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Paving the Road to Exascales with Many-Task Computing

This presentation delves into Many-Task Computing (MTC) as a bridge between High Performance Computing (HPC) and High Throughput Computing (HTC). It introduces key frameworks such as Falkon for task execution and Swift for parallel programming. The discussion covers critical topics like load balancing strategies, scheduling types (centralized, hierarchical, distributed), and provides insights into SimMatrix and MATRIX as robust solutions for MTC at exascale levels. The seminar highlights the importance of efficient task management for future computational endeavors.

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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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