high performance computing on graphics processing units: hgpu.org

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

high performance computing on graphics processing units: hgpu.org

SYnergy: Fine-grained Energy-Efficient Heterogeneous Computing for Scalable Energy Saving

Program Analysis and Machine Learning based Approach to Predict Power Consumption of CUDA Kernel

Improving Energy Efficiency of Basic Linear Algebra Routines on Heterogeneous Systems with Multiple GPUs

Prediction of Performance and Power Consumption of GPGPU Applications

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

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