8,386 research outputs found
Handling Parallelism in a Concurrency Model
Programming models for concurrency are optimized for dealing with
nondeterminism, for example to handle asynchronously arriving events. To shield
the developer from data race errors effectively, such models may prevent shared
access to data altogether. However, this restriction also makes them unsuitable
for applications that require data parallelism. We present a library-based
approach for permitting parallel access to arrays while preserving the safety
guarantees of the original model. When applied to SCOOP, an object-oriented
concurrency model, the approach exhibits a negligible performance overhead
compared to ordinary threaded implementations of two parallel benchmark
programs.Comment: MUSEPAT 201
Adaptive Transactional Memories: Performance and Energy Consumption Tradeoffs
Energy efficiency is becoming a pressing issue, especially in large data centers where it entails, at the same time, a non-negligible management cost, an enhancement of hardware fault probability, and a significant environmental footprint. In this paper, we study how Software Transactional Memories (STM) can provide benefits on both power saving and the overall applications’ execution performance. This is related to the fact that encapsulating shared-data accesses within transactions gives the freedom to the STM middleware to both ensure consistency and reduce the actual data contention, the latter having been shown to affect the overall power needed to complete the application’s execution.
We have selected a set of self-adaptive extensions to existing STM middlewares (namely, TinySTM and R-STM) to prove how self-adapting computation can capture the actual degree of parallelism and/or logical contention on shared data in a better way, enhancing even more the intrinsic benefits provided by STM. Of course, this benefit comes at a cost, which is the actual execution time required by the proposed approaches to precisely tune the execution parameters for reducing power consumption and enhancing execution performance. Nevertheless, the results hereby provided show that adaptivity is a strictly necessary requirement to reduce energy consumption in STM systems: Without it, it is not possible to reach any acceptable level of energy efficiency at all
Kevoree Modeling Framework (KMF): Efficient modeling techniques for runtime use
The creation of Domain Specific Languages(DSL) counts as one of the main
goals in the field of Model-Driven Software Engineering (MDSE). The main
purpose of these DSLs is to facilitate the manipulation of domain specific
concepts, by providing developers with specific tools for their domain of
expertise. A natural approach to create DSLs is to reuse existing modeling
standards and tools. In this area, the Eclipse Modeling Framework (EMF) has
rapidly become the defacto standard in the MDSE for building Domain Specific
Languages (DSL) and tools based on generative techniques. However, the use of
EMF generated tools in domains like Internet of Things (IoT), Cloud Computing
or Models@Runtime reaches several limitations. In this paper, we identify
several properties the generated tools must comply with to be usable in other
domains than desktop-based software systems. We then challenge EMF on these
properties and describe our approach to overcome the limitations. Our approach,
implemented in the Kevoree Modeling Framework (KMF), is finally evaluated
according to the identified properties and compared to EMF.Comment: ISBN 978-2-87971-131-7; N° TR-SnT-2014-11 (2014
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