Paper Info
Title
Design and evaluation of the gemtc framework for GPU-enabled many-task computing
Abstract
We present the design and first performance and usability evaluation of GeMTC, a novel execution model and runtime system that enables accelerators to be programmed with many concurrent and independent tasks of potentially short or variable duration. With GeMTC, a broad class of such \"many-task\" applications can leverage the increasing number of accelerated and hybrid high-end computing systems. GeMTC overcomes the obstacles to using GPUs in a many-task manner by scheduling and launching independent tasks on hardware designed for SIMD-style vector processing. We demonstrate the use of a high-level MTC programming model (the Swift parallel dataflow language) to run tasks on many accelerators and thus provide a high-productivity programming model for the growing number of supercomputers that are accelerator-enabled. While still in an experimental stage, GeMTC can already support tasks of fine (subsecond) granularity and execute concurrent heterogeneous tasks on 86,000 independent GPU warps spanning 2.7M GPU threads on the Blue Waters supercomputer.
Year
DOI
Venue
2014
10.1145/2600212.2600228
HPDC
Keywords
Field
DocType
workflow,cuda,many-task computing,hybrid execution,concurrent programming,programming models,gpgpu,accelerators,execution models
Many-task computing,Programming paradigm,Supercomputer,Computer science,Parallel computing,Real-time computing,Dataflow,Execution model,General-purpose computing on graphics processing units,Vector processor,Distributed computing,Runtime system
Conference
Citations 
PageRank 
References 
14
0.91
28
Authors
8
Name
Order
Citations
PageRank
Scott J. Krieder1151.29
Justin M. Wozniak246435.32
Timothy G. Armstrong337321.73
Michael Wilde4140.91
Daniel S. Katz51496121.04
Benjamin Grimmer6223.55
Foster Ian7229382663.24
Raicu, Ioan82264129.28