Hybrid Transactional/Analytical Graph Processing in Modern Memory Hierarchies (#TAG)

当今的企业计算体系结构的特点是具有复杂的内存层次结构：在延迟、带宽、持久性和访问模式方面的不同应用程序需求以及可用内存和存储技术的特征需要结合使用不同的技术。构建高效的数据管理和分析解决方案，以满足现代应用程序的挑战，需要利用这种内存层次结构，例如，通过缓存策略，考虑给定技术的特定特征，并将数据对象保持在最佳水平。在这个项目中，我们计划利用现代内存层次结构来支持图数据的混合事务/分析处理（HTAP）。该项目有三个主要目标：首先，我们计划设计、开发和评估持久内存中图形数据的数据结构和查询操作。其目标是同时支持事务性更改、导航查询和图形分析。第二个目标是通过高效的数据传输和一致性机制，支持图形数据在专用内存（例如gpu）的基于硬件的加速器上的分析。第三，我们通过将图数据的时间差异快照交换到面向块的外部存储并将其包含在查询处理中来考虑时间旅行查询。

Today’s enterprise computing architectures are characterized by a complex memory hierarchy: the different application requirements in terms of latency, bandwidth, persistence, and access pattern as well as the characteristics of available memory and storage technology require combining different technologies.Building highly efficient data management and analytics solutions which meet the challenges of modern applications requires to utilize this memory hierarchy, e.g. by caching strategies, taking the specific characteristics of a given technology into account, and keeping data objects in the optimal level. In this project, we plan to exploit modern memory hierarchies to support Hybrid transactional/analytical processing (HTAP) on graph data.The project has three main objectives: First, we plan to design, develop and evaluate data structures and query operations for graph data in persistent memory. The goal is to simultaneously support transactional changes, navigational queries and graph analysis. A second goal is to support analyses of graph data on hardware-based accelerators with dedicated memory (e.g. GPUs) by efficient data transfer and consistency mechanisms. Third, we consider time-travel queries by swapping temporal differential snapshots of graph data also to block-oriented external storage and including them in query processing.