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Towards Green Computing: A Survey of Performance and Energy Efficiency of Different Platforms using OpenCL

Philip Heinisch, Katharina Ostaszewski, Hendrik Ranocha

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Tags: AMD FirePro S9150, AMD Radeon R9 280X, ARM, ATI, Benchmarking, Computer science, Energy-efficient computing, Heterogeneous systems, nVidia, nVidia GeForce GTX 570, nVidia GRID K520, nVidia Titan RTX, OpenCL, survey, Tesla C2075, Tesla P100, Tesla V100

March 15, 2020 by hgpu

Evaluating the Energy Efficiency of OpenCL-accelerated AutoDock Molecular Docking

Leonardo Solis-Vasquez, Diogo Santos-Martins, Andreas Koch, Stefano Forli

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Tags: AMD Radeon RX Vega 56, ATI, Biology, Chemistry, Energy-efficient computing, Heterogeneous systems, molecular docking, nVidia, OpenCL, Tesla V100

March 1, 2020 by hgpu

Performance Counters based Power Modeling of Mobile GPUs using Deep Learning

Nadjib Mammeri, Markus Neu, Sohan Lal, Ben Juurlink

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Tags: Computer science, Deep learning, Energy-efficient computing, Heterogeneous systems, Neural networks, OpenCL, OpenGL, SoC

February 23, 2020 by hgpu

Verified Instruction-Level Energy Consumption Measurement for NVIDIA GPUs

Yehia Arafa, Ammar ElWazir, Abdelrahman ElKanishy, Youssef Aly, Ayatelrahman Elsayed, Abdel-Hameed Badawy, Gopinath Chennupati, Stephan Eidenbenz, Nandakishore Santhi

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Tags: Computer science, CUDA, Energy-efficient computing, Measurement techniques, nVidia, nVidia GeForce GTX 1080 Ti, nVidia GeForce GTX Titan V, nVidia GeForce GTX Titan X, nVidia Titan RTX, PTX

February 23, 2020 by hgpu

GPU Computing with Python: Performance, Energy Efficiency and Usability

Håvard H. Holm, André R. Brodtkorb, Martin L. Sætra

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Tags: Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 780, nVidia GeForce GTX 840 M, OpenCL, Performance, Python, Tesla K20, Tesla M2090, Tesla P100, Tesla V100

December 8, 2019 by hgpu

A Power Efficient Neural Network Implementation on Heterogeneous FPGA and GPU Devices

Yuexuan Tu, Saad Sadiq, Yudong Tao, Mei-Ling Shyu, Shu-Ching Chen

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Tags: Computer science, Deep learning, Energy-efficient computing, FPGA, Neural networks, nVidia, nVidia Jetson TX2

July 28, 2019 by hgpu

Energy Efficient Parallel K-Means Clustering for an Intel Hybrid Multi-Chip Package

Matheus Souza, Lucas Maciel, Pedro Henrique Penna, Henrique Freitas

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Tags: Clustering, Computer science, Energy-efficient computing, FPGA, Intel Xeon Phi, Machine learning, OpenCL

March 10, 2019 by hgpu

Application level energy measurements and models for hybrid platform with accelerators

Kenneth O'Brien

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Tags: Computer science, Energy-efficient computing, Intel Xeon Phi, OpenCL, Tesla K40, Thesis

March 3, 2019 by hgpu

Power analysis of sorting algorithms on FPGA using OpenCL

Aidan O Mahony, Emanuel Popovici

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Tags: Computer science, Energy-efficient computing, FPGA, OpenCL, Performance, Sorting

November 3, 2018 by hgpu

Energy-based Tuning of Convolutional Neural Networks on Multi-GPUs

Francisco M. Castro, Nicolas Guil, Manuel J. Marin-Jimenez, Jesus Perez-Serrano, Manuel Ujaldon

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Tags: Artificial intelligence, CNN, Computer science, Computer vision, CUDA, Deep learning, Energy-efficient computing, Neural networks, nVidia, nVidia GeForce GTX Titan X

August 5, 2018 by hgpu

Energy efficiency of finite difference algorithms on multicore CPUs, GPUs, and Intel Xeon Phi processors

Satya P. Jammy, Christian T. Jacobs, David J. Lusher, Neil D. Sandham

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Tags: Algorithms, Computer science, CUDA, Energy-efficient computing, Finite difference, Intel Xeon Phi, nVidia, Tesla K40

October 3, 2017 by hgpu

Smith-Waterman Acceleration in Multi-GPUs: A Performance per Watt Analysis

Jesus Perez Serrano, Edans Flavius De Oliveira Sandes, Alba Cristina Magalhaes Alves de Melo, Manuel Ujaldon

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Tags: Bioinformatics, Biology, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 980, Performance, Smith-Waterman algorithm

June 10, 2017 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

See all packages

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high performance computing on graphics processing units: hgpu.org

Towards Green Computing: A Survey of Performance and Energy Efficiency of Different Platforms using OpenCL

Evaluating the Energy Efficiency of OpenCL-accelerated AutoDock Molecular Docking

Performance Counters based Power Modeling of Mobile GPUs using Deep Learning

Verified Instruction-Level Energy Consumption Measurement for NVIDIA GPUs

GPU Computing with Python: Performance, Energy Efficiency and Usability

A Power Efficient Neural Network Implementation on Heterogeneous FPGA and GPU Devices

Energy Efficient Parallel K-Means Clustering for an Intel Hybrid Multi-Chip Package

Application level energy measurements and models for hybrid platform with accelerators

Power analysis of sorting algorithms on FPGA using OpenCL

Energy-based Tuning of Convolutional Neural Networks on Multi-GPUs

Energy efficiency of finite difference algorithms on multicore CPUs, GPUs, and Intel Xeon Phi processors

Smith-Waterman Acceleration in Multi-GPUs: A Performance per Watt Analysis

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

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