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Exploiting Asynchrony for Performance and Fault Tolerance in Distributed Graph Processing

  • Author(s): Vora, Keval
  • Advisor(s): Gupta, Rajiv
  • et al.
Abstract

While various iterative graph algorithms can be expressed via asynchronous parallelism, lack of its proper understanding limits the performance benefits that can be achieved via informed relaxations. In this thesis, we capture the algorithmic intricacies and execution semantics that enable us to improve asynchronous processing and allow us to reason about semantics of asynchronous execution while leveraging its benefits. To this end, we specify the asynchronous processing model in a distributed setting by identifying key properties of read-write dependences and ordering of reads that expose the set of legal executions of an asynchronous program. And then, we develop techniques to exploit the availability of multiple legal executions by choosing faster executions that reduce communication and computation while processing static and dynamic graphs.

For static graphs, we first develop a relaxed consistency protocol to allow the use of stale values during processing in order to eliminate long latency communication operations by up to 58%, hence accelerating the overall processing by a factor of 2. Then, to efficiently handle machine failures, we present a light-weight confined recovery strategy that quickly constructs an alternate execution state that may be different from any previously encountered program state, but is nevertheless a legal state that guarantees correct asynchronous semantics upon resumption of execution. Our confined recovery strategy enables the processing to finish 1.5-3.2x faster compared to the traditional recovery mechanism when failures impact 1-6 machines of a 16 machine cluster.

We further design techniques based on computation reordering and incremental computation to amortize the computation and communication costs incurred in processing evolving graphs, hence accelerating their processing by up to 10x. Finally, to process streaming graphs, we develop a dynamic dependence based incremental processing technique that identifies the minimal set of computations required to calculate the change in results that reflects the mutation in graph structure. We show that this technique not only produces correct results, but also improves processing by 8.5-23.7x.

Finally, we demonstrate the efficacy of asynchrony beyond distributed setting by leveraging it to design dynamic partitions that eliminate wasteful disk I/O involved in out-of-core graph processing by 25-76%.

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