Performance and modularity benefits of message-driven execution

Gürsoy, Attila; Kalé, Laxmikant V.

doi:10.1016/j.jpdc.2004.03.006

Cited by 17 publications

(12 citation statements)

References 45 publications

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“…Charm++ and MPI have both been described extensively, and their relative merits and deficiencies have been explored [20,13,26,2,14,4]. Therefore we will not describe them further except to say that both have proven suitable for developing complex parallel applications.…”

Section: A Multi-paradigm Runtime Systemmentioning

confidence: 99%

A Case Study in Tightly Coupled Multi-paradigm Parallel Programming

Chakravorty

Becker

Wilmarth

et al. 2008

Languages and Compilers for Parallel Computing

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Abstract. Programming paradigms are designed to express algorithms elegantly and efficiently. There are many parallel programming paradigms, each suited to a certain class of problems. Selecting the best parallel programming paradigm for a problem minimizes programming effort and maximizes performance. Given the increasing complexity of parallel applications, no one paradigm may be suitable for all components of an application. Today, most parallel scientific applications are programmed with a single paradigm and the challenge of multi-paradigm parallel programming remains unmet in the broader community. We believe that each component of a parallel program should be programmed using the most suitable paradigm. Furthermore, it is not sufficient to simply bolt modules together: programmers should be able to switch between paradigms easily, and resource management across paradigms should be automatic. We present a pre-existing adaptive runtime system (ARTS) and show how it can be used to meet these challenges by allowing the simultaneous use of multiple parallel programming paradigms and supporting resource management across all of them. We discuss the implementation of some common paradigms within the ARTS and demonstrate the use of multiple paradigms within our feature-rich unstructured mesh framework. We show how this approach boosts performance and productivity for an application developed using this framework.

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Section: A Multi-paradigm Runtime Systemmentioning

confidence: 99%

A Case Study in Tightly Coupled Multi-paradigm Parallel Programming

Chakravorty

Becker

Wilmarth

et al. 2008

Languages and Compilers for Parallel Computing

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“…The asynchronous messaging meta-machine that is the foundation of ThreadMill's communication and concurrency semantics is related to approaches such as Actors [1], messagedriven execution [13] and processor virtualization [14]. While these approaches are associated with programming languages and programming language constructs, ThreadMill employs similar mechanisms to define (and implement) its underlying operational semantics.…”

Section: Related Literaturementioning

confidence: 99%

“…To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. ICMI'04, October [13][14][15]2004 interfaces, or in better supporting groups of people that want to collaborate.…”

Section: Introductionmentioning

confidence: 99%

The ThreadMill architecture for stream-oriented human communication analysis applications

Barthelmess

Ellis

2004

Proceedings of the 6th International Conference on Multimodal Interfaces

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This work introduces a new component software architecture -ThreadMill -whose main purpose is to facilitate the development of applications in domains where high volumes of streamed data need to be efficiently analyzed. It focuses particularly on applications that target the analysis of human communication e.g. in speech and gesture recognition. Applications in this domain usually employ costly signal processing techniques, but offer in many cases ample opportunities for concurrent execution in many different phases. ThreadMill's abstractions facilitate the development of applications that take advantage of this potential concurrency by hiding the complexity of parallel and distributed programming. As a result, ThreadMill applications can be made to run unchanged on a wide variety of execution environments, ranging from a single-processor machine to a cluster of multi-processor nodes. The architecture is illustrated by an implementation of a tracker for hands and face of American Sign Language signers that uses a parallel and concurrent version of the Joint Likelihood Filter method.

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“…communicating between each other. The runtime system activates the entity when the data it is waiting for are available [15]. The data, control information, and actions are transferred in messages.…”

Section: Introductionmentioning

confidence: 99%

Exploiting controlled-grained parallelism in message-driven stream programs

Feng

Talpur

et al. 2014

J Supercomput

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With the increasing amount of parallelism obtainable on multicore platforms, stream programming has been proposed as an effective solution for exposing distributed parallelization. Nonetheless, a pressing demand of scheduling task and data parallelism in stream programming exists that can accomplish robust multicore performance in the face of varying application characteristics. This paper addresses the problem of scheduling task and data parallelism in stream programming. We present StreamMDE, an asynchronous concurrency stream programming framework which offers a novel parallel programming model for scheduling task and data parallelism in the message-driven execution paradigm. A key property of this framework is exposing controlled-grained parallelism, which allows us to control the granularity of task and data parallelism in stream graph. Our empirical evaluation of StreamMDE shows that higher efficiency of mixed task and data parallelism in stream programming can be exploited with the appropriate granularity control. The framework bridges the gap between the parallel scale and the architecture of stream programs and facilitates in designing and coding stream features in different schedules.

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Performance and modularity benefits of message-driven execution

Cited by 17 publications

References 45 publications

A Case Study in Tightly Coupled Multi-paradigm Parallel Programming

A Case Study in Tightly Coupled Multi-paradigm Parallel Programming

The ThreadMill architecture for stream-oriented human communication analysis applications

Exploiting controlled-grained parallelism in message-driven stream programs

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