This article presents GRAPE, a parallel (GRAPh) under bar (E) under bar ngine for graph computations. GRAPE differs from prior systems in its ability to parallelize existing sequential graph algorithms as a whole, wit...
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This article presents GRAPE, a parallel (GRAPh) under bar (E) under bar ngine for graph computations. GRAPE differs from prior systems in its ability to parallelize existing sequential graph algorithms as a whole, without the need for recasting the entire algorithm into a new model. Underlying GRAPE are a simple programming model and a principled approach based on fixpoint computation that starts with partial evaluation and uses an incremental function as the intermediate consequence operator. We show that users can devise existing sequential graph algorithms with minor additions, and GRAPE parallelizes the computation. Under a monotonic condition, the GRAPE parallelization guarantees to converge at correct answers as long as the sequentialalgorithms are correct. Moreover, we show that algorithms in MapReduce, BSP, and PRAM can be optimally simulated on GRAPE. In addition to the ease of programming, we experimentally verify that GRAPE achieves comparable performance to the state-of-the-art graph systems using real-life and synthetic graphs.
This article proposes an Adaptive Asynchronous Parallel (AAP) model for graph computations. As opposed to Bulk Synchronous Parallel (BSP) and Asynchronous Parallel (AP) models, AAP reduces both stragglers and stale co...
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This article proposes an Adaptive Asynchronous Parallel (AAP) model for graph computations. As opposed to Bulk Synchronous Parallel (BSP) and Asynchronous Parallel (AP) models, AAP reduces both stragglers and stale computations by dynamically adjusting relative progress of workers. We show that BSP, AP, and Stale Synchronous Parallel model (SSP) are special cases of AAP. Better yet, AAP optimizes parallel processing by adaptively switching among these models at different stages of a single execution. Moreover, employing the programming model of GRAPE, AAP aims to parallelize existing sequentialalgorithms based on simultaneous fixpoint computation with partial and incremental evaluation. Under a monotone condition, AAP guarantees to converge at correct answers if the sequentialalgorithms are correct. Furthermore, we show that AAP can optimally simulate MapReduce, PRAM, BSP, AP, and SSP. Using real-life and synthetic graphs, we experimentally verify that AAP outperforms BSP, AP, and SSP for a variety of graph computations.
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