Compiler and Runtime optimisations for Graph Databases

图数据库的编译器和运行时优化

• 批准号: 2560814
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2560814
• 关键词: Compiler; Runtime; optimisations; Graph; Databases

Graph databases (GDBs) have gained significant attention during the last decade following the explosion of Big Data processing due to social media, IoT, AI, etc. While users are constantly demanding faster access of high volume data, data analysis and predictive analytics are just going to increase the data access requirements.In particular, one of the major challenges of Java-based GDBs is the fact that they are affected by the performance of the system. Providing more memory, doesn't translate automatically to better performance, due to the limits of the Runtime Environment (RTE) they execute on, such as libraries, processes, Virtual Machine (VM) etc.The main contribution of this project is the focus on hardware/software interaction methods in order to solve the scalability problem of Java Graph databases, especially on a Non-Uniform Memory Access (NUMA) architecture.The key objectives of this project are:1) to investigate how system functionalities (NUMAness, compiler, runtime, etc.) affect the performance of a Graph Database,2) to perform targeted optimisations for GDBs (compiler/runtime and memory optimisations, etc.) to improve data locality, and3) to implement a custom version of experimental platform showcasing the aforementioned optimisations.The approach to be taken in order to proceed with the above, is:1) the implementation of an Allocation Memory Profiler for the inspection of allocation patterns of applications running on the VM and description of the domain application and its detailed performance analysis.2) the implementation of a tool on top of the VM, in order to wrap "performance" functionality into the VM and thus to allow the VM to access the CPU's performance monitoring unit hardware counters.3) the implementation of a NUMA-aware memory manager (Application Programming Interface - API), for off-heap memory allocation and accesses.To perform a scalability analysis of GDBs a benchmark suite for NoSQL Databases which supports Relational Property Graphs will be used. This benchmark suite, tests the following three scenarios:interactive: A transaction/query workloadbusiness intelligence: An analytical/query workloadgraph algorithms: Graph analysis algorithmsA detailed performance analysis will be conducted for a thorough evaluation of all aspects of the experimental platform, in order to investigate the GDBs' allocation behavior, categorise all the occurred patters and correlate them with the semantics of each benchmark's query.Our research will focus on the VM's Garbage Collector, and its effect on the overall performance ofGDBs. Since Java GDBs' querying languages are just another Java program, they fall into the same optimisation realm of standard Java programs. However, the sensitivity many GDBs have at the memory subsystem, steers our research efforts also on GC tuning and compiler optimisations.Relevant EPSRC research areas: DatabasesProgramming languages and compilers Architectures and operating systems Software engineering

在过去的十年中，随着社交媒体、物联网、人工智能等大数据处理的爆炸式增长，图形数据库(GDB)得到了极大的关注。尽管用户不断要求更快地访问海量数据，但数据分析和预测分析只会增加对数据的访问要求。尤其是，基于Java的GDB面临的主要挑战之一是它们受到系统性能的影响。由于运行时环境(RTE)的限制，提供更多的内存并不能自动转化为更好的性能。该项目的主要贡献是关注硬件/软件交互方法，以解决Java Graph数据库的可扩展性问题，特别是在非统一内存访问(NUMA)体系结构上。该项目的主要目标是：1)调查系统功能(NUMAness、编译器、运行时等)是如何实现的。影响图形数据库的性能，2)执行针对GDB的目标优化(编译器/运行时和内存优化等)为了改善数据局部性，以及3)实现展示上述优化的实验平台的定制版本。为了继续上述工作，要采取的方法是：1)实现分配存储器分析器，用于检查在VM上运行的应用的分配模式，以及域应用的描述及其详细的性能分析。2)在VM上实现工具，以便将“性能”功能包装到VM中，从而允许VM访问CPU的性能监视单元硬件计数器。3)实现NUMA感知的存储器管理器(应用编程接口-API)，为了执行GDB的可伸缩性分析，将使用支持关系属性图的NoSQL数据库基准测试套件。该基准测试套件测试了以下三个场景：交互：事务/查询工作负载商业智能：分析/查询工作负载图形算法：图形分析算法将对实验平台的各个方面进行详细的性能分析，以调查GDB的分配行为，对所有发生的模式进行分类，并将它们与每个基准查询的语义相关联。我们的研究将集中在VM的垃圾收集器，以及它对GDB整体性能的影响。由于Java GDB的查询语言只是另一个Java程序，因此它们属于标准Java程序的同一优化领域。然而，许多GDB对内存子系统的敏感性，也将我们的研究努力引导到GC调优和编译器优化上。相关的EPSRC研究领域：数据库编程语言和编译器体系结构与操作系统软件工程