high performance computing on graphics processing units: hgpu.org

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Efficient and Scalable k-Means on GPUs

Clemens Lutz, Sebastian Bress, Tilmann Rabl, Steffen Zeuch, Volker Markl

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Tags: Algorithms, Clustering, Computer science, nVidia, nVidia GeForce GTX 1080, OpenCL, Package

September 9, 2018 by hgpu

Compiler Fuzzing through Deep Learning

Chris Cummins, Pavlos Petoumenos, Hugh Leather, Alastair Murray

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Tags: Compilers, Computer science, Deep learning, Intel Xeon Phi, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 780, OpenCL, Package

July 1, 2018 by hgpu

NCRF++: An Open-source Neural Sequence Labeling Toolkit

Jie Yang, Yue Zhang

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Tags: CNN, Computer science, CUDA, Deep learning, LSTM, NLP, nVidia, nVidia GeForce GTX 1080, Package, Python, Torch

June 17, 2018 by hgpu

Deep Fluids: A Generative Network for Parameterized Fluid Simulations

Byungsoo Kim, Vinicius C. Azevedo, Nils Thuerey, Theodore Kim, Markus Gross, Barbara Solenthaler

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Tags: cfd, Deep learning, Fluid dynamics, Fluid simulation, Neural networks, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX Titan X, TensorFlow

June 9, 2018 by hgpu

Deep Neural Machine Translation with Weakly-Recurrent Units

Mattia Antonino Di Gangi, Marcello Federico

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Tags: Computer science, CUDA, NLP, nVidia, nVidia GeForce GTX 1080, Package, RNN, Tesla K80, Torch

May 20, 2018 by hgpu

Dwarfs on Accelerators: Enhancing OpenCL Benchmarking for Heterogeneous Computing Architectures

Beau Johnston, Josh Milthorpe

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Tags: AMD FirePro S9150, AMD Radeon HD 7970, AMD Radeon R9 290X, AMD Radeon R9 Fury X, AMD Radeon RX 480, ATI, Benchmarking, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia GeForce GTX 1080, OpenCL, Tesla K20, Tesla K40

May 12, 2018 by hgpu

Modeling and Evaluation of Synchronous Stochastic Gradient Descent in Distributed Deep Learning on Multiple GPUs

Shaohuai Shi, Qiang Wang, Xiaowen Chu, Bo Li

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Tags: Artificial intelligence, Benchmarking, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 980, Package, Performance, Tesla K80

May 12, 2018 by hgpu

Evaluating Performance Tradeoffs on the Radeon Open Compute Platform

Yifan Sun, Saoni Mukherjee, Trinayan Baruah, Shi Dong, Julian Gutierrez, Prannoy Mohan, David Kaeli

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Tags: AMD R9 Nano, ATI, Computer science, Heterogeneous systems, HSA, nVidia, nVidia GeForce GTX 1080, OpenCL, Package

April 7, 2018 by hgpu

TVM: End-to-End Optimization Stack for Deep Learning

Tianqi Chen, Thierry Moreau, Ziheng Jiang, Haichen Shen, Eddie Yan, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy

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Tags: Artificial intelligence, Computer science, CUDA, Deep learning, FPGA, Machine learning, nVidia, nVidia GeForce GTX 1080, OpenCL, Package, performance portability, TensorFlow, Tesla K80

February 15, 2018 by hgpu

An MPI-Based Python Framework for Distributed Training with Keras

Dustin Anderson, Jean-Roch Vlimant, Maria Spiropulu

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Tags: Computer science, CUDA, Deep learning, Keras, Machine learning, MPI, Neural networks, nVidia, nVidia GeForce GTX 1080, Package, Python, Tesla K80

December 24, 2017 by hgpu

Effective Extensible Programming: Unleashing Julia on GPUs

Tim Besard, Christophe Foket, Bjorn De Sutter

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Tags: Code generation, Computer science, CUDA, High-level Languages, LLVM, nVidia, nVidia GeForce GTX 1080, Package, Programming Languages

December 15, 2017 by hgpu

Computing the distance between two finite element solutions defined on different 3D meshes on a GPU

Maxence Reberol, Bruno Levy

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Tags: Computer science, nVidia, nVidia GeForce GTX 1080, OpenGL, Package, Traingular meshes

November 26, 2017 by hgpu

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

DuoReduce: MLIR's benchmark

Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

Shamrock: Multi-GPU hydrodynamics for astrophysics

The Shamrock code: I- Smoothed Particle Hydrodynamics on GPUs

LLMPerf: GPU Performance Modeling meets Large Language Models

Hercules: A Compiler for Productive Programming of Heterogeneous Systems

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high performance computing on graphics processing units: hgpu.org

Efficient and Scalable k-Means on GPUs

Compiler Fuzzing through Deep Learning

NCRF++: An Open-source Neural Sequence Labeling Toolkit

Deep Fluids: A Generative Network for Parameterized Fluid Simulations

Deep Neural Machine Translation with Weakly-Recurrent Units

Dwarfs on Accelerators: Enhancing OpenCL Benchmarking for Heterogeneous Computing Architectures

Modeling and Evaluation of Synchronous Stochastic Gradient Descent in Distributed Deep Learning on Multiple GPUs

Evaluating Performance Tradeoffs on the Radeon Open Compute Platform

TVM: End-to-End Optimization Stack for Deep Learning

An MPI-Based Python Framework for Distributed Training with Keras

Effective Extensible Programming: Unleashing Julia on GPUs

Computing the distance between two finite element solutions defined on different 3D meshes on a GPU

Recent source codes

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

DuoReduce: MLIR's benchmark

Shamrock: Multi-GPU hydrodynamics for astrophysics

LLMPerf: GPU Performance Modeling meets Large Language Models

Hercules: A Compiler for Productive Programming of Heterogeneous Systems

Celerity Runtime: High-level C++ for Accelerator Clusters

wgpy: WebGL accelerated numpy-compatible array library for web browser

Microbenchmarking OpenMP target offload with Catch2

SUperman: Highly Efficient Permanent Computation Library

TransCL: An Automatic CUDA-to-OpenCL Programs Transformation Framework

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