Browsing by Subject "Automatic parallelization"

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Open Access
Auto-parallelizing stateful distributed streaming applications
(2012) Schneider, S.; Hirzel, M.; Gedik, Buğra; Wu, K. -L.
Streaming applications transform possibly infinite streams of data and often have both high throughput and low latency requirements. They are comprised of operator graphs that produce and consume data tuples. The streaming programming model naturally exposes task and pipeline parallelism, enabling it to exploit parallel systems of all kinds, including large clusters. However, it does not naturally expose data parallelism, which must instead be extracted from streaming applications. This paper presents a compiler and runtime system that automatically extract data parallelism for distributed stream processing. Our approach guarantees safety, even in the presence of stateful, selective, and userdefined operators. When constructing parallel regions, the compiler ensures safety by considering an operator's selectivity, state, partitioning, and dependencies on other operators in the graph. The distributed runtime system ensures that tuples always exit parallel regions in the same order they would without data parallelism, using the most efficient strategy as identified by the compiler. Our experiments using 100 cores across 14 machines show linear scalability for standard parallel regions, and near linear scalability when tuples are shuffled across parallel regions. Copyright © 2012 by the Association for Computing Machinery, Inc. (ACM).
Open Access
AutopaR: An Automatic Parallelization Tool for Recursive Calls
(IEEE, 2014-09) Kalender, Mert Emin; Mergenci, Cem; Öztürk, Özcan
Manycore systems are becoming more and more powerful with the integration of hundreds of cores on a single chip. However, writing parallel programs on these manycore systems has become a problem since the amount of available parallel tools and applications are limited. Although exploiting parallelism in software is possible, it requires different design decisions, significant programmer effort and is error prone. Different libraries and tools try to make the transition to parallelism easier, however there is no concrete system to make it transparent to software developer. To this end, our proposed tool is a step forward to improve the current state. Our approach, Autopar, specifically aims at achieving automatic parallelization of recursive applications using static program analysis. It first decides on the recursive functions of a given program. Then, it performs analysis and collects information about these recursive functions. Our analysis module automatically collects program information without requiring any modification in the program design or developer involvement. Finally, it achieves automatic parallelization by introducing necessary OpenMP pragmas in appropriate places in the application. © 2014 IEEE.