Inference and Declaration of Independence in Task-Parallel Programs

Foivos S. Zakkak, Dimitrios Chasapis, Polyvios Pratikakis, Angelos Bilas, Dimitrios S. Nikolopoulos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

236 Downloads (Pure)


The inherent difficulty of thread-based shared-memory programming has recently motivated research in high-level, task-parallel programming models. Recent advances of Task-Parallel models add implicit synchronization, where the system automatically detects and satisfies data dependencies among spawned tasks. However, dynamic dependence analysis incurs significant runtime overheads, because the runtime must track task resources and use this information to schedule tasks while avoiding conflicts and races.
We present SCOOP, a compiler that effectively integrates static and dynamic analysis in code generation. SCOOP combines context-sensitive points-to, control-flow, escape, and effect analyses to remove redundant dependence checks at runtime. Our static analysis can work in combination with existing dynamic analyses and task-parallel runtimes that use annotations to specify tasks and their memory footprints. We use our static dependence analysis to detect non-conflicting tasks and an existing dynamic analysis to handle the remaining dependencies. We evaluate the resulting hybrid dependence analysis on a set of task-parallel programs.
Original languageEnglish
Title of host publicationAdvanced Parallel Processing Technologies
Subtitle of host publication10th International Symposium, APPT 2013, Stockholm, Sweden, August 27-28, 2013, Revised Selected Papers
EditorsChenggang Wu, Albert Cohen
Number of pages16
ISBN (Electronic)978-3-642-45293-2
ISBN (Print)978-3-642-45292-5
Publication statusPublished - 2013
Event10th International Symposium, APPT 2013 - Sweden, Sweden
Duration: 27 Aug 201328 Aug 2013

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


Conference10th International Symposium, APPT 2013

Fingerprint Dive into the research topics of 'Inference and Declaration of Independence in Task-Parallel Programs'. Together they form a unique fingerprint.

Cite this