high performance computing on graphics processing units: hgpu.org

hgpu.org » Benchmarking

Performance and Power Comparisons Between Fermi and Cypress GPUs

Ying Zhang

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Tags: ATI, ATI Radeon HD 5870, Benchmarking, Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 580, OpenCL, Performance, Thesis

November 18, 2013 by hgpu

An Empirical Study of Intel Xeon Phi

Jianbin Fang, Ana Lucia Varbanescu, Henk Sips, Lilun Zhang, Yonggang Che, Chuanfu Xu

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Tags: Benchmarking, Computer science, Intel Phi, Intel Xeon Phi, Performance

October 25, 2013 by hgpu

Understanding and Modeling the Synchronization Cost in the GPU Architecture

James T. Letendre

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, Performance, Tesla C2075, Thesis

October 17, 2013 by hgpu

Performance Comparison of GPU, DSP and FPGA implementations of image processing and computer vision algorithms in embedded systems

Egil Fykse

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Source codes

Tags: Benchmarking, Computer science, Computer vision, CUDA, DSP, FPGA, Image processing, nVidia, nVidia GeForce GTX 660 Ti, nVidia Quadro 1000 M, OpenCV, Performance, Thesis

October 15, 2013 by hgpu

Performance Analysis of a Large Memory Application on Multiple Architectures

P.M.Kogge

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Tags: Benchmarking, Heterogeneous systems, nVidia, nVidia Tesla, Performance

October 9, 2013 by hgpu

Compiling a High-level Directive-Based Programming Model for GPGPUs

Xiaonan Tian, Rengan Xu, Yonghong Yan, Zhifeng Yun, Sunita Chandrasekaran, Barbara Chapman

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Tags: Benchmarking, Computer science, nVidia, OpenACC, Tesla K20

September 30, 2013 by hgpu

Data-parallel Acceleration of PARSEC Black-Scholes Benchmark

August Andren, Patrik Hagernas

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Tags: Benchmarking, Computer science, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 680, OpenACC, Tesla C2050, Tesla M2090

September 30, 2013 by hgpu

BenchFriend: Correlating the Performance of GPU Benchmarks

Shuai Che, Kevin Skadron

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Tags: Benchmarking, Computer science, CUDA, GPGPU-sim, nVidia, Tesla C2050, Tesla K20

September 23, 2013 by hgpu

Processing MPI Derived Datatypes on Noncontiguous GPU-Resident Data

John Jenkins, James Dinan, Pavan Balaji, Tom Peterka, Nagiza F. Samatova, Rajeev Thakur

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Tags: Algorithms, Benchmarking, Computer science, CUDA, MPI, nVidia, Tesla C2050

September 23, 2013 by hgpu

Porting to the Intel Xeon Phi: Opportunities and Challenges

C. Rosales

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, Intel Phi, MPI, nVidia, Optimization, Performance

September 15, 2013 by hgpu

D5.5.2 – Architectural Techniques to exploit SLACK & ACCURACY trade-offs

S. Kaxiras, G. Keramidas, K. Koukos

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Tags: Benchmarking, Memory, Power-efficient computing

September 6, 2013 by hgpu

Optimizing the MapReduce Framework on Intel Xeon Phi Coprocessor

Mian Lu, Lei Zhang, Huynh Phung Huynh, Zhongliang Ong, Yun Liang, Bingsheng He, Rick Siow Mong Goh, Richard Huynh

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Tags: Benchmarking, Computer science, Intel Phi, MapReduce

September 4, 2013 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

Performance and Power Comparisons Between Fermi and Cypress GPUs

An Empirical Study of Intel Xeon Phi

Understanding and Modeling the Synchronization Cost in the GPU Architecture

Performance Comparison of GPU, DSP and FPGA implementations of image processing and computer vision algorithms in embedded systems

Performance Analysis of a Large Memory Application on Multiple Architectures

Compiling a High-level Directive-Based Programming Model for GPGPUs

Data-parallel Acceleration of PARSEC Black-Scholes Benchmark

BenchFriend: Correlating the Performance of GPU Benchmarks

Processing MPI Derived Datatypes on Noncontiguous GPU-Resident Data

Porting to the Intel Xeon Phi: Opportunities and Challenges

D5.5.2 – Architectural Techniques to exploit SLACK & ACCURACY trade-offs

Optimizing the MapReduce Framework on Intel Xeon Phi Coprocessor

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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