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Exploiting Memory Access Patterns to Improve Memory Performance in Data-Parallel Architectures

Byunghyun Jang, Dana Schaa, Perhaad Mistry, David Kaeli

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Tags: ATI, ATI Radeon HD 3870, Brook, Computer science, CUDA, Data parallelism, Memory model, nVidia, nVidia GeForce GTX 285, Review

June 17, 2011 by hgpu

Parallelization of tau-leap coarse-grained Monte Carlo simulations on GPUs

Lifan Xu, Michela Taufer, Stuart Collins, Dionisios G. Vlachos

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Tags: Chemistry, CUDA, Data parallelism, GPU cluster, Monte Carlo simulation, nVidia, Physics, Tesla S1070

June 9, 2011 by hgpu

Mixing Multi-Core CPUs and GPUs for Scientific Simulation Software

K.A. Hawick, A. Leist, and D.P. Playne

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Tags: Cell processor, Computer science, CUDA, Data parallelism, Finite difference, nVidia, nVidia GeForce GTX 295, OpenCL, Partial differential equations, PDEs, Playstation, Pseudo-random number generators

February 14, 2011 by hgpu

Data-parallel algorithms and data structures

John Owens

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Tags: Algorithms, Computer science, Data parallelism, Presentation

January 6, 2011 by hgpu

Data-parallel computing

Chas. Boyd

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Tags: Computer science, Data parallelism, Review

January 5, 2011 by hgpu

Scientific Programming for Heterogeneous Systems – Bridging the Gap between Algorithms and Applications

Alexey Lastovetsky

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Tags: Algorithms, Computer science, Data parallelism

December 15, 2010 by hgpu

MPI within a GPU

Bobby Dalton Young

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Tags: Computer science, CUDA, Data parallelism, MPI, nVidia, nVidia GeForce 8800 GTS, Thesis, Virtualization

November 5, 2010 by hgpu

Mars: a MapReduce framework on graphics processors

Bingsheng He, Wenbin Fang, Qiong Luo, Naga K. Govindaraju, Tuyong Wang

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Tags: Computer science, CUDA, Data parallelism, MapReduce, nVidia, nVidia GeForce 8800 GTX, Package, Programming techniques, Web Analysis

October 30, 2010 by hgpu

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

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

Optical flow algorithms for SYCL

SYCL in the edge: performance and energy evaluation for heterogeneous acceleration

OpenMP5-Offload-OpenMC-Intel-PVC

Distributed OpenMP Offloading of OpenMC on Intel GPU MAX Accelerators

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high performance computing on graphics processing units: hgpu.org

Exploiting Memory Access Patterns to Improve Memory Performance in Data-Parallel Architectures

Parallelization of tau-leap coarse-grained Monte Carlo simulations on GPUs

Mixing Multi-Core CPUs and GPUs for Scientific Simulation Software

Data-parallel algorithms and data structures

Data-parallel computing

Scientific Programming for Heterogeneous Systems – Bridging the Gap between Algorithms and Applications

MPI within a GPU

Mars: a MapReduce framework on graphics processors

Recent source codes

QArray

Celerity: High-level C++ for Accelerator Clusters

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

Optical flow algorithms for SYCL

OpenMP5-Offload-OpenMC-Intel-PVC

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