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Paving the Road to Exascales with Many-Task Computing

Paving the Road to Exascales with Many-Task Computing. Speaker: Ke Wang Home page: http://datasys.cs.iit.edu/~kewang Supervisor: Ioan Raicu Data-Intensive Distributed Systems Laboratory Computer Science Department Illinois Institute of Technology November 14 th , 2012.

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Paving the Road to Exascales with Many-Task Computing

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  1. Paving the Road to Exascales with Many-Task Computing Speaker: Ke Wang Home page: http://datasys.cs.iit.edu/~kewang Supervisor: IoanRaicu Data-Intensive Distributed Systems Laboratory Computer Science Department Illinois Institute of Technology November 14th, 2012

  2. Many-Task Computing (MTC) • Bridge the gap between HPC and HTC • Applications structured as DAGs • Data dependencies will be files that are written to and read from a file system • Loosely coupled apps with HPC orientations • Falkon • Fast and Lightweight Task Execution Framework • http://datasys.cs.iit.edu/projects/Falkon/index.html • Swift • Parallel Programming System • http://www.ci.uchicago.edu/swift/index.php Paving the Road to Exascales with Many-Task Computing

  3. Load Balancing • the technique of distributing computational and communication loads evenly across processors of a parallel machine, or across nodes of a supercomputer • Different scheduling strategies • Centralized scheduling: poor scalability (Falkon, Slurm, Cobalt) • Hierarchical scheduling: moderate scalability (Falkon, Charm++) • Distributed scheduling: possible approach to exascales (Charm++) • Work Stealing: a distributed load balancing strategy • Starved processors steal tasks from overloaded ones • Various parameters affect performance: • Number of tasks to steal (half) • Number of neighbors (square root of number of all nodes) • Static or Dynamic random neighbors (Dynamic random neighbors) • Stealing poll interval (exponential back off) Paving the Road to Exascales with Many-Task Computing

  4. SimMatrix • light-weight and scalable discrete event SIMulatorfor MAny-Task computing execution fabRIc at eXascales • supports centralized (FIFO) and distributed (work scheduling) scheduling • has great scalability (millions of nodes, billions of cores, trillions of tasks) • future extensions: task dependency, work flow system simulation, different network topologies, data-aware scheduling Paving the Road to Exascales with Many-Task Computing

  5. MATRIX • a real implementation of distributed MAny-Task execution fabRIc at eXascales Paving the Road to Exascales with Many-Task Computing

  6. Acknowledgement • DataSys Laboratory • IoanRaicu • AnupamRajendran • Tonglin Li • Kevin Brandstatter • University of Chicago • Zhao Zhang

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