@inproceedings{5963, abstract = {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.}, author = {Alistarh, Dan-Adrian and Brown, Trevor A and Kopinsky, Justin and Nadiradze, Giorgi}, booktitle = {Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing - PODC '18}, isbn = {9781450357951}, location = {Egham, United Kingdom}, pages = {377--386}, publisher = {ACM Press}, title = {{Relaxed schedulers can efficiently parallelize iterative algorithms}}, doi = {10.1145/3212734.3212756}, year = {2018}, } @inproceedings{5965, abstract = {Relaxed concurrent data structures have become increasingly popular, due to their scalability in graph processing and machine learning applications (\citeNguyen13, gonzalez2012powergraph ). Despite considerable interest, there exist families of natural, high performing randomized relaxed concurrent data structures, such as the popular MultiQueue~\citeMQ pattern for implementing relaxed priority queue data structures, for which no guarantees are known in the concurrent setting~\citeAKLN17. Our main contribution is in showing for the first time that, under a set of analytic assumptions, a family of relaxed concurrent data structures, including variants of MultiQueues, but also a new approximate counting algorithm we call the MultiCounter, provides strong probabilistic guarantees on the degree of relaxation with respect to the sequential specification, in arbitrary concurrent executions. We formalize these guarantees via a new correctness condition called distributional linearizability, tailored to concurrent implementations with randomized relaxations. Our result is based on a new analysis of an asynchronous variant of the classic power-of-two-choices load balancing algorithm, in which placement choices can be based on inconsistent, outdated information (this result may be of independent interest). We validate our results empirically, showing that the MultiCounter algorithm can implement scalable relaxed timestamps.}, author = {Alistarh, Dan-Adrian and Brown, Trevor A and Kopinsky, Justin and Li, Jerry Z. and Nadiradze, Giorgi}, booktitle = {Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures - SPAA '18}, isbn = {9781450357999}, location = {Vienna, Austria}, pages = {133--142}, publisher = {ACM Press}, title = {{Distributionally linearizable data structures}}, doi = {10.1145/3210377.3210411}, year = {2018}, } @inproceedings{5967, abstract = {The Big Match is a multi-stage two-player game. In each stage Player 1 hides one or two pebbles in his hand, and his opponent has to guess that number; Player 1 loses a point if Player 2 is correct, and otherwise he wins a point. As soon as Player 1 hides one pebble, the players cannot change their choices in any future stage. Blackwell and Ferguson (1968) give an ε-optimal strategy for Player 1 that hides, in each stage, one pebble with a probability that depends on the entire past history. Any strategy that depends just on the clock or on a finite memory is worthless. The long-standing natural open problem has been whether every strategy that depends just on the clock and a finite memory is worthless. We prove that there is such a strategy that is ε-optimal. In fact, we show that just two states of memory are sufficient. }, author = {Hansen, Kristoffer Arnsfelt and Ibsen-Jensen, Rasmus and Neyman, Abraham}, booktitle = {Proceedings of the 2018 ACM Conference on Economics and Computation - EC '18}, isbn = {9781450358293}, location = {Ithaca, NY, United States}, pages = {149--150}, publisher = {ACM Press}, title = {{The Big Match with a clock and a bit of memory}}, doi = {10.1145/3219166.3219198}, year = {2018}, } @inproceedings{5966, abstract = {The transactional conflict problem arises in transactional systems whenever two or more concurrent transactions clash on a data item. While the standard solution to such conflicts is to immediately abort one of the transactions, some practical systems consider the alternative of delaying conflict resolution for a short interval, which may allow one of the transactions to commit. The challenge in the transactional conflict problem is to choose the optimal length of this delay interval so as to minimize the overall running time penalty for the conflicting transactions. In this paper, we propose a family of optimal online algorithms for the transactional conflict problem. Specifically, we consider variants of this problem which arise in different implementations of transactional systems, namely "requestor wins'' and "requestor aborts'' implementations: in the former, the recipient of a coherence request is aborted, whereas in the latter, it is the requestor which has to abort. Both strategies are implemented by real systems. We show that the requestor aborts case can be reduced to a classic instance of the ski rental problem, while the requestor wins case leads to a new version of this classical problem, for which we derive optimal deterministic and randomized algorithms. Moreover, we prove that, under a simplified adversarial model, our algorithms are constant-competitive with the offline optimum in terms of throughput. We validate our algorithmic results empirically through a hardware simulation of hardware transactional memory (HTM), showing that our algorithms can lead to non-trivial performance improvements for classic concurrent data structures.}, author = {Alistarh, Dan-Adrian and Haider, Syed Kamran and Kübler, Raphael and Nadiradze, Giorgi}, booktitle = {Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures - SPAA '18}, isbn = {9781450357999}, location = {Vienna, Austria}, pages = {383--392}, publisher = {ACM Press}, title = {{The transactional conflict problem}}, doi = {10.1145/3210377.3210406}, year = {2018}, } @article{5975, abstract = {We consider the recent formulation of the algorithmic Lov ́asz Local Lemma [N. Har-vey and J. Vondr ́ak, inProceedings of FOCS, 2015, pp. 1327–1345; D. Achlioptas and F. Iliopoulos,inProceedings of SODA, 2016, pp. 2024–2038; D. Achlioptas, F. Iliopoulos, and V. Kolmogorov,ALocal Lemma for Focused Stochastic Algorithms, arXiv preprint, 2018] for finding objects that avoid“bad features,” or “flaws.” It extends the Moser–Tardos resampling algorithm [R. A. Moser andG. Tardos,J. ACM, 57 (2010), 11] to more general discrete spaces. At each step the method picks aflaw present in the current state and goes to a new state according to some prespecified probabilitydistribution (which depends on the current state and the selected flaw). However, the recent formu-lation is less flexible than the Moser–Tardos method since it requires a specific flaw selection rule,whereas the algorithm of Moser and Tardos allows an arbitrary rule (and thus can potentially beimplemented more efficiently). We formulate a new “commutativity” condition and prove that it issufficient for an arbitrary rule to work. It also enables an efficient parallelization under an additionalassumption. We then show that existing resampling oracles for perfect matchings and permutationsdo satisfy this condition.}, author = {Kolmogorov, Vladimir}, issn = {1095-7111}, journal = {SIAM Journal on Computing}, number = {6}, pages = {2029--2056}, publisher = {Society for Industrial & Applied Mathematics (SIAM)}, title = {{Commutativity in the algorithmic Lovász local lemma}}, doi = {10.1137/16m1093306}, volume = {47}, year = {2018}, }