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BTCache: A High Performance Concurrent Read Framework Based on Bit-Torrent Algorithm

机译:BTCache:基于Bit-Torrent算法的高性能并发读取框架

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During the computation stage of parallel seismic applications, it is usually a common need for a large amount of processes to concurrently read a large seismic dataset file from Cluster File Systems (CFS). When there are a great many of processes, for example, more than 100 processes, all processes reading from CFS will impose a huge pressure on the I/O subsystem of CFS. In this circumstance, the aggregated read bandwidth will decrease, which leads to that each process must spend a long time waiting for data to enter the computation stage. Thus, the efficiency of parallel seismic applications is affected severely. To solve this problem, we propose a concurrent read framework based on bit-torrent algorithm called BTCache. In BTCache, the whole dataset is divided into data blocks logically. Data blocks that are read by each process from CFS will be cached by a multi-level LRU-Cache constructed by user buffer, memory, and local disks on each computing node. Then, the bittorrent algorithm is used to transfer data among processes, so the pressure imposed on CFS will be reduced and the aggregated read bandwidth can be improved. Experimental results on a large high-performance computing cluster using 150 computing nodes show that BTCache can improve read bandwidth by 3.64 times compared with the concurrent read method. (Abstract)
机译:在并行地震应用程序的计算阶段,通常通常需要大量的过程来同时从簇文件系统(CFS)读取大型地震数据集文件。当有很多进程(例如,超过100个进程)时,从CFS读取的所有进程都会对CFS的I / O子系统施加巨大压力。在这种情况下,聚合的读取带宽将减少,这导致每个进程必须花费很长时间等待数据进入计算阶段。因此,并行地震应用的效率受到严重影响。为了解决这个问题,我们提出了一种基于BT比特洪流算法的并发读取框架。在BTCache中,整个数据集在逻辑上分为数据块。每个进程从CFS读取的数据块将由由每个计算节点上的用户缓冲区,内存和本地磁盘构造的多级LRU缓存进行缓存。然后,使用bittorrent算法在进程之间传输数据,从而减轻了对CFS的压力,并可以提高聚合的读取带宽。在使用150个计算节点的大型高性能计算群集上的实验结果表明,与并行读取方法相比,BTCache可以将读取带宽提高3.64倍。 (抽象的)

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