high performance computing on graphics processing units: hgpu.org

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Enabling multiple accelerator acceleration for Java/OpenMP

Ronald Veldema, Thorsten Blass, Michael Philippsen

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Tags: Benchmarking, Computer science, CUDA, Java, nVidia, OpenCL, OpenMP, OpenMPI, Tesla M1060

September 9, 2011 by hgpu

Automatic OpenCL Device Characterization: Guiding Optimized Kernel Design

Peter Thoman, Klaus Kofler, Heiko Studt, John Thomson, Thomas Fahringer

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Tags: Algorithms, ATI, ATI Radeon HD 5870, Benchmarking, Cell processor, Computer science, nVidia, nVidia GeForce GTX 275, nVidia GeForce GTX 460, OpenCL, Optimization, Tesla C2050

September 8, 2011 by hgpu

Chameleon: Virtualizing idle acceleration cores of a heterogeneous multicore processor for caching and prefetching

Dong Hyuk Woo, Joshua B. Fryman, Allan D. Knies, Hsien-Hsin S. Lee

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Tags: Benchmarking, Computer science, Heterogeneous systems, Prefetch

September 7, 2011 by hgpu

Exploring the tradeoffs between programmability and efficiency in data-parallel accelerators

Yunsup Lee, Rimas Avizienis, Alex Bishara, Richard Xia, Derek Lockhart, Christopher Batten, Krste Asanovic

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Tags: Benchmarking, Computer science, Hardware Architecture

September 7, 2011 by hgpu

Using Graphics Processing Units for Logic Simulation of Electronic Designs

Alper Sen, Baris Aksanli, Murat Bozkurt

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Electronic design automation, nVidia, nVidia Quadro FX 3800, Optimization

September 2, 2011 by hgpu

GPU Versus FPGA for High Productivity Computing

David H. Jones, Adam Powell, Christos-Savvas Bouganis, Peter Y. K. Cheung

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Tags: Benchmarking, Computer science, CUDA, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GTX 285

September 2, 2011 by hgpu

Parallel hierarchical cross entropy optimization for on-chip decap budgeting

Xueqian Zhao, Yonghe Guo, Zhuo Feng, Shiyan Hu

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Tags: Algorithms, Benchmarking, CAD, Computer science, CUDA, Nonlinear optimization, nVidia, nVidia GeForce GTX 285, Optimization, Probability

September 1, 2011 by hgpu

Bounding the effect of partition camping in GPU kernels

Ashwin M. Aji, Mayank Daga, Wu-chun Feng

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

August 23, 2011 by hgpu

Benchmarking and modelling of POWER7, Westmere, BG/P, and GPUs: an industry case study

J. A. Herdman, W. P. Gaudin, D. Turland, S. D. Hammond

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Tags: ATI, ATI Radeon HD 5870, Benchmarking, Computer science, MPI, nVidia, nVidia GeForce GTX 285, OpenCL, Performance, Tesla C1060

August 19, 2011 by hgpu

Energy-aware metrics for benchmarking heterogeneous systems

Simon McIntosh-Smith, Terry Wilson, Jon Crisp, Amaurys Avila Ibarra, Richard B. Sessions

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Tags: Benchmarking, Computer science, Energy-efficient computing, Heterogeneous systems, Molecular modeling, nVidia, nVidia GeForce GTX 280, OpenCL, Tesla C2050

August 18, 2011 by hgpu

THOR: A Transparent Heterogeneous Open Resource framework

J.L. Vazquez-Poletti, J. Perhac, J. Ryan, A.C. Elster

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Tags: Algorithms, Benchmarking, Cloud, Computer science, GPU cluster, Heterogeneous systems, nVidia, nVidia GeForce 9800 GX2, nVidia GeForce GTX 470, OpenCL, Tesla S1070

August 10, 2011 by hgpu

Performance evaluation and optimization of random memory access on multicores with high productivity

Vaibhav Saxena, Yogish Sabharwal, Pramod Bhatotia

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Tags: Benchmarking, Computer science, CUDA, Memory, nVidia, nVidia Quadro FX 4600, Optimization

August 10, 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

Enabling multiple accelerator acceleration for Java/OpenMP

Automatic OpenCL Device Characterization: Guiding Optimized Kernel Design

Chameleon: Virtualizing idle acceleration cores of a heterogeneous multicore processor for caching and prefetching

Exploring the tradeoffs between programmability and efficiency in data-parallel accelerators

Using Graphics Processing Units for Logic Simulation of Electronic Designs

GPU Versus FPGA for High Productivity Computing

Parallel hierarchical cross entropy optimization for on-chip decap budgeting

Bounding the effect of partition camping in GPU kernels

Benchmarking and modelling of POWER7, Westmere, BG/P, and GPUs: an industry case study

Energy-aware metrics for benchmarking heterogeneous systems

Performance evaluation and optimization of random memory access on multicores with high productivity

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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