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SCHEDULE: NOV 16-21, 2014
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Compiler Techniques for Massively Scalable Implicit Task Parallelism
SESSION: Compiler Analysis and Optimization
EVENT TYPE: Papers
TIME: 4:00PM - 4:30PM
SESSION CHAIR: Milind Kulkarni
AUTHOR(S):Timothy G. Armstrong, Justin M. Wozniak, Michael Wilde, Ian Foster
ROOM:393-94-95
ABSTRACT:
Swift/T is a high-level language for writing concise, deterministic scripts that compose serial or parallel codes implemented in lower-level programming models into large-scale parallel applications. It executes using a data-driven task parallel execution model that is
capable of orchestrating millions of concurrently executing asynchronous tasks on
homogeneous or heterogeneous resources.
Producing code that efficiently executes at this scale requires sophisticated compiler transformations: poorly optimized code inhibits scaling with excessive synchronization and communication. We present a comprehensive set of compiler techniques for data-driven task parallelism, including novel compiler optimizations and intermediate representations. We report application benchmark studies, including unbalanced tree search and simulated annealing, and demonstrate that our techniques greatly reduce communication overhead and enable extreme scalability, distributing up to 612 million dynamically load balanced tasks per second at scales of up to 262,144 cores without explicit parallelism, synchronization,
or load balancing in application code.
Chair/Author Details:
Milind Kulkarni (Chair) - Purdue University
Timothy G. Armstrong - University of Chicago
Justin M. Wozniak - Argonne National Laboratory
Michael Wilde - University of Chicago and Argonne National Laboratory
Ian Foster - University of Chicago and Argonne National Laboratory
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