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Performance Analysis of Parallel Sorting Algorithms using GPU Computing

Neetu Faujdar, SP Ghrera

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 460, Performance, Sorting

November 19, 2016 by hgpu

Lattice QCD simulations using the OpenACC platform

Pushan Majumdar

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Tags: CUDA, HEP, High Energy Physics - Lattice, nVidia, OpenACC, Physics, QCD, Tesla X2090

November 19, 2016 by hgpu

Autotuning CUDA Compiler Parameters for Heterogeneous Applications using the OpenTuner Framework

Pedro Bruel, Marcos Amaris, Alfredo Goldman

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Source codes

Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, Matrix multiplication, nVidia, nVidia GeForce GTX 750, nVidia GeForce GTX 980, Package, Performance, Tesla K40

November 16, 2016 by hgpu

Efficient Communications in Training Large Scale Neural Networks

Linnan Wang, Wei Wu, George Bosilca, Richard Vuduc, Zenglin Xu

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Tags: Caffe, Computer science, CUDA, Deep learning, MPI, Neural networks, nVidia, OpenMPI, Performance

November 16, 2016 by hgpu

Data Acquisition with GPUs: The DAQ for the Muon g-2 Experiment at Fermilab

W. Gohn

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Tags: CUDA, Data acquisition, nVidia, Physics, Tesla K40

November 16, 2016 by hgpu

Automatic code generation methods applied to numerical linear algebra in high performance computing

M. Ian Masliah

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Tags: Algorithms, Code generation, Computer science, CUDA, Heterogeneous systems, Linear Algebra, Mixed precision, nVidia, nVidia Tegra TX1, Programming techniques, Tesla C2075, Tesla K40, Thesis

November 16, 2016 by hgpu

CUDA-API-wrappers: Thin C++-flavored wrappers for the CUDA runtime API

Eyal Rozenberg

Source codes

Tags: CUDA, nVidia, Package

November 13, 2016 by epk

OpenCL-based optimizations for acceleration of object tracking on FPGAs and GPUs

Amir Momeni, Hamed Tabkhi, Gunar Schirner, David Kaeli

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Tags: Algorithms, Computer science, FPGA, Heterogeneous systems, nVidia, OpenCL, Tesla K20

November 13, 2016 by hgpu

Hadoopcl2: Motivating the design of a distributed, heterogeneous programming system with machine-learning applications

Max Grossman, Mauricio Breternitz, Vivek Sarkar

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Tags: Algorithms, Computer science, Hadoop, Heterogeneous systems, Machine learning, MapReduce, nVidia, OpenCL, Spark, Tesla M2050

November 13, 2016 by hgpu

Fractal Art Generation using GPUs

Will.D. Mayfield, Justin.C. Eiland, Taylor.J. Hutyra, Matt.C. Paulsen, Bryant.M. Wyatt

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Tags: Algorithms, Computer science, CUDA, Graphics, Image generation, nVidia, nVidia GeForce GTX Titan

November 13, 2016 by hgpu

Shuffle Reduction Based Sparse Matrix-Vector Multiplication on Kepler GPU

Yuan Tao, Huang Zhi-Bin

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Tags: Algorithms, Computer science, CUDA, nVidia, Sparse matrix, Tesla K20

November 10, 2016 by hgpu

Using multiple GPUs to accelerate string searching for digital forensic analysis

Ethan Bayne, R. Ian Ferguson, Adam T. Sampson, John Isaacs

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Tags: Algorithms, Computer science, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 970 M, nVidia GeForce GTX 980, OpenCL, String matching

November 10, 2016 by hgpu

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

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

chemtrain: Training Molecular Dynamics Potentials in JAX

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations

microSYCL: SYCL micro-benchmarks repository

Exploring SYCL as a Portability Layer for High-Performance Computing on CPUs

See all packages

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high performance computing on graphics processing units: hgpu.org

Performance Analysis of Parallel Sorting Algorithms using GPU Computing

Lattice QCD simulations using the OpenACC platform

Autotuning CUDA Compiler Parameters for Heterogeneous Applications using the OpenTuner Framework

Efficient Communications in Training Large Scale Neural Networks

Data Acquisition with GPUs: The DAQ for the Muon g-2 Experiment at Fermilab

Automatic code generation methods applied to numerical linear algebra in high performance computing

CUDA-API-wrappers: Thin C++-flavored wrappers for the CUDA runtime API

OpenCL-based optimizations for acceleration of object tracking on FPGAs and GPUs

Hadoopcl2: Motivating the design of a distributed, heterogeneous programming system with machine-learning applications

Fractal Art Generation using GPUs

Shuffle Reduction Based Sparse Matrix-Vector Multiplication on Kepler GPU

Using multiple GPUs to accelerate string searching for digital forensic analysis

Recent source codes

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

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

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