high performance computing on graphics processing units: hgpu.org

hgpu.org » Performance

Analysis-driven Engineering of Comparison-based Sorting Algorithms on GPUs

Ben Karsin, Volker Weichert, Henri Casanova, John Iacono, Nodari Sitchinava

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 770, nVidia Quadro M 4000, Performance, Sorting, Tesla K40

July 1, 2018 by hgpu

Improving tasks throughput on accelerators using OpenCL command concurrency

A.J. Lazaro-Munoz, J.M. Gonzalez-Linares, J. Gomez-Luna, N. Guil

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Tags: AMD Radeon R9, ATI, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenCL, Performance, Task scheduling, Tesla K20

June 28, 2018 by hgpu

Optimizing Sparse Matrix-Vector Multiplication on Emerging Many-Core Architectures

Shizhao Chen, Jianbin Fang, Donglin Chen, Chuanfu Xu, Zheng Wang

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Tags: Computer science, Intel Xeon Phi, Machine learning, OpenMP, Performance, Sparse matrix

June 9, 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

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

A Strategy for Automatic Performance Tuning of Stencil Computations on GPUs

Joseph D. Garvey, Tarek S. Abdelrahman

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Tags: AMD Radeon R9 390, ATI, Computer science, Machine learning, nVidia, nVidia GeForce GTX Titan, OpenCL, Performance, Stencil computation

April 28, 2018 by hgpu

Dissecting the NVIDIA Volta GPU Architecture via Microbenchmarking

Zhe Jia, Marco Maggioni, Benjamin Staiger, Daniele P. Scarpazza

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Tags: Benchmarking, Computer science, CUDA, nVidia, Performance, PTX, Tesla V100

April 22, 2018 by hgpu

Automatic Optimization of OpenCL-Based Stencil Codes for FPGAs and Its Evaluation

Tsukasa Endo, Hasitha Muthumala Waidyasooriya, Masanori Hariyama

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Tags: Computer science, FPGA, OpenCL, Performance, Stencil computation

April 15, 2018 by hgpu

A Comparison between GPU-based Volume Ray Casting Implementations: Fragment Shader, Compute Shader, OpenCL, and CUDA

Francisco Sans, Rhadames Carmona

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 660, OpenCL, OpenGL, Performance, Rendering

March 31, 2018 by hgpu

Scalable Breadth-First Search on a GPU Cluster

Yuechao Pan, Roger Pearce, John D. Owens

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Tags: Computer science, CUDA, GPU cluster, nVidia, OpenMP, Performance, Tesla P100

March 25, 2018 by hgpu

NVIDIA Tensor Core Programmability, Performance & Precision

Stefano Markidis, Steven Wei Der Chien, Erwin Laure, Ivy Bo Peng, Jeffrey S. Vetter

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Tags: Computer science, CUDA, Mixed precision, nVidia, Performance, Tesla V100

March 17, 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

WiLLM: An Open Wireless LLM Communication System

Vcc: the Vulkan Clang Compiler

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

hpcbench: A set of benchmarking utilities for biomolecular simulation tools

Engineering Supercomputing Platforms for Biomolecular Applications

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration

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high performance computing on graphics processing units: hgpu.org

Analysis-driven Engineering of Comparison-based Sorting Algorithms on GPUs

Improving tasks throughput on accelerators using OpenCL command concurrency

Optimizing Sparse Matrix-Vector Multiplication on Emerging Many-Core Architectures

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

Auto-tuning Streamed Applications on Intel Xeon Phi

A Strategy for Automatic Performance Tuning of Stencil Computations on GPUs

Dissecting the NVIDIA Volta GPU Architecture via Microbenchmarking

Automatic Optimization of OpenCL-Based Stencil Codes for FPGAs and Its Evaluation

A Comparison between GPU-based Volume Ray Casting Implementations: Fragment Shader, Compute Shader, OpenCL, and CUDA

Scalable Breadth-First Search on a GPU Cluster

NVIDIA Tensor Core Programmability, Performance & Precision

Chebyshev Filter Diagonalization on Modern Manycore Processors and GPGPUs

Recent source codes

WiLLM: An Open Wireless LLM Communication System

Vcc: the Vulkan Clang Compiler

hpcbench: A set of benchmarking utilities for biomolecular simulation tools

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration

chemtrain: Training Molecular Dynamics Potentials in JAX

microSYCL: SYCL micro-benchmarks repository

XaaS containers

CASS: Cuda-Amd aSSembly

Cluser of smartphones for edge computing application using TensorFlow

SYCL Container

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