Optimization Of Software Transactional Memory Through Linear Regression And Decision Tree
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Optimization of Software Transactional Memory Through Linear Regression and Decision Tree
Software Transactional Memory (STM) is a promising paradigm that facilitates programming for shared memory multiprocessors. In STM programs, synchronization of accesses to the shared memory locations is fully handled by STM library and does not require any intervention by programmers. While STM eases parallel programming, it results in run-time overhead which increases execution time of certain applications. In this thesis, we focus on overhead of STM and propose optimization techniques to enhance speed of STM applications. In particular, we focus on size of transaction, read-set, and write-set and show that execution time of applications significantly changes by varying these parameters. Optimizing these parameters manually is a time consuming process and requires significant labor work. We exploit Linear Regression (LR) and propose an optimization technique that decides on these parameters automatically. We further enhance this technique by using decision tree. The decision tree improves accuracy of predictions by selecting appropriate LR model for a given transaction. We evaluate our optimization techniques using a set of benchmarks from Stamp, NAS and DiscoPoP benchmark suites. Our experimental results reveal that LR and decision tree together are able to improve performance of STM programs up to 54.8%.
Algorithms and Architectures for Parallel Processing
This four volume set LNCS 9528, 9529, 9530 and 9531 constitutes the refereed proceedings of the 15th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2015, held in Zhangjiajie, China, in November 2015. The 219 revised full papers presented together with 77 workshop papers in these four volumes were carefully reviewed and selected from 807 submissions (602 full papers and 205 workshop papers). The first volume comprises the following topics: parallel and distributed architectures; distributed and network-based computing and internet of things and cyber-physical-social computing. The second volume comprises topics such as big data and its applications and parallel and distributed algorithms. The topics of the third volume are: applications of parallel and distributed computing and service dependability and security in distributed and parallel systems. The covered topics of the fourth volume are: software systems and programming models and performance modeling and evaluation.
Dependability Engineering
Author: Fausto Pedro García Márquez
language: en
Publisher: BoD – Books on Demand
Release Date: 2018-06-06
The new technology and system communication advances are being employed in any system, being more complex. The system dependability considers the technical complexity, size, and interdependency of the system. The stochastic characteristic together with the complexity of the systems as dependability requires to be under control the Reliability, Availability, Maintainability, and Safety (RAMS). The dependability contemplates, therefore, the faults/failures, downtimes, stoppages, worker errors, etc. Dependability also refers to emergent properties, i.e., properties generated indirectly from other systems by the system analyzed. Dependability, understood as general description of system performance, requires advanced analytics that are considered in this book. Dependability management and engineering are covered with case studies and best practices. The diversity of the issues will be covered from algorithms, mathematical models, and software engineering, by design methodologies and technical or practical solutions. This book intends to provide the reader with a comprehensive overview of the current state of the art, case studies, hardware and software solutions, analytics, and data science in dependability engineering.