Measuring the Impact of Burst Buffers on Data-Intensive Scientific Workflows

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 scientific workflow applications is still not 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 two data-intensive workflows, a high-throughput genome analysis workflow, and a subset of the SCEC high-performance CyberShake workflow, a production seismic hazard analysis workflow, we find that using burst buffers offers read and write improvements of an order of magnitude, and these improvements lead to increased job performance, and thereby increased overall workflow performance, even for long-running CPU-bound jobs.

I/O Read: performance comparison of read operations with burst- buffers (top) and the PFS (bottom) at NERSC.

Reference to the paper:

  • [PDF] [DOI] R. Ferreira da Silva, S. Callaghan, T. M. A. Do, G. Papadimitriou, and E. Deelman, “Measuring the Impact of Burst Buffers on Data-Intensive Scientific Workflows,” Future Generation Computer Systems, vol. 101, p. 208–220, 2019.
    [Bibtex]
    @article{ferreiradasilva-fgcs-bb-2019,
    title = {Measuring the Impact of Burst Buffers on Data-Intensive Scientific Workflows},
    author = {Ferreira da Silva, Rafael and Callaghan, Scott and Do, Tu Mai Anh and Papadimitriou, George and Deelman, Ewa},
    journal = {Future Generation Computer Systems},
    volume = {101},
    number = {},
    pages = {208--220},
    year = {2019},
    doi = {10.1016/j.future.2019.06.016}
    }

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14th Workflows in Support of Large-Scale Science – WORKS 2019 @SC19

The 14th Workflows in Support of Large-Scale Science (WORKS) Workshop will be held in conjunction with SC19 on Sunday November 17, 2019 in Denver, CO.

Important Dates

  • Papers due: July 15, 2019 July 29, 2019
  • Paper Acceptance Notification: September 1, 2019
  • E-copyright registration completed by authors: October 1, 2019
  • Camera-ready deadline: October 1, 2019
  • Workshop: November 17, 2019

Workshop Description

Data-intensive Workflows (a.k.a. scientific workflows) are routinely used in most scientific disciplines today, especially in the context of parallel and distributed computing. Workflows provide a systematic way of describing the analysis and rely on workflow management systems to execute the complex analyses on a variety of distributed resources. They are at the interface between end-users and computing infrastructures. With the dramatic increase of raw data volume in every domain, they play an even more critical role to assist scientists in organizing and processing their data and to leverage HPC or HTC resources, e.g., workflows played an important role in the discovery of Gravitational Waves.

This workshop focuses on the many facets of data-intensive workflow management systems, ranging from job execution to service management and the coordination of data, service and job dependencies. The workshop therefore covers a broad range of issues in the scientific workflow lifecycle that include: data-intensive workflows representation and enactment; designing workflow composition interfaces; workflow mapping techniques that may optimize the execution of the workflow; workflow enactment engines that need to deal with failures in the application and execution environment; and a number of computer science problems related to scientific workflows such as semantic technologies, compiler methods, fault detection and tolerance.


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13th Workflows in Support of Large-Scale Science – WORKS @SC18


Held in conjunction with SC18: The International Conference for High Performance Computing, Networking, Storage and Analysis

Data-intensive workflows (a.k.a. scientific workflows) are routinely used in most scientific disciplines today, especially in the context of high-performance, parallel and distributed computing. They provide a systematic way of describing a complex scientific process and rely on sophisticated workflow management systems to execute on a variety of parallel and distributed resources. With the dramatic increase of raw data volume in every domain, they play an even more critical role to assist scientists in organizing and processing their data and to leverage HPC or HTC resources, being at the interface between end-users and computing infrastructures.

This workshop focuses on the many facets of data-intensive workflow management systems, ranging from actual execution to service management and the coordination and optimization of data, service and job dependencies. The workshop covers a broad range of issues in the scientific workflow lifecycle that include: data-intensive workflows representation and enactment; designing workflow composition interfaces; workflow mapping techniques to optimize the execution of the workflow for different infrastructures; workflow enactment engines that need to deal with failures in the application and execution environment; and a number of computer science problems related to scientific workflows such as semantic technologies, compiler methods, scheduling and fault detection and tolerance.


Important Dates
Papers due: July 30 August 13, 2018
Paper Acceptance Notification: September 9 September 25, 2018

 

 

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Performance Analysis of an I/O-Intensive Workflow executing on Google Cloud and Amazon Web Services


Presentation held at the 18th Workshop on Advances in Parallel and Distributed Computational Models, 2016
Chicago, IL, USA – 30th IEEE International Parallel and Distributed Processing Symposium

Abstract – Scientific workflows have become the mainstream to conduct large-scale scientific research. In the meantime, cloud computing has emerged as an alternative computing paradigm. In this paper, we conduct an analysis of the performance of an I/O-intensive real scientific workflow on cloud environments using makespan (the turnaround time for a workflow to complete its execution) as the key performance metric. In particular, we assess the impact of varying the storage configurations on workflow performance when executing on Google Cloud and Amazon Web Services. We aim to understand the performance bottlenecks of the popular cloud-based execution environments. Experimental results show significant differences in application performance for different configurations. They also reveal that Amazon Web Services outperforms Google Cloud with equivalent application and system configurations. We then investigate the root cause of these results using provenance data and by benchmarking disk and network I/O on both infrastructures. Lastly, we also suggest modifications in the standard cloud storage APIs, which will reduce the makespan for I/O-intensive workflows.

 

Related Publication

  • [PDF] [DOI] H. Nawaz, G. Juve, R. Ferreira da Silva, and E. Deelman, “Performance Analysis of an I/O-Intensive Workflow executing on Google Cloud and Amazon Web Services,” in 18th Workshop on Advances in Parallel and Distributed Computational Models, 2016, p. 535–544.
    [Bibtex]
    @inproceedings{nawaz-apdcm-2016,
    author = {Nawaz, Hassan and Juve, Gideon and Ferreira da Silva, Rafael and Deelman, Ewa},
    title = {Performance Analysis of an I/O-Intensive Workflow executing on Google Cloud and Amazon Web Services},
    booktitle = {18th Workshop on Advances in Parallel and Distributed Computational Models},
    series = {APDCM'16},
    year = {2016},
    doi = {10.1109/IPDPSW.2016.90},
    pages = {535--544}
    }

 

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