The Experts below are selected from a list of 303 Experts worldwide ranked by ideXlab platform

Ivona Brandic - One of the best experts on this subject based on the ideXlab platform.

  • a review of machine learning and meta heuristic methods for scheduling parallel computing systems
    Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications, 2018
    Co-Authors: Suejb Memeti, Alécio Binotto, Sabri Pllana, Joanna Kolodziej, Ivona Brandic
    Abstract:

    Optimized software Execution on parallel computing systems demands consideration of many parameters at run-time. Determining the optimal set of parameters in a given Execution Context is a complex task, and therefore to address this issue researchers have proposed different approaches that use heuristic search or machine learning. In this paper, we undertake a systematic literature review to aggregate, analyze and classify the existing software optimization methods for parallel computing systems. We review approaches that use machine learning or meta-heuristics for scheduling parallel computing systems. Additionally, we discuss challenges and future research directions. The results of this study may help to better understand the state-of-the-art techniques that use machine learning and meta-heuristics to deal with the complexity of scheduling parallel computing systems. Furthermore, it may aid in understanding the limitations of existing approaches and identification of areas for improvement.

  • Using meta-heuristics and machine learning for software optimization of parallel computing systems: a systematic literature review
    Computing, 2018
    Co-Authors: Suejb Memeti, Alécio Binotto, Joanna Kołodziej, Sabri Pllana, Ivona Brandic
    Abstract:

    While modern parallel computing systems offer high performance, utilizing these powerful computing resources to the highest possible extent demands advanced knowledge of various hardware architectures and parallel programming models. Furthermore, optimized software Execution on parallel computing systems demands consideration of many parameters at compile-time and run-time. Determining the optimal set of parameters in a given Execution Context is a complex task, and therefore to address this issue researchers have proposed different approaches that use heuristic search or machine learning. In this paper, we undertake a systematic literature review to aggregate, analyze and classify the existing software optimization methods for parallel computing systems. We review approaches that use machine learning or meta-heuristics for software optimization at compile-time and run-time. Additionally, we discuss challenges and future research directions. The results of this study may help to better understand the state-of-the-art techniques that use machine learning and meta-heuristics to deal with the complexity of software optimization for parallel computing systems. Furthermore, it may aid in understanding the limitations of existing approaches and identification of areas for improvement.

Suejb Memeti - One of the best experts on this subject based on the ideXlab platform.

  • a review of machine learning and meta heuristic methods for scheduling parallel computing systems
    Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications, 2018
    Co-Authors: Suejb Memeti, Alécio Binotto, Sabri Pllana, Joanna Kolodziej, Ivona Brandic
    Abstract:

    Optimized software Execution on parallel computing systems demands consideration of many parameters at run-time. Determining the optimal set of parameters in a given Execution Context is a complex task, and therefore to address this issue researchers have proposed different approaches that use heuristic search or machine learning. In this paper, we undertake a systematic literature review to aggregate, analyze and classify the existing software optimization methods for parallel computing systems. We review approaches that use machine learning or meta-heuristics for scheduling parallel computing systems. Additionally, we discuss challenges and future research directions. The results of this study may help to better understand the state-of-the-art techniques that use machine learning and meta-heuristics to deal with the complexity of scheduling parallel computing systems. Furthermore, it may aid in understanding the limitations of existing approaches and identification of areas for improvement.

  • Using meta-heuristics and machine learning for software optimization of parallel computing systems: a systematic literature review
    Computing, 2018
    Co-Authors: Suejb Memeti, Alécio Binotto, Joanna Kołodziej, Sabri Pllana, Ivona Brandic
    Abstract:

    While modern parallel computing systems offer high performance, utilizing these powerful computing resources to the highest possible extent demands advanced knowledge of various hardware architectures and parallel programming models. Furthermore, optimized software Execution on parallel computing systems demands consideration of many parameters at compile-time and run-time. Determining the optimal set of parameters in a given Execution Context is a complex task, and therefore to address this issue researchers have proposed different approaches that use heuristic search or machine learning. In this paper, we undertake a systematic literature review to aggregate, analyze and classify the existing software optimization methods for parallel computing systems. We review approaches that use machine learning or meta-heuristics for software optimization at compile-time and run-time. Additionally, we discuss challenges and future research directions. The results of this study may help to better understand the state-of-the-art techniques that use machine learning and meta-heuristics to deal with the complexity of software optimization for parallel computing systems. Furthermore, it may aid in understanding the limitations of existing approaches and identification of areas for improvement.

Alécio Binotto - One of the best experts on this subject based on the ideXlab platform.

  • a review of machine learning and meta heuristic methods for scheduling parallel computing systems
    Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications, 2018
    Co-Authors: Suejb Memeti, Alécio Binotto, Sabri Pllana, Joanna Kolodziej, Ivona Brandic
    Abstract:

    Optimized software Execution on parallel computing systems demands consideration of many parameters at run-time. Determining the optimal set of parameters in a given Execution Context is a complex task, and therefore to address this issue researchers have proposed different approaches that use heuristic search or machine learning. In this paper, we undertake a systematic literature review to aggregate, analyze and classify the existing software optimization methods for parallel computing systems. We review approaches that use machine learning or meta-heuristics for scheduling parallel computing systems. Additionally, we discuss challenges and future research directions. The results of this study may help to better understand the state-of-the-art techniques that use machine learning and meta-heuristics to deal with the complexity of scheduling parallel computing systems. Furthermore, it may aid in understanding the limitations of existing approaches and identification of areas for improvement.

