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 Paving the Road to Exascales with Many-Task Computing1 Falkon  Fast and Lightweight Task Execution Framework  Swift  Parallel Programming System 

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 Computing2

3 light-weight and scalable discrete event SIMulator for 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

a real implementation of distributed MAny-Task execution fabRIc at eXascales Paving the Road to Exascales with Many-Task Computing4

DataSys Laboratory  Ioan Raicu  Anupam Rajendran  Tonglin Li  Kevin Brandstatter University of Chicago  Zhao Zhang