Benchmarking Machine Learning Methods for Performance Modeling of Scientific Applications

Malakar, Preeti; Balaprakash, Prasanna; Vishwanath, Venkatram; Morozov, Vitali; Kumaran, Kalyan

doi:10.1109/pmbs.2018.8641686

Cited by 52 publications

(27 citation statements)

References 35 publications

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“…Porém, a literatura nestaárea aindaé escassa. O que se encontra são trabalhos que avaliam o desempenho (acurácia e tempo de execução, por exemplo) de diferentes algoritmos de AM para resolver tarefas específicas em umaárea de aplicação [Malakar et al 2018, Olson et al 2017, Serpa et al 2018] ou trabalhos que utilizam os algoritmos de AM para predizer o desempenho e o consumo de energia para execução de uma aplicação [Ferreira et al 2017, Wu et al 2016, Klôh et al 2019.…”

Section: Trabalhos Relacionadosunclassified

“…Porém, mesmo com a relevância dos algoritmos de AM, pouco se sabe a respeito dos seus requisitos computacionais e consumo de energia em diferentes arquiteturas computacionais. O que se encontra são pesquisas comparativas entre a acurácia dos algoritmos para resolver um determinado problema [Malakar et al 2018] ou para avaliação de algoritmos de RNAs em GPUs [Santos 2015].…”

Section: Introductionunclassified

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Avaliação do Consumo de Energia e o Impacto da emissão de CO2 para algoritmos de Inteligência Artificial

Bernardo¹,

Silva²,

Gritz³

et al. 2020

Anais Do Brazilian E-Science Workshop (BRESCI 2020)

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Atualmente a Inteligencia Artificial (IA) é uma das forças mais transformadoras do nosso tempo, com resultados surpreendentes. Esses resultados se devem, em grande parte, ao uso de alta capacidade computacional oferecida pelos ambientes de HPC, os quais ao mesmo tempo requerem muita energia para seu funcionamento. Além disso, o consumo de energia é responsável pela emissão de gases de efeito estufa, entre os quais o CO2 é o mais expressivo. Neste trabalho é avaliado o impacto do treinamento de diferentes algoritmos de IA no consumo energético e na emissão de CO2 equivalente entre diferentes arquiteturas computacionais (ARM, GPU e X86).

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Section: Trabalhos Relacionadosunclassified

Section: Introductionunclassified

Avaliação do Consumo de Energia e o Impacto da emissão de CO2 para algoritmos de Inteligência Artificial

Bernardo¹,

Silva²,

Gritz³

et al. 2020

Anais Do Brazilian E-Science Workshop (BRESCI 2020)

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“…In this work, we used a promising approach for empirical modeling, Machine Learning (ML) techniques, in order to predict the time an application takes to complete its task (performance) and the energy consumed for it, from the empirical data (sample set) collected from application runs in phase (a) - Figure 1. This is an important and active area of research in HPC [Malakar et al 2018]. But, accurate performance and energy modeling are complex because of the unknown interaction of the applications and system parameters in these complex systems.…”

Section: Performance and Energy Modelingmentioning

confidence: 99%

Towards an Autonomous Framework for HPC Optimization: Using Machine Learning for Energy and Performance Modeling

Klôh¹,

Gritz²,

Schulze³

et al. 2019

Anais Do XX Simpósio Em Sistemas Computacionais De Alto Desempenho (SSCAD 2019)

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Performance and energy efficiency are now critical concerns in high performance scientific computing. It is expected that requirements of the scientific problem should guide the orchestration of different techniques of energy saving, in order to improve the balance between energy consumption and application performance. To enable this balance, we propose the development of an autonomous framework to make this orchestration and present the ongoing research to this development, more specifically, focusing in the characterization of the scientific applications and the performance modeling tasks using Machine Learning.

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“…However, modeling performance can be used to find the optimal way to schedule a loop and it is linked to our research. Previous works mainly focus on three types of models: analytic, trace-based, and empirical models [10].…”

Section: Related Contributionsmentioning

confidence: 99%

“…In [10], the authors investigate a set of machine learning techniques, including deep neural networks, support vector machine, decision tree, random forest, and k-nearest neighbors to predict the execution time of four different applications. They use deterministic application-specific features for each of their applications.…”

Section: Related Contributionsmentioning

confidence: 99%

Scheduling Optimization of Parallel Linear Algebra Algorithms Using Supervised Learning

Laberge

Shirzad

Diehl

et al. 2019

2019 IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments (MLHPC)

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Linear algebra algorithms are used widely in a variety of domains, e.g machine learning, numerical physics and video games graphics. For all these applications, loop-level parallelism is required to achieve high performance. However, finding the optimal way to schedule the workload between threads is a non-trivial problem because it depends on the structure of the algorithm being parallelized and the hardware the executable is run on. In the realm of Asynchronous Many Task runtime systems, a key aspect of the scheduling problem is predicting the proper chunk-size, where the chunk-size is defined as the number of iterations of a for-loop assigned to a thread as one task. In this paper, we study the applications of supervised learning models to predict the chunk-size which yields maximum performance on multiple parallel linear algebra operations using the HPX backend of Blaze's linear algebra library. More precisely, we generate our training and tests sets by measuring performance of the application with different chunk-sizes for multiple linear algebra operations; vector-addition, matrix-vector-multiplication, matrix-matrix addition and matrix-matrix-multiplication. We compare the use of logistic regression, neural networks and decision trees with a newly developed decision tree based model in order to predict the optimal value for chunk-size. Our results show that classical decision trees and our custom decision tree model are able to forecast a chunk-size which results in good performance for the linear algebra operations.

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Benchmarking Machine Learning Methods for Performance Modeling of Scientific Applications

Cited by 52 publications

References 35 publications

Avaliação do Consumo de Energia e o Impacto da emissão de CO2 para algoritmos de Inteligência Artificial

Avaliação do Consumo de Energia e o Impacto da emissão de CO2 para algoritmos de Inteligência Artificial

Towards an Autonomous Framework for HPC Optimization: Using Machine Learning for Energy and Performance Modeling

Scheduling Optimization of Parallel Linear Algebra Algorithms Using Supervised Learning

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