Topic
Synchronization (computer science)
About: Synchronization (computer science) is a research topic. Over the lifetime, 43303 publications have been published within this topic receiving 559005 citations.
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25 Oct 2008
TL;DR: This paper presents and characterizes the Princeton Application Repository for Shared-Memory Computers (PARSEC), a benchmark suite for studies of Chip-Multiprocessors (CMPs), and shows that the benchmark suite covers a wide spectrum of working sets, locality, data sharing, synchronization and off-chip traffic.
Abstract: This paper presents and characterizes the Princeton Application Repository for Shared-Memory Computers (PARSEC), a benchmark suite for studies of Chip-Multiprocessors (CMPs). Previous available benchmarks for multiprocessors have focused on high-performance computing applications and used a limited number of synchronization methods. PARSEC includes emerging applications in recognition, mining and synthesis (RMS) as well as systems applications which mimic large-scale multithreaded commercial programs. Our characterization shows that the benchmark suite covers a wide spectrum of working sets, locality, data sharing, synchronization and off-chip traffic. The benchmark suite has been made available to the public.
3,514 citations
TL;DR: In this paper, a review of the Kuramoto model of coupled phase oscillators is presented, with a rigorous mathematical treatment, specific numerical methods, and many variations and extensions of the original model that have appeared in the last few years.
Abstract: Synchronization phenomena in large populations of interacting elements are the subject of intense research efforts in physical, biological, chemical, and social systems. A successful approach to the problem of synchronization consists of modeling each member of the population as a phase oscillator. In this review, synchronization is analyzed in one of the most representative models of coupled phase oscillators, the Kuramoto model. A rigorous mathematical treatment, specific numerical methods, and many variations and extensions of the original model that have appeared in the last few years are presented. Relevant applications of the model in different contexts are also included.
2,864 citations
01 May 1981
TL;DR: It is shown that it is possible to automatically synthesize the synchronization skeleton of a concurrent program from a Temporal Logic specification and it is believed that this approach may in the long run turn out to be quite practical.
Abstract: We have shown that it is possible to automatically synthesize the synchronization skeleton of a concurrent program from a Temporal Logic specification We believe that this approach may in the long run turn out to be quite practical Since synchronization skeletons are, in general, quite small, the potentially exponential behavior of our algorithm need not be an insurmountable obstacle Much additional research will be needed, however, to make the approach feasible in practice
2,333 citations
TL;DR: Synchronization of chaos refers to a process where two chaotic systems adjust a given property of their motion to a common behavior due to a coupling or to a forcing (periodical or noisy) as discussed by the authors.
2,266 citations
Proceedings Article•
12 Dec 2011TL;DR: In this paper, the authors present an update scheme called HOGWILD!, which allows processors access to shared memory with the possibility of overwriting each other's work, which achieves a nearly optimal rate of convergence.
Abstract: Stochastic Gradient Descent (SGD) is a popular algorithm that can achieve state-of-the-art performance on a variety of machine learning tasks. Several researchers have recently proposed schemes to parallelize SGD, but all require performance-destroying memory locking and synchronization. This work aims to show using novel theoretical analysis, algorithms, and implementation that SGD can be implemented without any locking. We present an update scheme called HOGWILD! which allows processors access to shared memory with the possibility of overwriting each other's work. We show that when the associated optimization problem is sparse, meaning most gradient updates only modify small parts of the decision variable, then HOGWILD! achieves a nearly optimal rate of convergence. We demonstrate experimentally that HOGWILD! outperforms alternative schemes that use locking by an order of magnitude.
1,939 citations