high performance computing on graphics processing units: hgpu.org

Ashwin M. Aji, Lokendra S. Panwar, Feng Ji, Milind Chabbi, Karthik Murthy, Pavan Balaji, Keith R. Bisset, James Dinan, Wu-chun Feng, John Mellor-Crummey, Xiaosong May, Rajeev Thakur

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Tags: Computer science, CUDA, GPU cluster, Heterogeneous systems, MPI, nVidia, Optimization, Seismology, Tesla M2050

April 22, 2013 by hgpu

Programming Models and Runtimes for Heterogeneous Systems

Max Grossman

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, Tesla C2050, Tesla M2050, Thesis

April 22, 2013 by hgpu

Valar: A Benchmark Suite to Study the Dynamic Behavior of Heterogeneous Systems

Perhaad Mistry, Yash Ukidave, Dana Schaa, David Kaeli

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Tags: APU, ATI, ATI Radeon HD 7850, ATI Radeon HD 7970, Benchmarking, Computer science, Computer vision, Heterogeneous systems, OpenCL, Performance

April 22, 2013 by hgpu

An Automatic Input-Sensitive Approach for Heterogeneous Task Partitioning

Klaus Kofler, Ivan Grasso, Biagio Cosenza, Thomas Fahringer

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Tags: ATI, ATI Radeon HD 5870, Code generation, Compilers, Computer science, Heterogeneous systems, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 480, OpenCL, Performance, Task scheduling

April 21, 2013 by hgpu

Graphics processing unit (GPU) programming strategies and trends in GPU computing

Andre R. Brodtkorb, Trond R. Hagen, Martin L. Saetra

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Tags: Computer science, Heterogeneous systems, Metaheuristics, Optimization, Overview, Programming techniques

April 19, 2013 by hgpu

GPU Computing in Discrete Optimization – Part II: Survey Focused on Routing Problems

Christian Schulz, Geir Hasle, Andre R. Brodtkorb, Trond R. Hagen

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, Optimization, Tutorial

April 19, 2013 by hgpu

GPU Computing in Discrete Optimization – Part I: Introduction to the GPU

Andre R. Brodtkorb, Trond R. Hagen, Christian Schulz, Geir Hasle

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, Optimization, Tutorial

April 19, 2013 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

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high performance computing on graphics processing units: hgpu.org

GPU Acceleration of the Variational Monte Carlo Method for Many Body Physics

Parallel For Loops on Heterogeneous Resources

Automatic Compilation for Heterogeneous Architectures with Single Assignment C

Directive-based Approach to Heterogeneous Computing

Parallel Computing for Accelerated Texture Classification with Local Binary Pattern Descriptors using OpenCL

On the Efficacy of GPU-Integrated MPI for Scientific Applications

Programming Models and Runtimes for Heterogeneous Systems

Valar: A Benchmark Suite to Study the Dynamic Behavior of Heterogeneous Systems

An Automatic Input-Sensitive Approach for Heterogeneous Task Partitioning

Graphics processing unit (GPU) programming strategies and trends in GPU computing

GPU Computing in Discrete Optimization – Part II: Survey Focused on Routing Problems

GPU Computing in Discrete Optimization – Part I: Introduction to the GPU

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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