大型图上的高速缓存有效分叉处理模式
Cache-Efficient Fork-Processing Patterns on Large Graphs
摘要：
随着大型图处理的出现，我们观察到一个昂贵的叉处理模式（FPP），这是常见的许多图算法。FPP的独特之处在于它可以从同一个图上的不同源顶点发起许多独立的查询。例如，分析网络社区简档的算法可以同时执行从数万个源顶点开始的个性化PageRank。我们研究的效率，处理FPPs在国家的最先进的图形处理系统的多核架构。我们发现，这些系统遭受严重的高速缓存未命中惩罚，因为在处理FPPs的不规则和不协调的内存访问。在本文中，我们提出了ForkGraph，高速缓存高效的FPP处理系统的多核架构。为了提高缓存的重用，我们将图划分为LLC容量大小的分区，并在分区的基础上缓冲和执行FPP中的查询。为了提高效率，我们进一步开发了有效的分区内和分区间执行策略。对于分区内处理，由于图分区适合LLC，我们建议执行每个图查询与高效的顺序算法（与现有的并行图处理系统中的并行算法相比），并提出了一个原子的自由查询处理，通过合并竞争操作的缓存驻留图分区。对于分区间的处理，我们提出了让步和基于优先级的调度，以减少冗余的工作在处理。此外，我们从理论上证明了ForkGraph执行相同的工作量，在一个常数的因素，作为已知的最快的顺序算法在FPP查询处理，这是工作效率。我们对现实世界的图形的评估表明，ForkGraph显着优于国家的最先进的图形处理系统的两个数量级的加速。
英文摘要：
As large graph processing emerges, we observe a costly fork-processing pattern (FPP) that is common in many graph algorithms. The unique feature of the FPP is that it launches many independent queries from different source vertices on the same graph. For example, an algorithm in analyzing the network community profile can execute Personalized PageRanks that start from tens of thousands of source vertices at the same time. We study the efficiency of handling FPPs in state-of-the-art graph processing systems on multi-core architectures. We find that those systems suffer from severe cache miss penalty because of the irregular and uncoordinated memory accesses in processing FPPs. In this paper, we propose ForkGraph, a cache-efficient FPP processing system on multi-core architectures. To improve the cache reuse, we divide the graph into partitions each sized of LLC capacity, and the queries in an FPP are buffered and executed on the partition basis. We further develop efficient intra- and inter-partition execution strategies for efficiency. For intra-partition processing, since the graph partition fits into LLC, we propose to execute each graph query with efficient sequential algorithms (in contrast with parallel algorithms in existing parallel graph processing systems) and present an atomic-free query processing by consolidating contending operations to cache-resident graph partition. For inter-partition processing, we propose yielding and priority-based scheduling, to reduce redundant work in processing. Besides, we theoretically prove that ForkGraph performs the same amount of work, to within a constant factor, as the fastest known sequential algorithms in FPP queries processing, which is work efficient. Our evaluations on real-world graphs show that ForkGraph significantly outperforms state-of-the-art graph processing systems with two orders of magnitude speedups.
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