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Can Portability Improve Performance? An Empirical Study of Parallel Graph Analytics

Ana Lucia Varbanescu, Merijn Verstraaten, Cees de Laat, Ate Penders, Alexandru Iosup, Henk Sips

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Tags: Algorithms, Computer science, Graph theory, nVidia, nVidia GeForce GTX 480, OpenCL, Performance, Tesla C2050

January 23, 2015 by hgpu

Reproducible and Accurate Matrix Multiplication for GPU Accelerators

Roman Iakymchuk, David Defour, Sylvain Collange, Stef Graillat

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Tags: Algorithms, ATI, ATI Radeon HD 7970, Computer science, Matrix multiplication, nVidia, OpenCL, Performance, Tesla K20

January 21, 2015 by hgpu

GPU concurrency: Weak behaviours and programming assumptions

Jade Alglave, Mark Batty, Alastair F. Donaldson, Ganesh Gopalakrishnan, Jeroen Ketema, Daniel Poetzl, Tyler Sorensen, John Wickerson

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Tags: ATI, ATI Radeon HD 6570, ATI Radeon HD 7970, Computer science, Memory, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 540 M, nVidia GeForce GTX 660, nVidia GeForce GTX 750, nVidia GeForce GTX Titan, OpenCL, Performance, PTX, Tesla C2075

January 21, 2015 by hgpu

Accelerating mahout on heterogeneous clusters using HadoopCL

Xiangyu Li

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Tags: Cluster computing, Computer science, GPU cluster, Heterogeneous systems, Java, Machine learning, MapReduce, nVidia, OpenCL, Tesla K20, Thesis

January 19, 2015 by hgpu

Hybrid Multicore Algorithms for Some Semi-Numerical Applications and Graphs

Dip Sankar Banerjee

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Tags: Algorithms, Cell processor, Computer science, CUDA, Distributed computing, List ranking, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 580, OpenCL, Sorting, Thesis

January 19, 2015 by hgpu

Indexing of Spatiotemporal Trajectories for Efficient Distance Threshold Similarity Searches on the GPU

Michael Gowanlock, Henri Casanova

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Tags: Astrophysics, Computer science, Databases, nVidia, OpenCL, Tesla C2075

January 19, 2015 by hgpu

Identification and Elimination of Platform-Specific Code Smells in High Performance Computing Applications

Chunyan Wang, Shoichi Hirasawa, Hiroyuki Takizawa, Hiroaki Kobayashi

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Tags: Computer science, CUDA, nVidia, OpenACC, OpenCL, Tesla C1060, Tesla C2070, Tesla K20

January 15, 2015 by hgpu

Reducing overheads of dynamic scheduling on heterogeneous chips

Francisco Corbera, Andres Rodriguez, Rafael Asenjo, Angeles Navarro, Antonio Vilches, Maria J. Garzaran

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Tags: Computer science, Heterogeneous systems, OpenCL, Operating systems

January 15, 2015 by hgpu

Linear Performance-Breakdown Model: A Framework for GPU kernel programs performance analysis

Chapa Martell Mario Alberto, Hiroyuki Sato

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Tags: Computer science, nVidia, nVidia GeForce GTX 660, OpenCL, Performance

January 13, 2015 by hgpu

Development of an Algorithm for Extracting Parallelism and Pipeline Structure from Stream-based Processing flow with Spanning Tree

Shinichi Yamagiwa, Guyue Wang, Koichi Wada

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Tags: Computer science, CUDA, nVidia, OpenCL, Tesla C2050

January 13, 2015 by hgpu

CHO: A Benchmark Suite for OpenCL-based FPGA Accelerators

Geoffrey Ndu, Mikel Lujan, Javier Navaridas

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Tags: Benchmarking, Computer science, FPGA, OpenCL, Package

January 8, 2015 by hgpu

An Extensible Framework for Composing Stencils with Common Scientific Computing Patterns

Leonard Truong, Chick Markley, Armando Fox

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Tags: Code generation, Computer science, OpenCL, Python

January 8, 2015 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

Programming

Can Portability Improve Performance? An Empirical Study of Parallel Graph Analytics

Reproducible and Accurate Matrix Multiplication for GPU Accelerators

GPU concurrency: Weak behaviours and programming assumptions

Accelerating mahout on heterogeneous clusters using HadoopCL

Indexing of Spatiotemporal Trajectories for Efficient Distance Threshold Similarity Searches on the GPU

Identification and Elimination of Platform-Specific Code Smells in High Performance Computing Applications

Reducing overheads of dynamic scheduling on heterogeneous chips

Linear Performance-Breakdown Model: A Framework for GPU kernel programs performance analysis

Development of an Algorithm for Extracting Parallelism and Pipeline Structure from Stream-based Processing flow with Spanning Tree

CHO: A Benchmark Suite for OpenCL-based FPGA Accelerators

An Extensible Framework for Composing Stencils with Common Scientific Computing Patterns

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)