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The Power of Nested Parallelism in Big Data Processing – Hitting Three Flies with One Slap
Citation key GevayQM21
Author Gábor E. Gévay, Jorge-Arnulfo Quiané-Ruiz, Volker Markl
Year 2021
Journal SIGMOD
Note to be published
Abstract Many common data analysis tasks, such as performing hyperparameter optimization, processing a partitioned graph, and treating a matrix as a vector of vectors, offer natural opportunities for nested-parallel operations, i.e., launching parallel operations from inside other parallel operations. However, state-of-the-art dataflow engines, such as Spark and Flink, do not support nested parallelism. Users must implement workarounds, causing orders of magnitude slowdowns for their tasks, let alone the implementation effort. We present Matryoshka, a system that enables dataflow engines to support nested parallelism, even in the presence of control flow statements at inner nesting levels. Matryoshka achieves this via a novel two-phase flattening process, which translates nested-parallel programs to flat-parallel programs that can efficiently run on existing dataflow engines. The first phase introduces novel nesting primitives into the code, which allows for dynamic optimizations based on intermediate data characteristics in the second phase at run time. We validate our system using several common data analysis tasks, such as PageRank and K-means. The results show the superiority of Matryoshka over the state-of-the-art approaches (the DIQL system as well as the outer- and inner-parallel workarounds)to support nested parallelism in dataflow engines.
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