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

Xiongxiao Xu, Solomon Abera Bekele, Brice Videau, Kai Shu

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Tags: Computer science, Energy-efficient computing, HPC, Intel, Intel Data Center GPU Max 1550, Package

October 20, 2024 by hgpu

Stencil Computations on AMD and Nvidia Graphics Processors: Performance and Tuning Strategies

Johannes Pekkilä, Oskar Lappi, Fredrik Robertsén, Maarit J. Korpi-Lagg

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Tags: AMD Radeon Instinct MI100, AMD Radeon Instinct MI250X, ATI, Computer science, CUDA, Energy-efficient computing, HIP, nVidia, nVidia A100, nVidia V100, Package, Performance, PyTorch, Stencil computation

June 16, 2024 by hgpu

Optimizing Hardware Resource Partitioning and Job Allocations on Modern GPUs under Power Caps

Eishi Arima, Minjoon Kang, Issa Saba, Josef Weidendorfer, Carsten Trinitis, Martin Schulz

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Tags: Computer science, CUDA, Energy-efficient computing, Heterogeneous systems, nVidia, nVidia A100, Performance

May 12, 2024 by hgpu

Sustainable Supercomputing for AI: GPU Power Capping at HPC Scale

Dan Zhao, Siddharth Samsi, Joseph McDonald, Baolin Li, David Bestor, Michael Jones, Devesh Tiwari, Vijay Gadepally

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Tags: AI, Computer science, Energy-efficient computing, HPC, nVidia, nVidia A100

March 3, 2024 by hgpu

Green AI: A Preliminary Empirical Study on Energy Consumption in DL Models Across Different Runtime Infrastructures

Negar Alizadeh, Fernando Castor

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Tags: Computer science, CUDA, Deep learning, Energy-efficient computing, nVidia, nVidia GeForce RTX 3070, Package, PyTorch, TensorFlow

February 25, 2024 by hgpu

A Survey on Hardware Accelerators for Large Language Models

Christoforos Kachris

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Tags: Computer science, Energy-efficient computing, FPGA, Hardware Architecture, NLP, survey

January 21, 2024 by hgpu

Edge AI for Internet of Energy: Challenges and Perspectives

Yassine Himeur, Aya Nabil Sayed, Abdullah Alsalemi, Faycal Bensaali, Abbes Amira

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Tags: Artificial intelligence, Computer science, Energy-efficient computing, Review

December 10, 2023 by hgpu

GPU Auto-tuning Framework for Optimal Performance and Power Consumption

Sunbal Cheema, Gul N. Khan

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Tags: Computer science, Energy-efficient computing, OpenCL

November 19, 2023 by hgpu

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

Kaijie Fan, Marco D'Antonio, Lorenzo Carpentieri, Biagio Cosenza, Federico Ficarelli, Daniele Cesarini

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Tags: AMD Radeon Instinct MI100, ATI, Computer science, Energy-efficient computing, GPU cluster, Heterogeneous systems, Machine learning, nVidia, nVidia V100, SYCL

August 13, 2023 by hgpu

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

Gargi Alavani, Jineet Desai, Snehanshu Saha, Santonu Sarkar

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Tags: Benchmarking, Computer science, CUDA, Energy-efficient computing, Machine learning, nVidia, Package, Tesla K20, Tesla M60, Tesla V100

June 11, 2023 by hgpu

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

Hadi Zamani Sabzi

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Tags: Algorithms, Computer science, CUDA, Energy-efficient computing, Factorization, Heterogeneous systems, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 1660, nVidia GeForce GTX 980, Thesis

May 21, 2023 by hgpu

Prediction of Performance and Power Consumption of GPGPU Applications

Gargi Alavani, Santonu Sarkar

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Tags: Computer science, CUDA, Energy-efficient computing, Java, Machine learning, nVidia, Package, Performance, PTX, Tesla K20, Thesis

May 14, 2023 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

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high performance computing on graphics processing units: hgpu.org

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

Stencil Computations on AMD and Nvidia Graphics Processors: Performance and Tuning Strategies

Optimizing Hardware Resource Partitioning and Job Allocations on Modern GPUs under Power Caps

Sustainable Supercomputing for AI: GPU Power Capping at HPC Scale

Green AI: A Preliminary Empirical Study on Energy Consumption in DL Models Across Different Runtime Infrastructures

A Survey on Hardware Accelerators for Large Language Models

Edge AI for Internet of Energy: Challenges and Perspectives

GPU Auto-tuning Framework for Optimal Performance and Power Consumption

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

Recent source codes

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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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