Browsing by Subject "Fission"
Now showing 1 - 3 of 3
- Results Per Page
- Sort Options
Item Open Access Pipelined fission for stream programs with dynamic selectivity and partitioned state(2014-12) Özsema, Habibe GüldamlaThere is an ever increasing rate of digital information available in the form of online data streams. In many application domains, high throughput processing of such data is a critical requirement for keeping up with the soaring input rates. Data stream processing is a computational paradigm that aims at addressing this challenge by processing data streams in an on-the-fly manner. In this thesis, we study the problem of automatically parallelizing data stream processing applications to improve throughput. The parallelization is automatic in the sense that stream programs are written sequentially by the application developers and are parallelized by the system. We adopt the asynchronous data flow model for our work, where operators often have dynamic selectivity and are stateful. We solve the problem of pipelined fission, in which the original sequential program is parallelized by taking advantage of both pipeline and data parallelism at the same time. Our solution supports partitioned stateful data parallelism with dynamic selectivity and is designed for shared-memory multi-core machines. We first develop a cost-based formulation to express pipelined fission as an optimization problem. The bruteforce solution of this problem takes a very long time for moderately sized stream programs. Accordingly, we develop a heuristic algorithm that can quickly, but approximately, solve this problem. We provide an extensive evaluation studying the performance of our solution, including simulations and experiments with an industrial-strength Data Stream Processing Systems (DSPS). Our results show good scalability for applications that contain sufficient parallelism, closeness to optimal performance for the algorithm.Item Open Access Pipelined fission for stream programs with dynamic selectivity and partitioned state(Academic Press, 2016) Gedik, B.; Özsema, H. G.; Öztürk, Ö.There is an ever increasing rate of digital information available in the form of online data streams. In many application domains, high throughput processing of such data is a critical requirement for keeping up with the soaring input rates. Data stream processing is a computational paradigm that aims at addressing this challenge by processing data streams in an on-the-fly manner, in contrast to the more traditional and less efficient store-and-then process approach. In this paper, we study the problem of automatically parallelizing data stream processing applications in order to improve throughput. The parallelization is automatic in the sense that stream programs are written sequentially by the application developers and are parallelized by the system. We adopt the asynchronous data flow model for our work, which is typical in Data Stream Processing Systems (DSPS), where operators often have dynamic selectivity and are stateful. We solve the problem of pipelined fission, in which the original sequential program is parallelized by taking advantage of both pipeline parallelism and data parallelism at the same time. Our pipelined fission solution supports partitioned stateful data parallelism with dynamic selectivity and is designed for shared-memory multi-core machines. We first develop a cost-based formulation that enables us to express pipelined fission as an optimization problem. The bruteforce solution of this problem takes a long time for moderately sized stream programs. Accordingly, we develop a heuristic algorithm that can quickly, but approximately, solve the pipelined fission problem. We provide an extensive evaluation studying the performance of our pipelined fission solution, including simulations as well as experiments with an industrial-strength DSPS. Our results show good scalability for applications that contain sufficient parallelism, as well as close to optimal performance for the heuristic pipelined fission algorithm.Item Open Access Tutorial: Stream processing optimizations(ACM, 2013) Schneider, S.; Hirzel, M.; Gedik, BuğraThis tutorial starts with a survey of optimizations for streaming applications. The survey is organized as a catalog that introduces uniform terminology and a common categorization of optimizations across disciplines, such as data management, programming languages, and operating systems. After this survey, the tutorial continues with a deep-dive into the fission optimization, which automatically transforms streaming applications for data-parallelism. Fis-sion helps an application improve its throughput by taking advantage of multiple cores in a machine, or, in the case of a distributed streaming engine, multiple machines in a cluster. While the survey of optimizations covers a wide range of work from the literature, the in-depth discussion of ission relies more heavily on the presenters' own research and experience in the area. The tutorial concludes with a discussion of open research challenges in the field of stream processing optimizations. Copyright © 2013 ACM.