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Parallel Performance Measurement of Heterogeneous Parallel Systems with GPUs

Allen D. Malony, Scott Biersdorff, Sameer Shende, Heike Jagode, Stanimire Tomov, Guido Juckeland, Robert Dietrich, Duncan Poole, Christopher Lamb

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, OpenCL, Performance, Tesla C870

November 17, 2011 by hgpu

Large Scale Plane Wave Pseudopotential Density Functional Theory Calculations on GPU Clusters

Long Wang, Weile Jia, Xuebin Chi, Yue Wu, Weiguo Gao, Lin-Wang Wang

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Tags: Algorithms, Computational chemistry, CUDA, GPU cluster, MPI, nVidia, Performance, Physics, Tesla C2050

November 14, 2011 by hgpu

Comprehensive Performance Monitoring for GPU Cluster Systems

K. Furlinger, N.J. Wright, D. Skinner

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Tags: Benchmarking, Computer science, CUDA, GPU cluster, MPI, nVidia, Performance, Tesla C2050

November 13, 2011 by hgpu

Evaluation of an accelerator architecture for Speckle Reducing Anisotropic Diffusion

Siddharth Nilakantan, Srikanth Annangi, Nikhil Gulati, Karthik Sangaiah, Mark Hempstead

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Tags: Algorithms, Computer science, CUDA, Embedded high-performance computing, nVidia, nVidia GeForce 8800 GTX, OpenMP, Performance, Ultrasound

November 12, 2011 by hgpu

Building a Real-Time Multi-GPU Platform: Robust Real-Time Interrupt Handling Despite Closed-Source Drivers

Glenn A. Elliott, James H. Anderson

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Source codes

Tags: Computer science, CUDA, GPU cluster, nVidia, nVidia GeForce GTX 470, Package, Performance

November 11, 2011 by hgpu

Improving GPU Performance via Large Warps and Two-Level Warp Scheduling

Veynu Narasiman, Chang Joo Lee, Michael Shebanow, Rustam Miftakhutdinov, Onur Mutlu, Yale N. Patt

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Tags: Computer science, CUDA, nVidia, Performance, Warp level parallelism

November 11, 2011 by hgpu

Understanding the impact of CUDA tuning techniques for Fermi

Yuri Torres, Arturo Gonzalez-Escribano, Diego R. Llanos

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Performance

November 10, 2011 by hgpu

Performance Tuning for CUDA-Accelerated Neighborhood Denoising Filters

Ziyi Zheng, Wei Xu and Klaus Mueller

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Tags: Computed tomography, CT, CUDA, Filtering, Image processing, nVidia, nVidia GeForce GTX 480, Performance, Signal denoising

November 9, 2011 by hgpu

Effects of GPU and CPU Loads on Performance of CUDA Applications

Maksim Bobrov, Roy Melton, Stanislaw Radziszowski, Marcin Lukowiak

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Tags: AES, Algorithms, Computer science, CUDA, Hashing, nVidia, nVidia GeForce GTX 285, Performance, Security

November 9, 2011 by hgpu

Large data visualization on distributed memory multi-GPU clusters

Thomas Fogal, Hank Childs, Siddharth Shankar, Jens Kruger, R. Daniel Bergeron, Philip Hatcher

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Tags: Algorithms, Computer science, GLSL, GPU cluster, nVidia, nVidia GeForce 8800 GTX, nVidia Quadro FX 5800, OpenGL, Performance, Rendering, Visualization

November 9, 2011 by hgpu

Performance analysis of a hybrid MPI/CUDA implementation of the NASLU benchmark

S.J. Pennycook, S.D. Hammond, S.A. Jarvis, G.R. Mudalige

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Tags: Benchmarking, Computer science, CUDA, MPI, nVidia, nVidia GeForce 8400 GS, nVidia GeForce 9400 GT, Operating systems, Performance, Tesla C1060, Tesla C2050, Tesla T10

November 8, 2011 by hgpu

Functional Programming for High-Performance Computing on Heterogeneous Architectures

Sylvain Henry

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia Quadro FX 5800, OpenCL, Optimization, Performance, Programming Languages, Programming techniques

November 7, 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

Parallel Performance Measurement of Heterogeneous Parallel Systems with GPUs

Large Scale Plane Wave Pseudopotential Density Functional Theory Calculations on GPU Clusters

Comprehensive Performance Monitoring for GPU Cluster Systems

Evaluation of an accelerator architecture for Speckle Reducing Anisotropic Diffusion

Building a Real-Time Multi-GPU Platform: Robust Real-Time Interrupt Handling Despite Closed-Source Drivers

Improving GPU Performance via Large Warps and Two-Level Warp Scheduling

Understanding the impact of CUDA tuning techniques for Fermi

Performance Tuning for CUDA-Accelerated Neighborhood Denoising Filters

Effects of GPU and CPU Loads on Performance of CUDA Applications

Large data visualization on distributed memory multi-GPU clusters

Performance analysis of a hybrid MPI/CUDA implementation of the NASLU benchmark

Functional Programming for High-Performance Computing on Heterogeneous Architectures

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)