high performance computing on graphics processing units: hgpu.org

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Parallelizing Word2Vec in Shared and Distributed Memory

Shihao Ji, Nadathur Satish, Sheng Li, Pradeep Dubey

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Tags: Algorithms, Computer science, CUDA, Deep learning, Machine learning, NLP, nVidia, nVidia GeForce GTX Titan X, Tesla K40

May 9, 2016 by hgpu

Parallel Wavelet Schemes for Images

David Barina, Michal Kula, Pavel Zemcik

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Tags: AMD Radeon R9 290X, AMD Radeon R9 Fury, ATI, Compression, Discrete Wavelet Transform, Image processing, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX Titan X, OpenCL, Package

May 7, 2016 by hgpu

Array Program Transformation with Loo.py by Example: High-Order Finite Elements

Andreas Klockner, Lucas C. Wilcox, T. Warburton

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Tags: Algorithms, AMD Radeon R9 Fury, ATI, Code generation, Computer science, Fortran, Numerical Analysis, nVidia, nVidia GeForce GTX Titan X, OpenCL, Package, Performance, Prefetch, Programming Languages, Python, Tesla K40

April 29, 2016 by hgpu

Adaptive GPU Array Layout Auto-Tuning

Nicolas Weber, Michael Goesele

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Tags: Computer science, CUDA, Linear Algbera, nVidia, nVidia GeForce GTX Titan X, Package, Performance, Tesla K20

April 29, 2016 by hgpu

A Unified, Hardware-Fitted, Cross-GPU Performance Model

James Stevens, Andreas Klockner

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Tags: AMD Radeon R9 Fury, ATI, Computer science, nVidia, nVidia GeForce GTX Titan X, OpenCL, Performance, Tesla C2070, Tesla K40

April 19, 2016 by hgpu

A smooth particle hydrodynamics code to model collisions between solid, self-gravitating objects

Christoph M. Schafer, Sven Riecker, Thomas I. Maindl, Roland Speith, Samuel Scherrer, Wilhelm Kley

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Tags: Astrophysics, CUDA, Earth and Planetary Astrophysics, nVidia, nVidia GeForce GTX 770, nVidia GeForce GTX Titan, nVidia GeForce GTX Titan X, Tesla K40, Tesla K80

April 14, 2016 by hgpu

Automatic test case reduction of randomly generated OpenCL kernels

Moritz Pflanzer

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Tags: Code generation, Computer science, LLVM, nVidia, nVidia GeForce GT 630, nVidia GeForce GTX 980, nVidia GeForce GTX Titan X, nVidia Quadro K5200, OpenCL, Thesis

April 6, 2016 by hgpu

A generalized GPU-based connected component labeling algorithm

Yukihiro Komura

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Tags: Algorithms, Computational Physics, CUDA, nVidia, nVidia GeForce GTX Titan X, Physics

March 29, 2016 by hgpu

Generic Inverted Index on the GPU

Jingbo Zhou, Qi Guo, H. V. Jagadish, Wenhao Luan, Anthony K. H. Tung, Yueji Yang, Yuxin Zheng

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Tags: Computer science, CUDA, Databases, Hashing, Nearest neighbour, nVidia, nVidia GeForce GTX Titan X, Tesla K80

March 29, 2016 by hgpu

Accelerating Deep Neural Network Training with Inconsistent Stochastic Gradient Descent

Linnan Wang, Yi Yang, Martin Renqiang Min, Srimat Chakradhar

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Tags: Caffe, Computer science, CUDA, Data parallelism, Deep learning, Heterogeneous systems, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan X

March 25, 2016 by hgpu

Recurrent neural networks for language modeling

Emil Sauer Lynge

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Tags: Computer science, CUDA, Deep learning, LSTM, Neural networks, NLP, nVidia, nVidia GeForce GTX Titan X, Package, Python, RNN, Thesis

March 22, 2016 by hgpu

Faster and Cheaper: Parallelizing Large-Scale Matrix Factorization on GPUs

Wei Tan, Liangliang Cao, Liana Fong

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Tags: Algorithms, Computer science, CUDA, Data parallelism, Factorization, nVidia, nVidia GeForce GTX Titan X, OpenMP, Tesla K80

March 15, 2016 by hgpu

Specx: Speculative task-based runtime system

Specx: a C++ task-based runtime system for heterogeneous distributed architectures

Mutual-Supervised Learning for Sequential-to-Parallel Code Translation

KISim: Kubernetes Intelligent Scheduling Simulator

KIS-S: A GPU-Aware Kubernetes Inference Simulator with RL-Based Auto-Scaling

Hardware Compute Partitioning on NVIDIA GPUs for Composable Systems

Efficient GPU Implementation of Multi-Precision Integer Division

ParEval: A Parallel Code Evaluation Benchmark

ParEval-Repo: A Benchmark Suite for Evaluating LLMs with Repository-level HPC Translation Tasks

FlashSparse: Minimizing Computation Redundancy for Fast Sparse Matrix Multiplications on Tensor Cores

Libra: Synergizing CUDA and Tensor Cores for High-Performance Sparse Matrix Multiplication

exa-AMD: Exascale Accelerated Materials Discovery

Accelerated discovery and design of Fe-Co-Zr magnets with tunable magnetic anisotropy through machine learning and parallel computing

WiLLM: An Open Wireless LLM Communication System

Vcc: the Vulkan Clang Compiler

No More Shading Languages: Compiling C++ to Vulkan Shaders

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high performance computing on graphics processing units: hgpu.org

Parallelizing Word2Vec in Shared and Distributed Memory

Parallel Wavelet Schemes for Images

Array Program Transformation with Loo.py by Example: High-Order Finite Elements

Adaptive GPU Array Layout Auto-Tuning

A Unified, Hardware-Fitted, Cross-GPU Performance Model

A smooth particle hydrodynamics code to model collisions between solid, self-gravitating objects

Automatic test case reduction of randomly generated OpenCL kernels

A generalized GPU-based connected component labeling algorithm

Generic Inverted Index on the GPU

Accelerating Deep Neural Network Training with Inconsistent Stochastic Gradient Descent

Recurrent neural networks for language modeling

Faster and Cheaper: Parallelizing Large-Scale Matrix Factorization on GPUs

Recent source codes

Specx: Speculative task-based runtime system

Mutual-Supervised Learning for Sequential-to-Parallel Code Translation

KISim: Kubernetes Intelligent Scheduling Simulator

Hardware Compute Partitioning on NVIDIA GPUs for Composable Systems

Efficient GPU Implementation of Multi-Precision Integer Division

ParEval: A Parallel Code Evaluation Benchmark

FlashSparse: Minimizing Computation Redundancy for Fast Sparse Matrix Multiplications on Tensor Cores

exa-AMD: Exascale Accelerated Materials Discovery

WiLLM: An Open Wireless LLM Communication System

Vcc: the Vulkan Clang Compiler

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