high performance computing on graphics processing units: hgpu.org

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Design and Modeling of a Non-blocking Checkpointing System

Kento Sato, Kathryn Mohror, Adam Moody, Naoya Maruyama, Satoshi Matsuoka

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Tags: Computer science, Fault tolerance, GPU cluster, nVidia, Tesla M2050

September 11, 2012 by hgpu

A Map-Reduce-Like System for Programming and Optimizing Data-Intensive Computations on Emerging Parallel Architectures

Wei Jiang

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Tags: Computer science, CUDA, Data mining, GPU cluster, Heterogeneous systems, MapReduce, MPI, nVidia, Tesla C2050, Thesis

September 1, 2012 by hgpu

The multi-GPU System with ExpEther

Shimpei Nomura, Tetsuya Nakahama, Junichi Higuchi, Jun Suzuki, Takashi Yoshikawa, Hideharu Amano

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Tags: Computer science, CUDA, GPU cluster, nVidia, Tesla C2050

August 30, 2012 by hgpu

Password Recovery Using MPI and CUDA

David Apostal, Kyle Foerster, Amrita Chatterjee, Travis Desell

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Tags: Algorithms, Computer science, CUDA, GPU cluster, MPI, nVidia, Security, Tesla C1060

August 26, 2012 by hgpu

Extending MPI to Accelerators

Jeff A. Stuart, Pavan Balaji, John D. Owens

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Tags: Computer science, CUDA, GPU cluster, MPI, nVidia, OpenCL

August 26, 2012 by hgpu

High performance transcription factor-DNA docking with GPU computing

Jiadong Wu, Bo Hong, Takako Takeda, Jun-tao Guo

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Tags: Algorithms, Benchmarking, Bioinformatics, Biology, CUDA, Genetics, GPU cluster, nVidia, Tesla M2070

August 20, 2012 by hgpu

OP2: An Active Library Framework for Solving Unstructured Mesh-based Applications on Multi-Core and Many-Core Architectures

G.R. Mudalige, M.B. Giles, I. Reguly, C. Bertolli, P.H.J Kelly

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Tags: Benchmarking, Computer science, CUDA, GPU cluster, Heterogeneous systems, MPI, nVidia, Performance, Tesla C2050, Tesla C2070

August 11, 2012 by hgpu

Solving the Flexible Job Shop Problem on Multi-GPU

Wojciech Bozejko, Zdzilaw Hejducki, Mariusz Uchronski, Mieczyslaw Wodecki

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Tags: Computer science, CUDA, GPU cluster, Metaheuristics, MPI, nVidia, Optimization, Search, Tesla S2050

August 8, 2012 by hgpu

Visualization of Astronomical Nebulae via Distributed Multi-GPU Compressed Sensing Tomography

Stephan Wenger, Marco Ament, Stefan Guthe, Dirk Lorenz, Andreas Tillmann, Daniel Weiskopf, Marcus Magnor

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Tags: Computer science, CUDA, GPU cluster, MPI, nVidia, nVidia GeForce GTX 480, Rendering, Sensing, Tomography, Visualization

August 6, 2012 by hgpu

Analysis and performance estimation of the conjugate gradient method on multiple GPUs

Mickeal Verschoor, Andrei C. Jalba

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Tags: Computer science, Conjugate gradient solver, CUDA, GPU cluster, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 570, Performance, Sparse matrix

August 4, 2012 by hgpu

Interference-driven resource management for GPU-based heterogeneous clusters

Rajat Phull, Cheng-Hong Li, Kunal Rao, Srihari Cadambi, Srimat Chakradhar

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Tags: Computer science, CUDA, GPU cluster, Heterogeneous systems, MPI, nVidia, PC cluster, Performance, Tesla C2050

August 1, 2012 by hgpu

A virtual memory based runtime to support multi-tenancy in clusters with GPUs

Michela Becchi, Kittisak Sajjapongse, Ian Graves, Adam Procter, Vignesh Ravi, Srimat Chakradhar

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Tags: Cloud, Computer science, CUDA, GPU cluster, Heterogeneous systems, nVidia, PC cluster, Tesla C1060, Tesla C2050, Virtualization

July 27, 2012 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

Design and Modeling of a Non-blocking Checkpointing System

A Map-Reduce-Like System for Programming and Optimizing Data-Intensive Computations on Emerging Parallel Architectures

The multi-GPU System with ExpEther

Password Recovery Using MPI and CUDA

Extending MPI to Accelerators

High performance transcription factor-DNA docking with GPU computing

OP2: An Active Library Framework for Solving Unstructured Mesh-based Applications on Multi-Core and Many-Core Architectures

Solving the Flexible Job Shop Problem on Multi-GPU

Visualization of Astronomical Nebulae via Distributed Multi-GPU Compressed Sensing Tomography

Analysis and performance estimation of the conjugate gradient method on multiple GPUs

Interference-driven resource management for GPU-based heterogeneous clusters

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)