Overall Objectives
Research Program
Application Domains
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
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Section: New Results

Control of Autonomid Parallelism on Software Transactional Memory

Participants : Naweiluo Zhou, Gwenaël Delaval [Univ. Grenoble Alpes, Associate Professor, Ctrl-A Inria team] , Bogdan Robu [Univ. Grenoble Alpes, Associate Professor, Gipsa Laboratory] , Eric Rutten [Inria, Rsearcher, Ctrl-A Inria team] , Jean-François Méhaut.

Parallel programs need to manage the trade-off between the time spent in synchronization and computation. A high parallelism may decrease computing time while increase synchronization cost among threads. A way to improve program performance is to adjust parallelism to balance conflicts among threads. However, there is no universal rule to decide the best parallelism for a program from an offline view. Furthermore, an offline tuning is error-prone. Hence, it becomes necessary to adopt a dynamic tuning-configuration strategy to better manage a STM system. Software Transactional Memory (STM) has emerged as a promising technique, which bypasses locks, to address syn- chronization issues through transactions. Autonomic computing offers designers a framework of methods and techniques to build automated systems with well-mastered behaviours. Its key idea is to implement feedback control loops to design safe, efficient and predictable controllers, which enable monitoring and adjusting controlled systems dynamically while keeping overhead low. We propose to design feedback control loops to automate the choice of parallelism level at runtime to diminish program execution time.

This work is funded by the Persyval laboratory (LabEx) and the HPES team 8.1.2. This scientific result is part of the Naweiluo Zhou's thesis. The thesis was defended in October 2016 [2]. This work was presented in the HPCS conference [25]. The paper was selected as best paper finalist. The Naweiluo Zhou's work is also presented at the ICAC conference.