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Incrementally Parallelizing Database Transactions with Thread-Level Speculation

机译:通过线程级推测逐步并行化数据库事务

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With the advent of chip multiprocessors, exploiting intratransaction parallelism in database systems is an attractive way of improving transaction performance. However, exploiting intratransaction parallelism is difficult for two reasons: first, significant changes are required to avoid races or conflicts within the DBMS; and second, adding threads to transactions requires a high level of sophistication from transaction programmers. In this article we show how dividing a transaction into speculative threads solves both problems-it minimizes the changes required to the DBMS, and the details of parallelization are hidden from the transaction programmer. Our technique requires a limited number of small, localized changes to a subset of the low-level data structures in the DBMS. Through this method of incrementally parallelizing transactions, we can dramatically improve performance: on a simulated four-processor chip-multiprocessor, we improve the response time by 44-66% for three of the five TPC-C transactions, assuming the availability of idle processors.
机译:随着芯片多处理器的出现,在数据库系统中利用事务内并行性是提高事务性能的一种有吸引力的方法。但是,利用事务内并行性很困难,原因有两个:首先,需要进行重大更改才能避免DBMS内部的竞争或冲突。其次,向事务中添加线程需要事务程序员的高度精通。在本文中,我们将展示如何将事务划分为推测性线程来解决这两个问题-它将对DBMS的更改减至最少,并且并行化的详细信息对事务程序员而言是隐藏的。我们的技术需要对DBMS中的底层数据结构的子集进行少量的局部更改。通过这种使事务并行化的方法,我们可以显着提高性能:在模拟的四处理器芯片多处理器上,假设空闲处理器可用,我们对五个TPC-C事务中的三个事务的响应时间提高了44-66%。 。

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