JetLag: An Interactive, Asynchronous Array Computing Environment
JetLag: An Interactive, Asynchronous Array Computing Environment
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DOI：
10.1145/3311790.3396657
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发表时间：
2020-07
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通讯作者：
Steven R. Brandt;Alex Bigelow;Sayef Azad Sakin;Katy Williams;Katherine E. Isaacs;K. Huck;R. Tohid;Bibek Wagle;S. Shirzad;Hartmut Kaiser
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文献类型：
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作者：
Steven R. Brandt;Alex Bigelow;Sayef Azad Sakin;Katy Williams;Katherine E. Isaacs;K. Huck;R. Tohid;Bibek Wagle;S. Shirzad;Hartmut Kaiser
我们描述了一个称为喷气机的交互式计算环境。为客户预定和奇异图像服务器。诊断和修复性能相关的问题适用于各种数组计算任务，包括机器学习和探索性数据分析。
We describe an interactive computing environment called JetLag. JetLag implements the following features of Phylanx project: (1) Phylanx, a Python-based asynchronous array computing toolkit; (2) the APEX performance measurement library; (3) a performance visualization framework called Traveler; (4) the Tapis/Agave Science as a Service middleware; and (6) a container infrastructure that includes Docker-based Jupyter notebook for the client and a singularity image for the server. The running system starts with a user performing array computations on their workstation or laptop. If, at some point, the calculation the user is performing becomes sufficiently intensive or numerous, it can be packaged and sent to another machine where it will run (through the batch queue system if there is one), produce a result, and have that result sent back to the user’s local interface. Whether the calculation is local or remote, the user will be able to use APEX and Traveler to diagnose and fix performance related problems. The JetLag system is suitable for a variety of array computational tasks, including machine learning and exploratory data analysis.