论文标题
Lucata Pathfinder上的并发图查询
Concurrent Graph Queries on the Lucata Pathfinder
论文作者
论文摘要
现在对非结构化数据（如图）的高性能分析对于从商业智能到基因组分析的应用程序至关重要。为此，数据中心在内存中拥有大图，以提供来自不同用户的多个并发查询。即使是单个分析也经常探索多个选项。当前的计算体系结构通常不是最耗时或节能的解决方案。新型的Lucata探路者结构解决了这个问题，将迁移线程与低延迟阅读与内存端处理相结合，以进行高性能积累。一百至750个并发广度优先搜索（BFS）在一个时间上的一个时间上，端到端的速度均达到了81％至97％的速度，其边缘为52.2亿。与在基于英特尔的大型服务器上运行的Redisgraph相比，Pathfinder同时运行128 BFS查询的速度达到了19美元的$ \ times $。探路者还有效地支持了通过连接的组件和BFS证明的并发分析的混合。
High-performance analysis of unstructured data like graphs now is critical for applications ranging from business intelligence to genome analysis. Towards this, data centers hold large graphs in memory to serve multiple concurrent queries from different users. Even a single analysis often explores multiple options. Current computing architectures often are not the most time- or energy-efficient solutions. The novel Lucata Pathfinder architecture tackles this problem, combining migratory threads for low-latency reading with memory-side processing for high-performance accumulation. One hundred to 750 concurrent breadth-first searches (BFS) all achieve end-to-end speed-ups of 81% to 97% over one-at-a-time queries on a graph with 522M edges. Comparing to RedisGraph running on a large Intel-based server, the Pathfinder achieves a 19$\times$ speed-up running 128 BFS queries concurrently. The Pathfinder also efficiently supports a mix of concurrent analyses, demonstrated with connected components and BFS.
