Making time-stepped applications tick in the cloud

TY - GEN

T1 - Making time-stepped applications tick in the cloud

AU - Zou, Tao

AU - Wang, Guozhang

AU - Vaz Salles, Marcos António

AU - Bindel, David

AU - Demers, Alan

AU - Gehrke, Johannes

AU - White, Walker

N1 - Conference code: 2

PY - 2011

Y1 - 2011

N2 - Scientists are currently evaluating the cloud as a new platform. Many important scientific applications, however, perform poorly in the cloud. These applications proceed in highly parallel discrete time-steps or "ticks," using logical synchronization barriers at tick boundaries. We observe that network jitter in the cloud can severely increase the time required for communication in these applications, significantly increasing overall running time. In this paper, we propose a general parallel framework to process time-stepped applications in the cloud. Our framework exposes a high-level, data-centric programming model which represents application state as tables and dependencies between states as queries over these tables. We design a jitter-tolerant runtime that uses these data dependencies to absorb latency spikes by (1) carefully scheduling computation and (2) replicating data and computation. Our data-driven approach is transparent to the scientist and requires little additional code. Our experiments show that our methods improve performance up to a factor of three for several typical timestepped applications.

AB - Scientists are currently evaluating the cloud as a new platform. Many important scientific applications, however, perform poorly in the cloud. These applications proceed in highly parallel discrete time-steps or "ticks," using logical synchronization barriers at tick boundaries. We observe that network jitter in the cloud can severely increase the time required for communication in these applications, significantly increasing overall running time. In this paper, we propose a general parallel framework to process time-stepped applications in the cloud. Our framework exposes a high-level, data-centric programming model which represents application state as tables and dependencies between states as queries over these tables. We design a jitter-tolerant runtime that uses these data dependencies to absorb latency spikes by (1) carefully scheduling computation and (2) replicating data and computation. Our data-driven approach is transparent to the scientist and requires little additional code. Our experiments show that our methods improve performance up to a factor of three for several typical timestepped applications.

U2 - 10.1145/2038916.2038936

DO - 10.1145/2038916.2038936

M3 - Article in proceedings

SN - 978-1-4503-0976-9

BT - Proceedings of the 2nd ACM Symposium on Cloud Computing

PB - Association for Computing Machinery

T2 - 2nd ACM Symposium on Cloud Computing

Y2 - 26 October 2011 through 28 October 2011

ER -