Relaxed Schedulers Can Efficiently Parallelize Iterative Algorithms
| Autor: | Giorgi Nadiradze, Justin Kopinsky, Dan Alistarh, Trevor Brown |
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| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Computer science Concurrency 0102 computer and information sciences 02 engineering and technology 01 natural sciences 020202 computer hardware & architecture Scheduling (computing) 010201 computation theory & mathematics Computer Science - Data Structures and Algorithms Scalability 0202 electrical engineering electronic engineering information engineering Data Structures and Algorithms (cs.DS) Maximal independent set Computer Science::Operating Systems Algorithm |
| Zdroj: | Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing. |
| DOI: | 10.1145/3212734.3212756 |
| Popis: | There has been significant progress in understanding the parallelism inherent to iterative sequential algorithms: for many classic algorithms, the depth of the dependence structure is now well understood, and scheduling techniques have been developed to exploit this shallow dependence structure for efficient parallel implementations. A related, applied research strand has studied methods by which certain iterative task-based algorithms can be efficiently parallelized via relaxed concurrent priority schedulers. These allow for high concurrency when inserting and removing tasks, at the cost of executing superfluous work due to the relaxed semantics of the scheduler. In this work, we take a step towards unifying these two research directions, by showing that there exists a family of relaxed priority schedulers that can efficiently and deterministically execute classic iterative algorithms such as greedy maximal independent set (MIS) and matching. Our primary result shows that, given a randomized scheduler with an expected relaxation factor of $k$ in terms of the maximum allowed priority inversions on a task, and any graph on $n$ vertices, the scheduler is able to execute greedy MIS with only an additive factor of poly($k$) expected additional iterations compared to an exact (but not scalable) scheduler. This counter-intuitive result demonstrates that the overhead of relaxation when computing MIS is not dependent on the input size or structure of the input graph. Experimental results show that this overhead can be clearly offset by the gain in performance due to the highly scalable scheduler. In sum, we present an efficient method to deterministically parallelize iterative sequential algorithms, with provable runtime guarantees in terms of the number of executed tasks to completion. PODC 2018, pages 377-386 in proceedings |
| Databáze: | OpenAIRE |
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