerated Applications on Hybrid Architectures

Robert Dietrich, Thomas Ilsche, Guido Juckeland

Tags: Computer science, CUDA, nVidia, OpenCL, Performance

April 1, 2011 by hgpu

Message Passing Interface support for the runtime adaptive multi-processor system-on-chip RAMPSoC

D. Gohringer, M. Hubner, L. Hugot-Derville, J. Becker

Tags: Computer science, CUDA, MPI, nVidia, OpenCL, OpenGL

April 1, 2011 by hgpu

Higher order FEM numerical integration on GPUs with OpenCL

Przemyslaw Plaszewski, Krzysztof Banas, Pawel Maciol

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Tags: Computer science, CUDA, FEM, Finite element method, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 275, nVidia GeForce GTX 285, OpenCL

April 1, 2011 by hgpu

Implementation of Smith-Waterman Algorithm in OpenCL for GPUs

Dzmitry Razmyslovich, Guillermo Marcus, Markus Gipp, Marc Zapatka, Andreas Szillus

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Tags: Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 480, OpenCL, Poster, Sequence alignment, Smith-Waterman algorithm

March 31, 2011 by hgpu

CUDACL: A tool for CUDA and OpenCL programmers

Ferosh Jacob, David Whittaker, Sagar Thapaliya, Purushotham Bangalore, Marjan Mernik, Jeff Gray

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Tags: Computer science, CUDA, High-level Languages, Java, nVidia, nVidia GeForce GTX 260, OpenCL

March 31, 2011 by hgpu

OpenCL – An effective programming model for data parallel computations at the Cell Broadband Engine

J. Breitbart, C. Fohry

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Tags: Cell processor, Computer science, CUDA, Memory model, nVidia, nVidia GeForce GTX 280, OpenCL

March 31, 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

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high performance computing on graphics processing units: hgpu.org

Energy consumption of Graphic Processing Units with respect to automotive use-cases

Throughput-Effective On-Chip Networks for Manycore Accelerators

Parallel implementation of the Finite-Difference Time-Domain method in Open Computing Language

Speeding-up Pearson Correlation Coefficient calculation on graphical processing units

A characterization and analysis of PTX kernels

Parallel computing with CUDA

Non-intrusive Performance Analysis of Parallel Hardware Accelerated Applications on Hybrid Architectures

Message Passing Interface support for the runtime adaptive multi-processor system-on-chip RAMPSoC

Higher order FEM numerical integration on GPUs with OpenCL

Implementation of Smith-Waterman Algorithm in OpenCL for GPUs

CUDACL: A tool for CUDA and OpenCL programmers

OpenCL – An effective programming model for data parallel computations at the Cell Broadband Engine

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)