Paper Info
Title
Integrating Transactions into the Data-Driven Multi-threading Model Using the TFlux Platform.
Abstract
The introduction of multi-core processors has renewed the interest in programming models which can efficiently exploit general purpose parallelism. Data-Flow is one such model which has demonstrated significant potential in the past. However, it is generally associated with functional styles of programming which do not deal well with shared mutable state. There have been a number of attempts to introduce state into Data-Flow models and functional languages but none have proved able to maintain the simplicity and efficiency of pure Data-Flow parallelism. Transactional memory is a concurrency control mechanism that simplifies sharing data when developing parallel applications while at the same time promises to deliver affordable performance. In this paper we report our experience of integrating Transactional Memory and Data-Flow within the TFlux Platform. The ability of the Data-Flow model to expose large amounts of parallelism is maintained while Transactional Memory provides simplified sharing of mutable data in those circumstances where it is important to the expression of the program. The isolation property of transactions ensures that the exploitation of Data-Flow parallelism is not compromised. In this study we extend the TFlux platform, a Data-Driven Multi-threading implementation, to support transactions. We achieve this by proposing new pragmas that allow the programmer to specify transactions. In addition we extend the runtime functionality by integrating a software transactional memory library with TFlux. To test the proposed system, we ported two applications that require transactional memory: Random Counter and Labyrinth an implementation of Lee’s parallel routing algorithm. Our results show good opportunities for scaling when using the integration of the two models.
Year
DOI
Venue
2016
10.1007/s10766-015-0369-2
DFM '11 Proceedings of the 2011 First Workshop on Data-Flow Execution Models for Extreme Scale Computing
Keywords
Field
DocType
Programming models, Future multi-cores, Transactional Memory, Data-Flow model, Data-Driven Multi-threading
Multithreading,Software transactional memory,Programmer,Data-driven,Functional programming,Concurrency control,Programming paradigm,Computer science,Parallel computing,Transactional memory,Distributed computing
Journal
Volume
Issue
ISSN
44
2
1573-7640
ISBN
Citations 
PageRank 
978-1-4673-0709-3
3
0.51
References 
Authors
20
4
Name
Order
Citations
PageRank
Andreas Diavastos194.48
Pedro Trancoso237743.79
Mikel Luján354046.40
Ian Watson4109266.10