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High-performance execution of service compositions: a multicore-aware engine design

机译:服务组合的高性能执行:多核感知引擎设计

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摘要

Although modern computer hardware offers an increasing number of processing elements organized inrnnonuniform memory access (NUMA) architectures, prevailing middleware engines for executing businessrnprocesses, workflows, and Web service compositions have not been optimized for properly exploiting thernabundant processing resources of such machines. Amongst others, factors limiting performance are inefficientrnthread scheduling by the operating system, which can result in suboptimal use of system memory andrnCPU caches, and sequential code sections that cannot take advantage of multiple available cores.rnIn this article, we study the performance of the JOpera process execution engine on recent multicorernmachines. We first evaluate its performance without any dedicated optimization for multicore hardware,rnshowing that additional cores do not significantly improve performance, although the engine has a multithreadedrndesign. Therefore, we apply optimizations on the basis of replication together with an improved,rnhardware-aware usage of the underlying resources such as NUMA nodes and CPU caches. Thanks to ourrnoptimizations, we achieve speedups from a factor of 2 up to a factor of 20 (depending on the target machine)rnwhen compared with a baseline execution ‘as is’.
机译:尽管现代计算机硬件提供了越来越多的以非均匀内存访问(NUMA)架构组织的处理元素,但尚未对用于执行业务流程,工作流和Web服务组合的流行中间件引擎进行优化,以正确利用此类机器的大量处理资源。除其他因素外,限制性能的因素包括操作系统的低效率线程调度,这可能导致系统内存和CPU高速缓存的使用不足,以及无法利用多个可用内核的顺序代码节。在本文中,我们研究了JOpera的性能。最近的multicorernmachines上的流程执行引擎。我们首先评估其性能,而没有对多核硬件进行任何专门的优化,这表明尽管引擎具有多线程设计,但其他内核并不能显着提高性能。因此,我们在复制的基础上进行优化,同时对诸如NUMA节点和CPU缓存之类的基础资源进行改进的硬件感知使用。借助我们的优化,与“按原样”执行基准相比,我们将加速从2倍提高到20倍(取决于目标计算机)。

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