On the Use of Burst Buffers for Accelerating Data-Intensive Scientific Workflows


Presentation held at the 12th Workflows in Support of Large-Scale Science, 2017
Denver, CO, USA – SuperComputing’17

Abstract – Science applications frequently produce and consume large volumes of data, but delivering this data to and from compute resources can be challenging, as parallel file system performance is not keeping up with compute and memory performance. To mitigate this I/O bottleneck, some systems have deployed burst buffers, but their impact on performance for real-world workflow applications is not always clear. In this paper, we examine the impact of burst buffers through the remote-shared, allocatable burst buffers on the Cori system at NERSC. By running a subset of the SCEC CyberShake workflow, a production seismic hazard analysis workflow, we find that using burst buffers offers read and write improvements of about an order of magnitude, and these improvements lead to increased job performance, even for long-running CPU-bound jobs.

 

Related Publication

  • [PDF] [DOI] R. Ferreira da Silva, S. Callaghan, and E. Deelman, “On the Use of Burst Buffers for Accelerating Data-Intensive Scientific Workflows,” in 12th Workshop on Workflows in Support of Large-Scale Science (WORKS’17), 2017.
    [Bibtex]
    @inproceedings{ferreiradasilva-works-2017,
    title = {On the Use of Burst Buffers for Accelerating Data-Intensive Scientific Workflows},
    author = {Ferreira da Silva, Rafael and Callaghan, Scott and Deelman, Ewa},
    booktitle = {12th Workshop on Workflows in Support of Large-Scale Science (WORKS'17)},
    year = {2017},
    pages = {},
    doi = {10.1145/3150994.3151000}
    }

 

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A Characterization of Workflow Management Systems for Extreme-Scale Applications

 

Automation of the execution of computational tasks is at the heart of improving scientific productivity. Scientific workflows have supported breakthroughs across several domains such as astronomy, physics, climate science, earthquake science, biology, and others. Scientific workflow management systems (WMS) are critical automation components that enable efficient and robust workflow execution across heterogeneous infrastructures.

In this paper, we seek to understand the requirements and characteristics of state-of-the-art WMSs for extreme-scale applications. We evaluate and classify 15 popular workflow systems and the applications they support designed specifically for extreme-scale workflows. We surveyed and classified workflow properties and management systems in terms of workflow execution models, heterogeneous computing environments, and data access methods. This paper has identified a number of properties that future WMSs need to support in order to meet extreme-scale requirements, as well as the re-search gaps in the state-of-the-art.

This paper has been published in the Future Generation Computer Systems, available online here.

 

Characterization of state-of-the-art WMSs. The classification highlights relevant characteristics to attain extreme-scale.

Abstract – Automation of the execution of computational tasks is at the heart of improving scientific productivity. Over the last years, scientific workflows have been established as an important abstraction that captures data processing and computation of large and complex scientific applications. By allowing scientists to model and express entire data processing steps and their dependencies,workflow management systems relieve scientists from the details of an application and manage its execution on a computational infrastructure. As the resource requirements of today’s computational and data science applications that process vast amounts of data keep increasing, there is a compelling case for a new generation of advances in high-performance computing, commonly termed as extreme-scale computing, which will bring forth multiple challenges for the design of workflow applications and management systems. This paper presents a novel characterization of workflow management systems using features commonly associated with extreme-scale computing applications. We classify 15 popular workflow management systems in terms of workflow execution models, heterogeneous computing environments, and data access methods. The paper also surveys workflow applications and identifies gaps for future research on the road to extreme-scale workflows and management systems.

 

Reference to the paper:

  • [PDF] [DOI] R. Ferreira da Silva, R. Filgueira, I. Pietri, M. Jiang, R. Sakellariou, and E. Deelman, “A Characterization of Workflow Management Systems for Extreme-Scale Applications,” Future Generation Computer Systems, vol. 75, pp. 228-238, 2017.
    [Bibtex]
    @article{ferreiradasilva-fgcs-2017,
    title = {A Characterization of Workflow Management Systems for Extreme-Scale Applications},
    author = {Ferreira da Silva, Rafael and Filgueira, Rosa and Pietri, Ilia and Jiang, Ming and Sakellariou, Rizos and Deelman, Ewa},
    journal = {Future Generation Computer Systems},
    volume = {75},
    number = {},
    pages = {228--238},
    year = {2017},
    doi = {10.1016/j.future.2017.02.026}
    }

 

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