TY - GEN
T1 - JetLag
T2 - 2020 Conference on Practice and Experience in Advanced Research Computing: Catch the Wave, PEARC 2020
AU - Brandt, Steven R.
AU - Bigelow, Alex
AU - Sakin, Sayef Azad
AU - Williams, Katy
AU - Isaacs, Katherine E.
AU - Huck, Kevin
AU - Tohid, Rod
AU - Wagle, Bibek
AU - Shirzad, Shahrzad
AU - Kaiser, Hartmut
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/7/26
Y1 - 2020/7/26
N2 - 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.
AB - 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.
KW - array
KW - asynchronous
KW - cloud computing
KW - interactive computing
KW - performance tuning
KW - performance visualization
KW - research environment
UR - http://www.scopus.com/inward/record.url?scp=85089263666&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85089263666&partnerID=8YFLogxK
U2 - 10.1145/3311790.3396657
DO - 10.1145/3311790.3396657
M3 - Conference contribution
AN - SCOPUS:85089263666
T3 - ACM International Conference Proceeding Series
SP - 8
EP - 12
BT - PEARC 2020 - Practice and Experience in Advanced Research Computing 2020
PB - Association for Computing Machinery
Y2 - 27 July 2020 through 31 July 2020
ER -