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Energy-Efficient GPU Clusters Scheduling for Deep Learning

Diandian Gu, Xintong Xie, Gang Huang, Xin Jin, Xuanzhe Liu

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Tags: Computer science, Deep learning, Energy-efficient computing, GPU cluster, Neural networks, nVidia, Tesla V100

April 16, 2023 by hgpu

ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales

Xingfu Wu, Prasanna Balaprakash, Michael Kruse, Jaehoon Koo, Brice Videau, Paul Hovland, Valerie Taylor, Brad Geltz, Siddhartha Jana, Mary Hall

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Tags: Bayesian, Computer science, Energy-efficient computing, MPI, nVidia, OpenMP, Package, Tesla V100

April 2, 2023 by hgpu

Task parallelism-based architectures on FPGA to optimize the energy efficiency of AI at the edge

Rafael Gadea-Gironés, Jorge Fe, Jose M. Monzo

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Tags: Artificial intelligence, Computer science, Energy-efficient computing, FPGA, Neural networks, OpenCL

April 2, 2023 by hgpu

A Survey on Optimization Techniques for Edge Artificial Intelligence (AI)

Chellammal Surianarayanan, John Jeyasekaran Lawrence, Pethuru Raj Chelliah, Edmond Prakash, Chaminda Hewage

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Tags: Artificial intelligence, Computer science, Energy-efficient computing, Optimization, survey

February 12, 2023 by hgpu

Precise Energy Consumption Measurements of Heterogeneous Artificial Intelligence Workloads

René Caspart, Sebastian Ziegler, Arvid Weyrauch, Holger Obermaier, Simon Raffeiner, Leon Pascal Schuhmacher, Jan Scholtyssek, Darya Trofimova, Marco Nolden, Ines Reinartz, Fabian Isensee, Markus Götz, Charlotte Debus

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Tags: Artificial intelligence, Computer science, Deep learning, Energy-efficient computing, Heterogeneous systems, nVidia, nVidia A100, nVidia V100, Package

December 11, 2022 by hgpu

Going green: optimizing GPUs for energy efficiency through model-steered auto-tuning

Richard Schoonhoven, Bram Veenboer, Ben van Werkhoven, Kees Joost Batenburg

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Tags: Computer science, CUDA, Energy-efficient computing, nVidia, nVidia RTX A4000, OpenCL, Package, Tesla A100

November 20, 2022 by hgpu

PMT: Power Measurement Toolkit

Stefano Corda, Bram Veenboer, Emma Tolley

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Tags: AMD Radeon Pro W6600, ATI, Computer science, Energy-efficient computing, HPC, nVidia, nVidia Titan RTX, Package, Performance

October 16, 2022 by hgpu

Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training

Jie You, Jae-Won Chung, Mosharaf Chowdhury

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Tags: Computer science, Deep learning, Energy-efficient computing, Neural networks, nVidia, Package, Tesla V100

August 21, 2022 by hgpu

Dynamic GPU Energy Optimization for Machine Learning Training Workloads

Farui Wang, Weizhe Zhang, Shichao Lai, Meng Hao, Zheng Wang

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Tags: Benchmarking, Computer science, CUDA, Energy-efficient computing, Machine learning, nVidia, nVidia GeForce RTX 3080 Ti, Package

January 9, 2022 by hgpu

Energy-aware Task Scheduling with Deadline Constraint in DVFS-enabled Heterogeneous Clusters

Xinxin Mei, Qiang Wang, Xiaowen Chu, Hai Liu, Yiu-Wing Leung, Zongpeng Li

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Tags: Computer science, Deep learning, Energy-efficient computing, Heterogeneous systems, nVidia, nVidia GeForce GTX 1080 Ti, Package, Task scheduling

April 5, 2021 by hgpu

Accurate Energy and Performance Prediction for Frequency-Scaled GPU Kernels

Kaijie Fan, Biagio Cosenza, Ben Juurlink

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Tags: Computer science, Energy-efficient computing, Machine learning, nVidia, nVidia GeForce GTX Titan X, OpenCL, Performance, Tesla P100

May 10, 2020 by hgpu

Learnergy: Energy-based Machine Learners

Mateus Roder, Gustavo Henrique de Rosa, João Paulo Papa

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Tags: Computer science, CUDA, Deep learning, Energy-efficient computing, Machine learning, Neural networks, nVidia, Package, Python, PyTorch

March 22, 2020 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

Energy-Efficient GPU Clusters Scheduling for Deep Learning

ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales

Task parallelism-based architectures on FPGA to optimize the energy efficiency of AI at the edge

A Survey on Optimization Techniques for Edge Artificial Intelligence (AI)

Precise Energy Consumption Measurements of Heterogeneous Artificial Intelligence Workloads

Going green: optimizing GPUs for energy efficiency through model-steered auto-tuning

PMT: Power Measurement Toolkit

Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training

Dynamic GPU Energy Optimization for Machine Learning Training Workloads

Energy-aware Task Scheduling with Deadline Constraint in DVFS-enabled Heterogeneous Clusters

Accurate Energy and Performance Prediction for Frequency-Scaled GPU Kernels

Learnergy: Energy-based Machine Learners

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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