Gossamer: A Lightweight Approach to Using Multicore Machines

Roback, Joseph A.; Andrews, Gregory R.

doi:10.1109/icpp.2010.12

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…OpenMP provides ordered as a directive for loop parallelization [21]. Gossamer introduced it as a general directive for task parallelism [26]. The examples in these papers show a single ordered call in each task or loop iteration.…”

Section: The Ordered Hintmentioning

confidence: 99%

Safe parallel programming using dynamic dependence hints

Ke¹,

Liu²,

Zhang

et al. 2011

Proceedings of the 2011 ACM International Conference on Object Oriented Programming Systems Languages and Applications

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Speculative parallelization divides a sequential program into possibly parallel tasks and permits these tasks to run in parallel if and only if they show no dependences with each other. The parallelization is safe in that a speculative execution always produces the same output as the sequential execution.In this paper, we present the dependence hint, an interface for a user to specify possible dependences between possibly parallel tasks. Dependence hints may be incorrect or incomplete but they do not change the program output. The interface extends Cytron's do-across and recent OpenMP ordering primitives and makes them safe and safely composable. We use it to express conditional and partial parallelism and to parallelize large-size legacy code. The prototype system is implemented as a software library. It is used to improve performance by nearly 10 times on average on current multicore machines for 8 programs including 5 SPEC benchmarks.

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Section: The Ordered Hintmentioning

confidence: 99%

Safe parallel programming using dynamic dependence hints

Ke¹,

Liu²,

Zhang

et al. 2011

Proceedings of the 2011 ACM International Conference on Object Oriented Programming Systems Languages and Applications

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“…There are an ever increasing number of parallel environments and language/runtime combinations such as Chapel [13], Cilk [12], OpenMP [4], Gossamer [23] and Intel's TBB [6] and Ct\RapidMind [3]. However, they don't provide automatic mechanisms for shared state protection, generally focusing instead on providing tools for the programmer to manually manage state.…”

Section: Related Workmentioning

confidence: 99%

Synchronization via scheduling

Best¹,

Mottishaw²,

Mustard³

et al. 2011

Proceedings of the 32nd ACM SIGPLAN Conference on Programming Language Design and Implementation

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Shared state access conflicts are one of the greatest sources of error for fine grained parallelism in any domain. Notoriously hard to debug, these conflicts reduce reliability and increase development time. The standard task graph model dictates that tasks with potential conflicting accesses to shared state must be linked by a dependency, even if there is no explicit logical ordering on their execution. In cases where it is difficult to understand if such implicit dependencies exist, the programmer often creates more dependencies than needed, which results in constrained graphs with large monolithic tasks and limited parallelism.We propose a new technique, Synchronization via Scheduling (SvS), that uses the results of static and dynamic code analysis to manage potential shared state conflicts by exposing the data accesses of each task to the scheduler. We present an in-depth performance analysis of SvS via examples from video games, our target domain, and show that SvS performs well in comparison to software transactional memory (TM) and fine grained mutexes.

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