Change Detection Based Parallelism Mapping: Exploiting Offline Models and Online Adaptation

Emani, Murali; O’Boyle, Michael

doi:10.1007/978-3-319-17473-0_14

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…Their approach predicts the optimal number of threads for a program and also predicts the run time environment. Emani et al [162] proposed an efficient parallel mapping based on online change detection by combining offline model with online adaption to find the optimal number of threads for an OpenMP program. Luk et al [163] proposed a fully automatic mapping technique to map computations to processing elements on heterogeneous multiprocessors.…”

Section: Machine Learning Based Parallelism Mappingmentioning

confidence: 99%

Data Locality in High Performance Computing, Big Data, and Converged Systems: An Analysis of the Cutting Edge and A Future System Architecture

Usman¹,

Mehmood²,

Katib³

et al. 2022

Preprint

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Big data has revolutionised science and technology leading to the transformation of our societies. High Performance Computing (HPC) provides the necessary computational power for big data analysis using artificial intelligence and methods. Traditionally HPC and big data had focused on different problem domains and had grown into two different ecosystems. Efforts have been underway for the last few years on bringing the best of both paradigms into HPC and big converged architectures. Designing HPC and big data converged systems is a hard task requiring careful placement of data, analytics, and other computational tasks such that the desired performance is achieved with the least amount of resources. Energy efficiency has become the biggest hurdle in the realisation of HPC, big data, and converged systems capable of delivering exascale and beyond performance. Data locality is a key parameter of HPDA system design as moving even a byte costs heavily both in time and energy with an increase in the size of the system. Performance in terms of time and energy are the most important factors for users, particularly energy, due to it being the major hurdle in high performance system design and the increasing focus on green energy systems due to environmental sustainability. Data locality is a broad term that encapsulates different aspects including bringing computations to data, minimizing data movement by efficient exploitation of cache hierarchies, reducing intra- and inter-node communications, locality-aware process and thread mapping, and in-situ and in-transit data analysis. This paper provides an extensive review of the cutting-edge on data locality in HPC, big data, and converged systems. We review the literature on data locality in HPC, big data, and converged environments and discuss challenges, opportunities, and future directions. Subsequently, using the knowledge gained from this extensive review, we propose a system architecture for future HPC and big data converged systems. To the best of our knowledge, there is no such review on data locality in converged HPC and big data systems.

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Section: Machine Learning Based Parallelism Mappingmentioning

confidence: 99%

Data Locality in High Performance Computing, Big Data, and Converged Systems: An Analysis of the Cutting Edge and A Future System Architecture

Usman¹,

Mehmood²,

Katib³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Their approach predicts the optimal number of threads for a program and the run-time environment. Emani et al [162] proposed an efficient parallel mapping based on online change detection by combining an offline model with online adaption to find the optimal number of threads for an OpenMP program. Luk et al [163] proposed a fully automatic mapping technique to map computations to processing elements on heterogeneous multiprocessors.…”

Section: Machine Learning-based Parallelism Mappingmentioning

confidence: 99%

Data Locality in High Performance Computing, Big Data, and Converged Systems: An Analysis of the Cutting Edge and a Future System Architecture

Usman¹,

Mehmood

Katib

et al. 2022

Electronics

View full text Add to dashboard Cite

Big data has revolutionized science and technology leading to the transformation of our societies. High-performance computing (HPC) provides the necessary computational power for big data analysis using artificial intelligence and methods. Traditionally, HPC and big data had focused on different problem domains and had grown into two different ecosystems. Efforts have been underway for the last few years on bringing the best of both paradigms into HPC and big converged architectures. Designing HPC and big data converged systems is a hard task requiring careful placement of data, analytics, and other computational tasks such that the desired performance is achieved with the least amount of resources. Energy efficiency has become the biggest hurdle in the realization of HPC, big data, and converged systems capable of delivering exascale and beyond performance. Data locality is a key parameter of HPDA system design as moving even a byte costs heavily both in time and energy with an increase in the size of the system. Performance in terms of time and energy are the most important factors for users, particularly energy, due to it being the major hurdle in high-performance system design and the increasing focus on green energy systems due to environmental sustainability. Data locality is a broad term that encapsulates different aspects including bringing computations to data, minimizing data movement by efficient exploitation of cache hierarchies, reducing intra- and inter-node communications, locality-aware process and thread mapping, and in situ and transit data analysis. This paper provides an extensive review of cutting-edge research on data locality in HPC, big data, and converged systems. We review the literature on data locality in HPC, big data, and converged environments and discuss challenges, opportunities, and future directions. Subsequently, using the knowledge gained from this extensive review, we propose a system architecture for future HPC and big data converged systems. To the best of our knowledge, there is no such review on data locality in converged HPC and big data systems.

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“…An interesting RL based approach for scheduling parallel OpenMP programs is presented in [114]. This approach predicts the best number of threads for a target OpenMP program when it runs with other competing workloads, aiming to make the target program run faster.…”

Section: B Unsupervised Learningmentioning

confidence: 99%

Machine Learning in Compiler Optimization

2018

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Abstract-In the last decade, machine learning based compilation has moved from an an obscure research niche to a mainstream activity. In this article, we describe the relationship between machine learning and compiler optimisation and introduce the main concepts of features, models, training and deployment. We then provide a comprehensive survey and provide a road map for the wide variety of different research areas. We conclude with a discussion on open issues in the area and potential research directions. This paper provides both an accessible introduction to the fast moving area of machine learning based compilation and a detailed bibliography of its main achievements.

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Change Detection Based Parallelism Mapping: Exploiting Offline Models and Online Adaptation

Cited by 6 publications

References 17 publications

Data Locality in High Performance Computing, Big Data, and Converged Systems: An Analysis of the Cutting Edge and A Future System Architecture

Data Locality in High Performance Computing, Big Data, and Converged Systems: An Analysis of the Cutting Edge and A Future System Architecture

Data Locality in High Performance Computing, Big Data, and Converged Systems: An Analysis of the Cutting Edge and a Future System Architecture

Machine Learning in Compiler Optimization

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