Implementation of an efficient parallel BDD package

Stornetta, T.; Brewer, Forrest

doi:10.1145/240518.240639

Cited by 53 publications

(13 citation statements)

References 12 publications

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“…Most parallel or distributed work on symbolic reachability analysis employs a vertical slicing scheme to parallelize BDD manipulations, where image computation jobs are spawned and queued on the individual workstations of the NOW [19,21,29], allowing the algorithm to overlap image computation. Figure 10 shows how an MDDs can be decomposed into three portions where x 5 and x 4 are selected as slicing variables (the corresponding MDD nodes are indicated with solid boxes).…”

Section: Related Workmentioning

confidence: 99%

“…For explicit reachability analysis or model checking, references [1,24,27] introduce algorithms that use the overall resources of a network of workstations (NOW). For symbolic reachability analysis or model checking, most works employ a vertical slicing scheme to parallelize BDD manipulations by spawning multiple image computations over the NOW, corresponding to distinct sets of paths through the BDD [19,21,28]. Algorithms following this scheme can overlap the application of the next-state function to sets of states encoded by different decision diagram node, i.e.…”

Section: Introductionmentioning

confidence: 99%

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Speculative Image Computation for Distributed Symbolic Reachability Analysis

Chung

Ciardo

2009

Journal of Logic and Computation

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The Saturation-style fixpoint iteration strategy for symbolic reachability analysis is particularly effective for globally asynchronous locally synchronous discrete-state systems. However, its inherently sequential nature makes it difficult to parallelize Saturation on a network workstations (NOW). We then propose the idea of using idle workstation time to perform speculative image computations. Since an unrestrained prediction may make excessive use of computational resources, we introduce a history-based approach to dynamically recognize image computation (event firing) patterns and explore only firings that conform to these patterns. In addition, we employ an implicit encoding for the patterns, so that the actual image computation history can be efficiently preserved. Experiments not only show that image speculation works on a realistic model, but also indicate that the use of an implicit encoding together with two heuristics results in a better informed speculation.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Speculative Image Computation for Distributed Symbolic Reachability Analysis

Chung

Ciardo

2009

Journal of Logic and Computation

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“…Distributed Dynamic Hashing (DDH) [4] offered an alternative approach to LH* while EH* [5] provided a distributed version of EH [17]. Although in a very specific application context, [18] have exploited a very similar concept to DPH, named two-level hashing. Distributed versions of several other classical data structures, such as trees [7,8] and even hybrid structures, such as hash-trees [19], have also been designed.…”

Section: Related Workmentioning

confidence: 99%

Distributed Paged Hash Tables

Rufino

Pina

Alves

et al. 2003

Lecture Notes in Computer Science

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Abstract. In this paper we present the design and implementation of DPH, a storage layer for cluster environments. DPH is a Distributed Data Structure (DDS) based on the distribution of a paged hash table. It combines main memory with file system resources across the cluster in order to implement a distributed dictionary that can be used for the storage of very large data sets with key based addressing techniques. The DPH storage layer is supported by a collection of cluster-aware utilities and services. Access to the DPH interface is provided by a user-level API. A preliminary performance evaluation shows promising results.

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“…The use of distributed processing to increase the speedup and capacity of model checking has recently begun to generate interest [5,29,22,1,27,20,15,30,24]. Distributed techniques that achieve these goals do so by exploiting the cumulative computational power and memory of a cluster of computers.…”

Section: Introductionmentioning

confidence: 99%

Achieving Speedups in Distributed Symbolic Reachability Analysis Through Asynchronous Computation

Grumberg

Heyman

Ifergan

et al. 2005

Lecture Notes in Computer Science

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Abstract. This paper presents a novel BDD-based distributed algorithm for reachability analysis which is completely asynchronous. Previous BDD-based distributed schemes are synchronous: they consist of interleaved rounds of computation and communication, in which the fastest machine (or one which is lightly loaded) must wait for the slowest one at the end of each round.We make two major contributions. First, the algorithm performs image computation and message transfer concurrently, employing non-blocking protocols in several layers of the communication and the computation infrastructures. As a result, regardless of the scale and type of the underlying platform, the maximal amount of resources can be utilized efficiently. Second, the algorithm incorporates an adaptive mechanism which splits the workload, taking into account the availability of free computational power. In this way, the computation can progress more quickly because, when more CPUs are available to join the computation, less work is assigned to each of them. Less load implies additional important benefits, such as better locality of reference, less overhead in compaction activities (such as reorder), and faster and better workload splitting.We implemented the new approach by extending a symbolic model checker from Intel. The effectiveness of the resulting scheme is demonstrated on a number of large industrial designs as well as public benchmark circuits, all known to be hard for reachability analysis. Our results show that the asynchronous algorithm enables efficient utilization of higher levels of parallelism. High speedups are reported, up to an order of magnitude, for computing reachability for models with higher memory requirements than was previously possible.

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Implementation of an efficient parallel BDD package

Cited by 53 publications

References 12 publications

Speculative Image Computation for Distributed Symbolic Reachability Analysis

Speculative Image Computation for Distributed Symbolic Reachability Analysis

Distributed Paged Hash Tables

Achieving Speedups in Distributed Symbolic Reachability Analysis Through Asynchronous Computation

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