TY - GEN
T1 - TGE
T2 - 2017 IEEE International Conference on Cluster Computing, CLUSTER 2017
AU - Choi, Jong Youl
AU - Logan, Jeremy
AU - Wolf, Matthew
AU - Ostrouchov, George
AU - Kurc, Tahsin
AU - Liu, Qing
AU - Podhorszki, Norbert
AU - Klasky, Scott
AU - Romanus, Melissa
AU - Sun, Qian
AU - Parashar, Manish
AU - Churchill, Randy Michael
AU - Chang, Cs
N1 - Funding Information:
This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/9/22
Y1 - 2017/9/22
N2 - Task mapping is an important problem in parallel and distributed computing. The goal in task mapping is to find an optimal layout of the processes of an application (or a task) onto a given network topology. We target this problem in the context of staging applications. A staging application consists of two or more parallel applications (also referred to as staging tasks) which run concurrently and exchange data over the course of computation. Task mapping becomes a more challenging problem in staging applications, because not only data is exchanged between the staging tasks, but also the processes of a staging task may exchange data with each other. We propose a novel method, called Task Graph Embedding (TGE), that harnesses the observable graph structures of parallel applications and network topologies. TGE employs a machine learning based algorithm to find the best representation of a graph, called an embedding, onto a space in which the task-To-processor mapping problem can be solved. We evaluate and demonstrate the effectiveness of TGE experimentally with the communication patterns extracted from runs of XGC, a large-scale fusion simulation code, on Titan.
AB - Task mapping is an important problem in parallel and distributed computing. The goal in task mapping is to find an optimal layout of the processes of an application (or a task) onto a given network topology. We target this problem in the context of staging applications. A staging application consists of two or more parallel applications (also referred to as staging tasks) which run concurrently and exchange data over the course of computation. Task mapping becomes a more challenging problem in staging applications, because not only data is exchanged between the staging tasks, but also the processes of a staging task may exchange data with each other. We propose a novel method, called Task Graph Embedding (TGE), that harnesses the observable graph structures of parallel applications and network topologies. TGE employs a machine learning based algorithm to find the best representation of a graph, called an embedding, onto a space in which the task-To-processor mapping problem can be solved. We evaluate and demonstrate the effectiveness of TGE experimentally with the communication patterns extracted from runs of XGC, a large-scale fusion simulation code, on Titan.
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U2 - 10.1109/CLUSTER.2017.67
DO - 10.1109/CLUSTER.2017.67
M3 - Conference contribution
AN - SCOPUS:85032621616
T3 - Proceedings - IEEE International Conference on Cluster Computing, ICCC
SP - 587
EP - 591
BT - Proceedings - 2017 IEEE International Conference on Cluster Computing, CLUSTER 2017
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 5 September 2017 through 8 September 2017
ER -