high performance computing on graphics processing units: hgpu.org

hgpu.org » Heterogeneous systems

Efficient Large-Scale Graph Processing on Hybrid CPU and GPU Systems

Abdullah Gharaibeh, Elizeu Santos-Neto, Lauro Beltrao Costa, Matei Ripeanu

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Tags: Algorithms, Computer science, CUDA, Graph theory, Heterogeneous systems, Hybrid computing, nVidia, Package

December 12, 2013 by hgpu

Runtime Support toward Transparent Memory Access in GPU-accelerated Heterogeneous Systems

Feng Ji

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Tags: Computer science, Hadoop, Heterogeneous systems, MapReduce, MPI, nVidia, nVidia GeForce GTX 280, Tesla M2070, Thesis

December 11, 2013 by hgpu

A Distributed Data Mining Framework Accelerated with Graphics Processing Units

Nam-Luc Tran, Quentin Dugauthier, Sabri Skhiri

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Tags: Clustering, Computer science, Data mining, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan, Tesla K20

December 6, 2013 by hgpu

GPU-based Multi-stream Analyzer on Application Layer for Service-oriented Router

Kazumasa Ikeuchi, Janaka Wijekoon, Shinichi Ishida, Hiroaki Nishi

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 680, Software router, String matching

December 4, 2013 by hgpu

Exploiting Heterogeneous Systems: Keccak on OpenCL

Allan Mariano de Souza, Fabio Dacencio Pereira, Edward David Moreno

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Tags: Algorithms, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 295, OpenCL, Security

December 4, 2013 by hgpu

Benchmarking Parallel Performance on Many-Core Processors

Bryant C. Lam, Ajay Barboza, Ravi Agrawal, Alan D. George, Herman Lam

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

November 29, 2013 by hgpu

Autotuning of Pattern Runtimes for Accelerated Parallel Systems

Enes Bajrovic, Siegfried Benkner, Jiri Dokulil, Martin Sandrieser

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, Tesla C1060, Tesla C2050

November 27, 2013 by hgpu

Regression Modelling of Power Consumption for Heterogeneous Processors

Tahir Diop

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Tags: Benchmarking, Computer science, Energy-efficient computing, Heterogeneous systems, nVidia, OpenCL, Tesla M2090, Thesis

November 27, 2013 by hgpu

Dynamic Partitioning-based JPEG Decompression on Heterogeneous Multicore Architectures

Wasuwee Sodsong, Jingun Hong, Seongwook Chung, Shin-Dug Kim, Bernd Burgstaller

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Tags: Compression, Heterogeneous systems, Image processing, nVidia, nVidia GeForce GT 430, nVidia GeForce GTX 560 Ti, nVidia GeForce GTX 680, OpenCL, Performance

November 23, 2013 by hgpu

Multi-GPU Support on the Marrow Algorithmic Skeleton Framework

Fernando Jorge Marques Alexandre

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Tags: ATI, ATI Radeon HD 7950, Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Performance, Tesla C1060, Thesis

November 20, 2013 by hgpu

A Hybrid GPU/CPU FFT Library for Large FFT Problems

Shuo Chen

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Tags: Computer science, CUDA, FFT, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, Tesla C2070, Tesla C2075, Thesis

November 12, 2013 by hgpu

A High Performance Random Number Generator Using Heterogeneous Computing Platform

Fan Li, Minglu Jin

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, Pseudo-random number generators, Tesla C2070

November 11, 2013 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

Efficient Large-Scale Graph Processing on Hybrid CPU and GPU Systems

Runtime Support toward Transparent Memory Access in GPU-accelerated Heterogeneous Systems

A Distributed Data Mining Framework Accelerated with Graphics Processing Units

GPU-based Multi-stream Analyzer on Application Layer for Service-oriented Router

Exploiting Heterogeneous Systems: Keccak on OpenCL

Benchmarking Parallel Performance on Many-Core Processors

Autotuning of Pattern Runtimes for Accelerated Parallel Systems

Regression Modelling of Power Consumption for Heterogeneous Processors

Dynamic Partitioning-based JPEG Decompression on Heterogeneous Multicore Architectures

Multi-GPU Support on the Marrow Algorithmic Skeleton Framework

A Hybrid GPU/CPU FFT Library for Large FFT Problems

A High Performance Random Number Generator Using Heterogeneous Computing Platform

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)