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On the Partitioning of GPU Power among Multi-Instances

Tirth Vamja, Kaustabha Ray, Felix George, UmaMaheswari C Devi

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Tags: Computer science, CUDA, Energy-efficient computing, Machine learning, Matrix multiplication, nVidia, nVidia A100, nVidia V100, Performance

February 3, 2025 by hgpu

CPPJoules: An Energy Measurement Tool for C++

Shivadharshan S, Akilesh P, Rajrupa Chattaraj, Sridhar Chimalakonda

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Tags: Computer science, CUDA, Energy-efficient computing, nVidia, Package, Software Engineering

December 24, 2024 by hgpu

Automating Energy-Efficient GPU Kernel Generation: A Fast Search-Based Compilation Approach

Yijia Zhang, Zhihong Gou, Shijie Cao, Weigang Feng, Sicheng Zhang, Guohao Dai, Ningyi Xu

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Tags: Computer science, CUDA, Energy-efficient computing, Machine learning, Neural networks, nVidia, nVidia A100, nVidia GeForce RTX 4090, Performance

December 8, 2024 by hgpu

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

Xiaoteng (Frank) Liu, Pavly Halim

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Tags: Computer science, CUDA, Energy-efficient computing, GEMM, Machine learning, Matrix multiplication, nVidia, nVidia GeForce RTX 4070, Package, Performance

December 1, 2024 by hgpu

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

Shamrock: Multi-GPU hydrodynamics for astrophysics

The Shamrock code: I- Smoothed Particle Hydrodynamics on GPUs

LLMPerf: GPU Performance Modeling meets Large Language Models

Hercules: A Compiler for Productive Programming of Heterogeneous Systems

TransCL: An Automatic CUDA-to-OpenCL Programs Transformation Framework

pyATF: The Auto-Tuning Framework (ATF) in Python

pyATF: Constraint-Based Auto-Tuning in Python

TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators

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high performance computing on graphics processing units: hgpu.org

On the Partitioning of GPU Power among Multi-Instances

CPPJoules: An Energy Measurement Tool for C++

Automating Energy-Efficient GPU Kernel Generation: A Fast Search-Based Compilation Approach

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

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

Recent source codes

Shamrock: Multi-GPU hydrodynamics for astrophysics

LLMPerf: GPU Performance Modeling meets Large Language Models

Hercules: A Compiler for Productive Programming of Heterogeneous Systems

Celerity Runtime: High-level C++ for Accelerator Clusters

wgpy: WebGL accelerated numpy-compatible array library for web browser

Microbenchmarking OpenMP target offload with Catch2

SUperman: Highly Efficient Permanent Computation Library

TransCL: An Automatic CUDA-to-OpenCL Programs Transformation Framework

pyATF: The Auto-Tuning Framework (ATF) in Python

TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators

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