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Parallelization and scheduling of data intensive particle physics analysis jobs on clusters of PCs

机译：数据密集型粒子物理分析工作中的PCS集群的并行化和调度

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摘要

Summary form only given. Scheduling policies are proposed for parallelizing data intensive particle physics analysis applications on computer clusters. Particle physics analysis jobs require the analysis of tens of thousands of particle collision events, each event requiring typically 200ms processing time and 600KB of data. Many jobs are launched concurrently by a large number of physicists. At a first view, particle physics jobs seem to be easy to parallelize, since particle collision events can be processed independently one from another. However, since large amounts of data need to be accessed, the real challenge resides in making an efficient use of the underlying computing resources. We propose several job parallelization and scheduling policies aiming at reducing job processing times and at increasing the sustainable load of a cluster server. Since particle collision events are usually reused by several jobs, cache based job splitting strategies considerably increase cluster utilization and reduce job processing times. Compared with straightforward job scheduling on a processing form, cache based first in first out job splitting speeds up average response times by an order of magnitude and reduces job waiting times in the system's queues from hours to minutes. By scheduling the jobs out of order, according to the availability of their collision events in the node disk caches, response times are further reduced, especially at high loads. In the delayed scheduling policy, job requests are accumulated during a time period, divided into subjob requests according to a parameterizable subjob size, and scheduled at the beginning of the next time period according to the availability of their data segments within the disk node caches. Delayed scheduling sustains a load close to the maximal theoretically sustainable load of a cluster, but at the cost of longer average response times. Finally we propose an adaptive delay scheduling approach, where the scheduling delay is adapted to the current load. This last scheduling approach sustains very high loads and offers low response times at normal loads.
机译：摘要表格仅给出。调度策略提出了并行数据上的计算机集群密集的粒子物理分析中的应用。粒子物理分析工作需要成千上万的粒子碰撞事件的分析，每个事件通常需要200ms的处理时间和数据的600KB。许多工作是由大量的物理学家同时启动。在第一视图中，粒子物理工作似乎容易并行化，由于颗粒碰撞事件可以独立地处理一个从另一个。然而，由于要访问大量数据的需求，真正的挑战所在在做一个有效利用底层计算资源。我们建议旨在减少加工时间并提高集群服务器的负载持续几个任务并行化和调度策略。由于粒子碰撞事件通常是由几个工作重用，基于缓存的工作拆分策略大大增加群集利用率，减少加工时间。与处理表单上简单的作业调度相比，高速缓存第一个基于入先出作业分割由一个数量级加快平均响应时间与从几小时缩短到几分钟减少了系统中的工作队列等待时间。通过调度作业乱序，根据在节点磁盘高速缓存其碰撞事件的可用性，响应时间进一步减少，尤其是在高负荷。在延迟调度策略，工作请求是在一段时间内积累，根据设定参数的子作业大小分为子作业的请求，并且根据盘节点的高速缓存内的数据片段的可用性预定在下一时间段的开始。延迟调度维持负载接近最大群集的理论上可持续负载，但是在更长的平均响应时间的成本。最后，我们提出了一种自适应延迟调度方法，其中，所述调度延迟适合于当前负载。这最后的调度方式，在正常负荷维持非常高的负载，并提供低响应时间。

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