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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

Simulating Quantum Computers Using OpenCL

Adam Kelly

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Tags: Computer science, nVidia, OpenCL, Package, Quantum computing, Quantum Physics, Tesla V100

May 5, 2018 by hgpu

Auto-tuning Streamed Applications on Intel Xeon Phi

Peng Zhang, Jianbin Fang, Tao Tang, Canqun Yang, Zheng Wang

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Tags: Computer science, Intel Xeon Phi, Machine learning, Package, Performance

May 5, 2018 by hgpu

Multidimensional Parallelization for Streaming Text Processing Applications Based on Parabix Framework

Dan Lin

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Tags: Algorithms, Computer science, Data parallelism, LLVM, nVidia, nVidia GeForce GTX 950, Package, PTX, Thesis

April 28, 2018 by hgpu

CytonRL: an Efficient Reinforcement Learning Open-source Toolkit Implemented in C++

Xiaolin Wang

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Tags: Artificial intelligence, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, OpenCV, Package

April 22, 2018 by hgpu

DLL: A Blazing Fast Deep Neural Network Library

Baptiste Wicht, Jean Hennebert, Andreas Fischer

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Tags: Algorithms, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 960, Package, Software Engineering

April 15, 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

CuLDA_CGS: Solving Large-scale LDA Problems on GPUs

Xiaolong Xie, Yun Liang, Xiuhong Li, Wei Tan

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Tags: Algorithms, Computer science, CUDA, Latent Dirichlet allocation, Machine learning, nVidia, nVidia GeForce GTX Titan X, nVidia GeForce GTX Titan XP, Package, Tesla V100

March 17, 2018 by hgpu

Programming Abstractions and Optimization Techniques for GPU-based Heterogeneous Systems

Lu Li

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, Package, Tesla C1060, Tesla C2050, Tesla K20, Tesla M2050, Thesis

March 10, 2018 by hgpu

Chebyshev Filter Diagonalization on Modern Manycore Processors and GPGPUs

Moritz Kreutzer, Georg Hager, Dominik Ernst, Holger Fehske, Alan R. Bishop, Gerhard Wellein

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Tags: Chemistry, Computational Physics, Computer science, CUDA, Intel Xeon Phi, nVidia, OpenMP, Package, Performance, Physics, Quantum Physics, Tesla P100

March 10, 2018 by hgpu

Equalizer 2.0 – Convergence of a Parallel Rendering Framework

Stefan Eilemann, David Steiner, Renato Pajarola

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Tags: Algorithms, Computer science, Graphics, nVidia, nVidia GeForce GTX 970, OpenGL, Package, Rendering, Virtual reality, Visualization

March 3, 2018 by hgpu

Input-Aware Auto-Tuning of Compute-Bound HPC Kernels

Philippe Tillet, David Cox

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Tags: Auto-Tuning, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 980 Ti, OpenCL, Package, Performance, PTX, Tesla P100

February 17, 2018 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

Applications

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

Simulating Quantum Computers Using OpenCL

Auto-tuning Streamed Applications on Intel Xeon Phi

Multidimensional Parallelization for Streaming Text Processing Applications Based on Parabix Framework

CytonRL: an Efficient Reinforcement Learning Open-source Toolkit Implemented in C++

DLL: A Blazing Fast Deep Neural Network Library

Evaluating Performance Tradeoffs on the Radeon Open Compute Platform

CuLDA_CGS: Solving Large-scale LDA Problems on GPUs

Programming Abstractions and Optimization Techniques for GPU-based Heterogeneous Systems

Chebyshev Filter Diagonalization on Modern Manycore Processors and GPGPUs

Equalizer 2.0 – Convergence of a Parallel Rendering Framework

Input-Aware Auto-Tuning of Compute-Bound HPC Kernels

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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