high performance computing on graphics processing units: hgpu.org

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Balancing locality and concurrency: solving sparse triangular systems on GPUs

Andrea Picciau, Gordon E. Inggs, John Wickerson, Eric C. Kerrigan, George A. Constantinides

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Tags: Algorithms, AMD FirePro W5000, ATI, Computer science, Linear Algebra, nVidia, nVidia Quadro K4000, OpenCL, Sparse matrix

November 8, 2016 by hgpu

Acceleration of Block-Aware Matrix Factorization on Heterogeneous Platforms

Gregory W. Somers

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Tags: ATI, ATI Radeon HD 7950, Computer science, Factorization, Heterogeneous systems, Linear Algebra, OpenCL, Thesis

September 20, 2016 by hgpu

Feynman Machine: The Universal Dynamical Systems Computer

Eric Laukien, Richard Crowder, Fergal Byrne

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Tags: AMD Radeon R9 290, Artificial intelligence, ATI, Computer science, Deep learning, Mathematics, Neuroscience, OpenCL

September 20, 2016 by hgpu

QSL Squasher: A Fast Quasi-Separatrix Layer Map Calculator

Svetlin Tassev, Antonia Savcheva

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Tags: AMD FirePro W8100, Astrophysics, ATI, OpenCL, Package, Solar and Stellar Astrophysics

September 8, 2016 by hgpu

Parallel Dictionary Learning Algorithms for Sparse Representations

Paul Irofti

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Tags: ATI, ATI FirePro V8800, Computer science, Data mining, Machine learning, OpenCL, Signal processing, Thesis

September 5, 2016 by hgpu

Improving the Performance of the Contextual Spaces Re-Ranking Algorithm on Heterogeneous Systems

Flavia Pisani, Daniel C. G. Pedronette, Ricardo da S. Torres, Edson Borin

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Tags: Algorithms, ATI, ATI Radeon HD 6550, Computer science, Heterogeneous systems, List ranking, OpenCL

September 5, 2016 by hgpu

Fast Multidimensional Image Processing with OpenCL

Daniel Oliveira Dantas, Helton Danilo Passos Leal, Davy Oliveira Barros Sousa

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Tags: AMD Radeon R9 270X, ATI, CUDA, GLSL, Image processing, OpenCL, Package

August 23, 2016 by hgpu

MetaMorph: A Library Framework for Interoperable Kernels on Multi- and Many-core Clusters

Ahmed E. Helal, Paul Sathre, Wu-chun Feng

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Tags: ATI, ATI Radeon HD 7970, Computer science, CUDA, Fluid dynamics, Heterogeneous systems, Intel Xeon Phi, MPI, nVidia, OpenCL, performance portability, Tesla K20

August 23, 2016 by hgpu

A Comprehensive Performance Analysis of HSA and OpenCL 2.0

Saoni Mukherjee, Yifan Sun, Paul Blinzer, Amir Kavyan Ziabari, David Kaeli

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Tags: Algorithms, ATI, Benchmarking, Computer science, Heterogeneous systems, OpenCL, Performance

August 8, 2016 by hgpu

OpenCL-accelerated object classification in video streams using Spatial Pooler of Hierarchical Temporal Memory

Maciej Wielgosz, Marcin Pietron

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Tags: Algorithms, AMD Radeon R9 390, ATI, Computer science, Computer vision, Machine learning, OpenCL, Package, PyOpenCL, Python

August 8, 2016 by hgpu

Algorithmic Trading: A brief, computational finance case study on data centre FPGAs

Gordon Inggs

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Tags: AMD FirePro W5000, ATI, Computational finance, Computer science, Finance, FPGA, nVidia, nVidia Quadro K4000, OpenCL

July 20, 2016 by hgpu

Scientific Computing Using Consumer Video-Gaming Hardware Devices

Glenn Volkema, Gaurav Khanna

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Tags: AMD Radeon R9 Fury X, ATI, Benchmarking, Computer science, nVidia, nVidia Tegra TX1, OpenCL, Package, Performance, Tesla K40

July 20, 2016 by hgpu

Code Generation for Cryptographic Kernels using Multi-word Modular Arithmetic on GPU

GSParLib

GSParLib: A multi-level programming interface unifying OpenCL and CUDA for expressing stream and data parallelism

Keras Sig: Efficient Path Signature Computation on GPU in Keras 3

KSig: GPU-accelerated computation of popular time series kernels

A User’s Guide to KSig: GPU-Accelerated Computation of the Signature Kernel

IterApp: Iterative Application Skeleton

Boosting Performance of Iterative Applications on GPUs: Kernel Batching with CUDA Graphs

SCALE-Ahead-Of-Time Compilation of CUDA for AMD GPUs

LeetDecoding: A PyTorch Library for Exponentially Decaying Causal Linear Attention with CUDA Implementations

Anari-visionaray

Data Parallel Visualization and Rendering on the RAMSES Supercomputer with ANARI

PROM: an open-source software framework to enhance the robustness and performance of predictive models against such changes during deployment

Enhancing Deployment-Time Predictive Model Robustness for Code Analysis and Optimization

DeepSpeed: a deep learning optimization library that makes distributed training and inference easy, efficient, and effective

Scalable Access-Pattern Aware I/O Acceleration and Multi-Tiered Data Management for HPC and AI Workloads

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high performance computing on graphics processing units: hgpu.org

Balancing locality and concurrency: solving sparse triangular systems on GPUs

Acceleration of Block-Aware Matrix Factorization on Heterogeneous Platforms

Feynman Machine: The Universal Dynamical Systems Computer

QSL Squasher: A Fast Quasi-Separatrix Layer Map Calculator

Parallel Dictionary Learning Algorithms for Sparse Representations

Fast Multidimensional Image Processing with OpenCL

MetaMorph: A Library Framework for Interoperable Kernels on Multi- and Many-core Clusters

A Comprehensive Performance Analysis of HSA and OpenCL 2.0

OpenCL-accelerated object classification in video streams using Spatial Pooler of Hierarchical Temporal Memory

Algorithmic Trading: A brief, computational finance case study on data centre FPGAs

Scientific Computing Using Consumer Video-Gaming Hardware Devices

Recent source codes

Code Generation for Cryptographic Kernels using Multi-word Modular Arithmetic on GPU

GSParLib

Keras Sig: Efficient Path Signature Computation on GPU in Keras 3

KSig: GPU-accelerated computation of popular time series kernels

IterApp: Iterative Application Skeleton

SCALE-Ahead-Of-Time Compilation of CUDA for AMD GPUs

LeetDecoding: A PyTorch Library for Exponentially Decaying Causal Linear Attention with CUDA Implementations

Anari-visionaray

PROM: an open-source software framework to enhance the robustness and performance of predictive models against such changes during deployment

DeepSpeed: a deep learning optimization library that makes distributed training and inference easy, efficient, and effective

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