high performance computing on graphics processing units: hgpu.org

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Auto-Vectorizing a Large-scale Production Unstructured-mesh CFD Application

G.R. Mudalige, I.Z. Reguly, M.B. Giles

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Tags: cfd, Code generation, Computer science, CUDA, Fluid dynamics, Fortran, nVidia, Programming techniques, Tesla K80

February 25, 2016 by hgpu

DIANNE: Distributed Artificial Neural Networks for the Internet of Things

Elias De Coninck, Tim Verbelen, Bert Vankeirsbilck, Steven Bohez, Sam Leroux, Pieter Simoens

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, Java, Neural networks, nVidia, nVidia GeForce GTX 980, nVidia Jetson TK1, Package

February 25, 2016 by hgpu

Parallel Approaches to Shortest-Path Problems for Multilevel Heterogeneous Computing

Hector Ortega Arranz

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 680, nVidia GeForce GTX Titan, Path problems, Thesis

February 25, 2016 by hgpu

Deep Learning At Scale and At Ease

Wei Wang, Gang Chen, Haibo Chen, Tien Tuan Anh Dinh, Jinyang Gao, Beng Chin Ooi, Kian-Lee Tan, Sheng Wang

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Tags: Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce GTX 970, Package

February 23, 2016 by hgpu

Automatic Command Queue Scheduling for Task-Parallel Workloads in OpenCL

Ashwin M. Aji, Antonio J. Pena, Pavan Balaji, Wu-chun Feng

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Tags: Computer science, Heterogeneous systems, nVidia, OpenCL, Performance, Seismology, Task scheduling, Tesla C2050

February 23, 2016 by hgpu

VirtCL: a framework for OpenCL device abstraction and management

Yi-Ping You, Hen-Jung Wu, Yeh-Ning Tsai, Yen-Ting Chao

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Tags: Computer science, Heterogeneous systems, Memory model, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 590, OpenCL, Performance

February 23, 2016 by hgpu

Sparse Convex Optimization on GPUs

Marco Maggioni

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Tags: Algorithms, Computer science, CUDA, Factorization, nVidia, Sparse matrix, Tesla K40, Thesis

February 23, 2016 by hgpu

Togpu: Automatic Source Transformation from C++ to CUDA using Clang/LLVM

Matthew Marangoni, Thomas Wischgoll

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Tags: Code generation, Computer science, CUDA, Image processing, LLVM, nVidia, Tesla C1060

February 23, 2016 by hgpu

HeSP: a simulation framework for solving the task scheduling-partitioning problem on heterogeneous architectures

Anton Rey, Francisco D. Igual, Manuel Prieto-Matias

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Tags: Algorithms, ARM, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 980, OpenCL, Task scheduling

February 19, 2016 by hgpu

Automatic and portable mapping of data parallel programs to OpenCL for GPU-based heterogeneous systems

Zheng Wang, Dominik Grewe, Michael F.P. O'Boyle

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Tags: ATI, ATI Radeon HD 7970, Code generation, Compilers, Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 580, OpenCL, OpenMP, Performance

February 19, 2016 by hgpu

LN-Annote: An Alternative Approach to Information Extraction from Emails using Locally-Customized Named-Entity Recognition

YoungHoon Jung, Karl Stratos, Luca P. Carloni

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Tags: Algorithms, ARM, Cloud, Computer science, Machine learning, Neural networks, NLP, OpenCL

February 19, 2016 by hgpu

Deep Feature-based Face Detection on Mobile Devices

Sayantan Sarkar, Vishal M. Patel, Rama Chellappa

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Tags: Computer science, Computer vision, Deep learning, nVidia, nVidia GeForce GTX 850 M, OpenCL, RenderScript

February 18, 2016 by hgpu

CLUEstering: a high-performance density-based clustering library for scientific computing

PyOMP: OpenMP for Python in Numba

PyOMP: Parallel programming for CPUs and GPUs with OpenMP and Python

GPPerf: Energy-Aware GPU Performance Prediction and Optimization Framework

Understanding GEMM Performance and Energy on NVIDIA Ada Lovelace: A Machine Learning-Based Analytical Approach

HiRep

Scaling SU(2) to 1000 GPUs using HiRep

Gaia AVU-GSR

Performance portability via C++ PSTL, SYCL, OpenMP, and HIP: the Gaia AVU-GSR case study

gpuPairHMM: Ultra-fast GPU-based PairHMM for DNA Variant Calling

gpuPairHMM: High-speed Pair-HMM Forward Algorithm for DNA Variant Calling on GPUs

See all packages

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high performance computing on graphics processing units: hgpu.org

Auto-Vectorizing a Large-scale Production Unstructured-mesh CFD Application

DIANNE: Distributed Artificial Neural Networks for the Internet of Things

Parallel Approaches to Shortest-Path Problems for Multilevel Heterogeneous Computing

Deep Learning At Scale and At Ease

Automatic Command Queue Scheduling for Task-Parallel Workloads in OpenCL

VirtCL: a framework for OpenCL device abstraction and management

Sparse Convex Optimization on GPUs

Togpu: Automatic Source Transformation from C++ to CUDA using Clang/LLVM

HeSP: a simulation framework for solving the task scheduling-partitioning problem on heterogeneous architectures

Automatic and portable mapping of data parallel programs to OpenCL for GPU-based heterogeneous systems

Deep Feature-based Face Detection on Mobile Devices

Recent source codes

tdg-benchs: benchmarks used to test the performance of taskgraph

SW#SYCL

LLOR: Automatic Repair of OpenMP Programs

Solomon: Simple Off-LOading Macros Orchestrating multiple Notations

CLUEstering: a high-performance density-based clustering library for scientific computing

PyOMP: OpenMP for Python in Numba

GPPerf: Energy-Aware GPU Performance Prediction and Optimization Framework

HiRep

Gaia AVU-GSR

gpuPairHMM: Ultra-fast GPU-based PairHMM for DNA Variant Calling

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