  • Using meta-heuristics and machine learning for software optimization of parallel computing systems: a systematic literature review
    Computing, 2018
    Co-Authors: Suejb Memeti, Alécio Binotto, Joanna Kołodziej, Sabri Pllana, Ivona Brandic
    Abstract:

    While modern parallel computing systems offer high performance, utilizing these powerful computing resources to the highest possible extent demands advanced knowledge of various hardware architectures and parallel programming models. Furthermore, optimized software Execution on parallel computing systems demands consideration of many parameters at compile-time and run-time. Determining the optimal set of parameters in a given Execution Context is a complex task, and therefore to address this issue researchers have proposed different approaches that use heuristic search or machine learning. In this paper, we undertake a systematic literature review to aggregate, analyze and classify the existing software optimization methods for parallel computing systems. We review approaches that use machine learning or meta-heuristics for software optimization at compile-time and run-time. Additionally, we discuss challenges and future research directions. The results of this study may help to better understand the state-of-the-art techniques that use machine learning and meta-heuristics to deal with the complexity of software optimization for parallel computing systems. Furthermore, it may aid in understanding the limitations of existing approaches and identification of areas for improvement.

Sabri Pllana - One of the best experts on this subject based on the ideXlab platform.

  • a review of machine learning and meta heuristic methods for scheduling parallel computing systems
    Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications, 2018
    Co-Authors: Suejb Memeti, Alécio Binotto, Sabri Pllana, Joanna Kolodziej, Ivona Brandic
    Abstract:

    Optimized software Execution on parallel computing systems demands consideration of many parameters at run-time. Determining the optimal set of parameters in a given Execution Context is a complex task, and therefore to address this issue researchers have proposed different approaches that use heuristic search or machine learning. In this paper, we undertake a systematic literature review to aggregate, analyze and classify the existing software optimization methods for parallel computing systems. We review approaches that use machine learning or meta-heuristics for scheduling parallel computing systems. Additionally, we discuss challenges and future research directions. The results of this study may help to better understand the state-of-the-art techniques that use machine learning and meta-heuristics to deal with the complexity of scheduling parallel computing systems. Furthermore, it may aid in understanding the limitations of existing approaches and identification of areas for improvement.

  • Using meta-heuristics and machine learning for software optimization of parallel computing systems: a systematic literature review
    Computing, 2018
    Co-Authors: Suejb Memeti, Alécio Binotto, Joanna Kołodziej, Sabri Pllana, Ivona Brandic
    Abstract:

    While modern parallel computing systems offer high performance, utilizing these powerful computing resources to the highest possible extent demands advanced knowledge of various hardware architectures and parallel programming models. Furthermore, optimized software Execution on parallel computing systems demands consideration of many parameters at compile-time and run-time. Determining the optimal set of parameters in a given Execution Context is a complex task, and therefore to address this issue researchers have proposed different approaches that use heuristic search or machine learning. In this paper, we undertake a systematic literature review to aggregate, analyze and classify the existing software optimization methods for parallel computing systems. We review approaches that use machine learning or meta-heuristics for software optimization at compile-time and run-time. Additionally, we discuss challenges and future research directions. The results of this study may help to better understand the state-of-the-art techniques that use machine learning and meta-heuristics to deal with the complexity of software optimization for parallel computing systems. Furthermore, it may aid in understanding the limitations of existing approaches and identification of areas for improvement.

Joanna Kołodziej - One of the best experts on this subject based on the ideXlab platform.

  • Using meta-heuristics and machine learning for software optimization of parallel computing systems: a systematic literature review
    Computing, 2018
    Co-Authors: Suejb Memeti, Alécio Binotto, Joanna Kołodziej, Sabri Pllana, Ivona Brandic
    Abstract:

    While modern parallel computing systems offer high performance, utilizing these powerful computing resources to the highest possible extent demands advanced knowledge of various hardware architectures and parallel programming models. Furthermore, optimized software Execution on parallel computing systems demands consideration of many parameters at compile-time and run-time. Determining the optimal set of parameters in a given Execution Context is a complex task, and therefore to address this issue researchers have proposed different approaches that use heuristic search or machine learning. In this paper, we undertake a systematic literature review to aggregate, analyze and classify the existing software optimization methods for parallel computing systems. We review approaches that use machine learning or meta-heuristics for software optimization at compile-time and run-time. Additionally, we discuss challenges and future research directions. The results of this study may help to better understand the state-of-the-art techniques that use machine learning and meta-heuristics to deal with the complexity of software optimization for parallel computing systems. Furthermore, it may aid in understanding the limitations of existing approaches and identification of areas for improvement.