high performance computing on graphics processing units: hgpu.org

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A Framework for Fast and Efficient Neural Network Compression

Hyeji Kim, Muhammad Umar Karim, Chong-Min Kyung

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Tags: ARM, Caffe, Compression, Computational Complexity, Computer science, Computer vision, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti

December 9, 2018 by hgpu

A Fast and Simple Approach to Merge and Merge Sort using Wide Vector Instructions

Alex Watkins, Oded Green

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Tags: Algorithms, Computer science, Intel Xeon Phi, Sorting

December 2, 2018 by hgpu

Mix-and-Match: A Model-driven Runtime Optimisation Strategy for BFS on GPUs

Merijn Verstraaten, Ana Lucia Varbanescu, Cees de Laat

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Tags: Breadth-First Search, Computer science, CUDA, nVidia, nVidia GeForce GTX Titan X, OpenCL, Package

December 2, 2018 by hgpu

Parallel source code transformation techniques using design patterns

David del Rio Astorga

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Tags: Algorithms, Auto-Tuning, Code generation, Computer science, CUDA, nVidia, nVidia GeForce GTX 680, Tesla K40, Thesis

December 2, 2018 by hgpu

PanJoin: A Partition-based Adaptive Stream Join

Fei Pan, Hans-Arno Jacobsen

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Tags: Algorithms, Computer science, Databases, FPGA, OpenCL

December 2, 2018 by hgpu

Performance Portability Challenges for Fortran Applications

Abigail Hsu, David Neill Asanza, Joseph A. Schoonover, Zach Jibben, Neil N. Carlson, Robert Robey

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Tags: Computer science, CUDA, Finite difference, Fortran, nVidia, OpenMP, Package, performance portability, Tesla P100, Tesla V100

December 2, 2018 by hgpu

CLort: High Throughput and Low Energy Network Intrusion Detection on IoT Devices with Embedded GPUs

Charalampos Stylianopoulos, Linus Johansson, Oskar Olsson, Magnus Almgren

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Tags: ARM, Computer science, IoT, OpenCL, Package, Security

November 25, 2018 by hgpu

SuperNeurons: FFT-based Gradient Sparsification in the Distributed Training of Deep Neural Networks

Linnan Wang, Wei Wu, Yiyang Zhao, Junyu Zhang, Hang Liu, George Bosilca, Jack Dongarra, Maurice Herlihy, Rodrigo Fonseca

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Tags: Compression, Computer science, CUDA, Deep learning, FFT, GPU cluster, Neural networks, nVidia, Package, Tesla P100

November 25, 2018 by hgpu

Modeling Deep Learning Accelerator Enabled GPUs

Md Aamir Raihan, Negar Goli, Tor Aamodt

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Tags: Computer science, CUDA, Deep learning, Hardware Architecture, Machine learning, nVidia, Tesla V100

November 25, 2018 by hgpu

Dense and sparse parallel linear algebra algorithms on graphics processing units

Alejandro Lamas Davina

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, MPI, nVidia, Sparse, Tesla K20, Tesla M2090, Thesis

November 25, 2018 by hgpu

Accelerating Low-End Edge Computing with Cross-Kernel Functionality Abstraction

Chao Wu, Yaoxue Zhang, Yuezhi Zhou, Qiushi Li

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 1080 Ti, OpenCL, Performance

November 18, 2018 by hgpu

Spatter: A Benchmark Suite for Evaluating Sparse Access Patterns

Patrick Lavin, Jason Riedy, Rich Vuduc, Jeffrey Young

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Tags: Benchmarking, Computer science, CUDA, FPGA, Intel Xeon Phi, nVidia, nVidia GeForce GTX Titan XP, OpenCL, Package, Performance, Tesla P100

November 18, 2018 by hgpu

CLUEstering: a high-performance density-based clustering library for scientific computing

PyOMP: OpenMP for Python in Numba

PyOMP: Parallel programming for CPUs and GPUs with OpenMP and Python

GPPerf: Energy-Aware GPU Performance Prediction and Optimization Framework

Understanding GEMM Performance and Energy on NVIDIA Ada Lovelace: A Machine Learning-Based Analytical Approach

HiRep

Scaling SU(2) to 1000 GPUs using HiRep

Gaia AVU-GSR

Performance portability via C++ PSTL, SYCL, OpenMP, and HIP: the Gaia AVU-GSR case study

gpuPairHMM: Ultra-fast GPU-based PairHMM for DNA Variant Calling

gpuPairHMM: High-speed Pair-HMM Forward Algorithm for DNA Variant Calling on GPUs

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high performance computing on graphics processing units: hgpu.org

A Framework for Fast and Efficient Neural Network Compression

A Fast and Simple Approach to Merge and Merge Sort using Wide Vector Instructions

Mix-and-Match: A Model-driven Runtime Optimisation Strategy for BFS on GPUs

Parallel source code transformation techniques using design patterns

PanJoin: A Partition-based Adaptive Stream Join

Performance Portability Challenges for Fortran Applications

CLort: High Throughput and Low Energy Network Intrusion Detection on IoT Devices with Embedded GPUs

SuperNeurons: FFT-based Gradient Sparsification in the Distributed Training of Deep Neural Networks

Modeling Deep Learning Accelerator Enabled GPUs

Dense and sparse parallel linear algebra algorithms on graphics processing units

Accelerating Low-End Edge Computing with Cross-Kernel Functionality Abstraction

Spatter: A Benchmark Suite for Evaluating Sparse Access Patterns

Recent source codes

tdg-benchs: benchmarks used to test the performance of taskgraph

SW#SYCL

LLOR: Automatic Repair of OpenMP Programs

Solomon: Simple Off-LOading Macros Orchestrating multiple Notations

CLUEstering: a high-performance density-based clustering library for scientific computing

PyOMP: OpenMP for Python in Numba

GPPerf: Energy-Aware GPU Performance Prediction and Optimization Framework

HiRep

Gaia AVU-GSR

gpuPairHMM: Ultra-fast GPU-based PairHMM for DNA Variant Calling

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