high performance computing on graphics processing units: hgpu.org

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Cross-Platform Performance Portability Using Highly Parametrized SYCL Kernels

John Lawson, Mehdi Goli, Duncan McBain, Daniel Soutar, Louis Sugy

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Tags: AMD R9 Nano, ATI, BLAS, Computer science, Deep learning, Linear Algebra, Machine learning, Mathematical Software, OpenCL, Package, Performance, performance portability, SYCL

April 14, 2019 by hgpu

On the Representation of Partially Specified Implementations and its Application to the Optimization of Linear Algebra Kernels on GPU

Ulysse Beaugnon, Basile Clément, Nicolas Tollenaere, Albert Cohen

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Tags: Analytical model, Computer science, CUDA, Linear Algebra, nVidia, nVidia Quadro K4000, Programming Languages

April 14, 2019 by hgpu

High Performance Monte Carlo Simulation of Ising Model on TPU Clusters

Kun Yang, Yi-Fan Chen, Georgios Roumpos, Chris Colby, John Anderson

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Tags: Cloud, CUDA, Ising model, Machine learning, Monte Carlo simulation, Neural networks, nVidia, Package, Physics, Tesla V100, TPU

April 7, 2019 by hgpu

The Study of the OpenCL Processing Models for the FPGA Devices

Piotr Szkotak, Pawel Russek, Kazimierz Wiatr

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Tags: AMD Radeon R9 290, ATI, Computer science, FPGA, HLS, nVidia, nVidia GeForce GTX 1080, OpenCL, Package

April 7, 2019 by hgpu

Full-System Simulation of Mobile CPU/GPU Platforms

Kuba Kaszyk, Harry Wagstaff, Tom Spink, Bjorn Franke, Mike O'Boyle, Bruno Bodin

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Tags: Computer science, Computer vision, OpenCL

April 7, 2019 by hgpu

fairseq: A Fast, Extensible Toolkit for Sequence Modeling

Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, Michael Auli

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Tags: Computer science, CUDA, NLP, nVidia, Package, PyTorch, Video

April 7, 2019 by hgpu

Methods for Accelerating Machine Learning in High Performance Computing

Robert Lim

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, Tesla P100, Thesis

March 31, 2019 by hgpu

Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey

Giang Nguyen, Stefan Dlugolinsky, Martin Bobak, Viet Tran, Alvaro Lopez Garcia, Ignacio Heredia, Peter Malik, Ladislav Hluchy

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Tags: Algorithms, Artificial intelligence, Computer science, CUDA, Data mining, Deep learning, Machine learning, nVidia, OpenCL

March 31, 2019 by hgpu

Dynamic Application Autotuning for Self-Aware Approximate Computing

Davide Gadioli

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Tags: Auto-Tuning, Computer science, OpenCL, Package, Thesis

March 31, 2019 by hgpu

Hybrid CPU-GPU execution support in the skeleton programming framework SkePU

Tomas Oehberg, August Ernstsson, Christoph Kessler

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Tags: Computer science, CUDA, Heterogeneous systems, Hybrid computing, MapReduce, nVidia, OpenCL, Package, Tesla K20

March 31, 2019 by hgpu

HeteroMap: A Runtime Performance Predictor for Efficient Processing of Graph Analytics on Heterogeneous Multi-Accelerators

Masab Ahmad, Halit Dogan, Christopher J. Michael, Omer Khan

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Tags: Algorithms, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia GeForce GTX 750 Ti, nVidia GeForce GTX 970, OpenCL, Package, Performance

March 31, 2019 by hgpu

swCaffe: a Parallel Framework for Accelerating Deep Learning Applications on Sunway TaihuLight

Jiarui Fang, Liandeng Li, Haohuan Fu, Jinlei Jiang, Wenlai Zhao, Conghui He, Xin You, Guangwen Yang

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, Neural networks, nVidia, OpenMPI, Package, Tesla K40

March 24, 2019 by hgpu

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Efficient deep learning inference on end devices

Ouroboros: Virtualized Queues for dynamic memory management

Dynamic Memory Management on GPUs with SYCL

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high performance computing on graphics processing units: hgpu.org

Programming

Cross-Platform Performance Portability Using Highly Parametrized SYCL Kernels

On the Representation of Partially Specified Implementations and its Application to the Optimization of Linear Algebra Kernels on GPU

The Study of the OpenCL Processing Models for the FPGA Devices

Full-System Simulation of Mobile CPU/GPU Platforms

fairseq: A Fast, Extensible Toolkit for Sequence Modeling

Methods for Accelerating Machine Learning in High Performance Computing

Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey

Dynamic Application Autotuning for Self-Aware Approximate Computing

Hybrid CPU-GPU execution support in the skeleton programming framework SkePU

HeteroMap: A Runtime Performance Predictor for Efficient Processing of Graph Analytics on Heterogeneous Multi-Accelerators

swCaffe: a Parallel Framework for Accelerating Deep Learning Applications on Sunway TaihuLight

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Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

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Ouroboros: Virtualized Queues for dynamic memory management

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