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检索条件"主题词=GPU Query Optimization"

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Efficient gpu-accelerated Join optimization for Complex Queries 38

Efficient GPU-accelerated Join Optimization for Complex Quer...

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38th IEEE International Conference on Data Engineering (ICDE)

作者： Mageirakos, Vasilis Mancini, Riccardo Karthik, Srinivas Chandra, Bikash Ailamaki, Anastasia Univ Patras Patras Greece Scuola Super Sant Anna Pisa Italy Ecole Polytech Fed Lausanne Lausanne Switzerland RAW Labs SA Vaud Switzerland

ISBN: (数字)9781665408837

ISBN: (纸本)9781665408837

Analytics on modern data analytic and data warehouse systems often need to run large complex queries on increasingly complex database schemas. A lot of progress has been made on executing such complex queries using techniques like scale out query processing, hardware accelerators like gpus and code generation techniques. However, optimization of such queries remains a challenge. Existing optimal solutions either cannot be effectively parallelized, or are inefficient while doing a lot of unnecessary work. In this demonstration, we present our system, gpu-QO, which aims to demonstrate query optimization techniques for large analytical queries using gpus. We first demonstrate Massively Parallel Dynamic Programming (MPDP) - a novel query optimization technique that can run on gpus to generate optimal plans in a (massively) parallel and efficient manner. We then showcase IDP2-MPDP and UnionDP - two heuristic techniques, again using gpus, that can even optimize queries containing 1000s of joins. Furthermore, we compare our techniques with current state-of-the-art solutions, and demonstrate how our techniques can reduce optimization time for optimal solutions by nearly two orders of magnitude and produce much better query plans for heuristics (up to 7x).

关键词： Parallel query optimization gpu query optimization Dynamic programming

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Efficient Massively Parallel Join optimization for Large Queries 22

Efficient Massively Parallel Join Optimization for Large Que...

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International Conference on Management of Data (SIGMOD)

作者： Mancini, Riccardo Karthik, Srinivas Chandra, Bikash Mageirakos, Vasilis Ailamaki, Anastasia Scuola Super Sant Anna Pisa Italy Ecole Polytech Fed Lausanne Lausanne Switzerland Univ Patras Patras Greece RAW Labs SA Vaud Switzerland

ISBN: (纸本)9781450392495

Modern data analytical workloads often need to run queries over a large number of tables. An optimal query plan for such queries is crucial for being able to run these queries within acceptable time bounds. However, with queries involving many tables, finding the optimal join order becomes a bottleneck in query optimization. Due to the exponential nature of join order optimization, optimizers resort to heuristic solutions after a threshold number of tables. Our objective is two fold: (a) reduce the optimization time for generating optimal plans;and (b) improve the quality of the heuristic solution. In this paper, we propose a new massively parallel algorithm, MPDP, that can efficiently prune the large search space (via a novel plan enumeration technique) while leveraging the massive parallelism offered by modern hardware (Eg: gpus). When evaluated on real-world benchmark queries with PostgreSQL, MPDP is at least an order of magnitude faster compared to state-of-the-art techniques for large analytical queries. As a result, we can increase the heuristic-fall-back limit from 12 relations to 25 relations with the same time budget in PostgreSQL. Also, to handle queries with even larger number of tables, we augment MPDP to a well-known heuristic, IDP 2 (iterative DP version 2) and a novel heuristic UnionDP. By systematically exploring a much larger search space, these heuristics provides query plans that are up to 7 times cheaper as compared to the state-of-the-art techniques while being faster to compute.

关键词： Parallel query optimization gpu query optimization Dynamic Programming

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