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Creators/Authors contains: "Wu, Jiacheng"

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  1. ; (, Proceedings of the ACM on Management of Data)
    To achieve true scalability on massive datasets, a modern query engine needs to be able to take advantage of large, shared-memory, multicore systems.Binary joinsare conceptually easy to parallelize on a multicore system; however, several applications require a different approach to query evaluation, using a Worst-Case Optimal Join (WCOJ) algorithm. WCOJ is known to outperform traditional query plans for cyclic queries. However, there is no obvious adaptation of WCOJ to parallel architectures. The few existing systems that parallelizeWCOJ do this by partitioning only the top variable of theWCOJ algorithm. This leads to work skew (since some relations end up being read entirely by every thread), possible contention between threads (when the hierarchical trie index is built lazily, which is the case on most recent WCOJ systems), and exacerbates the redundant computations already existing in WCOJ. 
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    Free, publicly-accessible full text available June 17, 2026
  2. ; ; ; ; (, Conference on Innovative Data Systems Research)
    Cloud data centers are evolving fast. At the same time, today’s large-scale data analytics applications require non-trivial performance tuning that is often specific to the applications, workloads, and data center infrastructure. We propose TeShu, which makes network shuffling an extensible unified service layer common to all data analytics. Since an optimal shuffle depends on a myriad of factors, TeShu introduces parameterized shuffle templates, instantiated by accurate and efficient sampling that enables TeShu to dynamically adapt to different application workloads and data center layouts. Our preliminary experimental results show that TeShu efficiently enables shuffling optimizations that improve performance and adapt to a variety of data center network scenarios. 
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    Accepted Manuscript Full Text Available
  3. ; ; ; ; (, CIDR)
    Accepted Manuscript Full Text Available