Processing-In-Memory Primitives for Data Management (PIMPMe)

内存技术领域的持续发展为构建高效的数据密集型系统提供了巨大的潜力，以应对现代应用的挑战。同时，它需要重新思考传统的假设，以充分利用这项技术。在这个项目中，我们计划研究以内存为中心的计算范式的好处：我们将进行将计算卸载到内存的研究，以减少内存和CPU之间传输的数据量，并以这种方式增加带宽，以减少CPU的计算负载，最终，帮助降低现代IT系统的能源消耗。我们计划特别在三个领域进行研究，其总体目标是开发一个原语框架，作为典型数据管理任务的构建块，例如查询处理，恢复日志和事务管理。这三个领域是内存处理（PIM），其中操作直接在内存中执行;内存处理（PNM），使处理更接近内存并减轻CPU的负担，例如通过基于FPGA的加速器卡与设备内存;以及Transmitting Memory，为关键代码段提供原子性抽象。该项目有三个主要目标：首先，我们计划设计和实现PIM和PNM原语，用于图数据的查询处理、索引管理和日志管理。其次，我们希望通过利用内存操作和事务内存来改进数据库中的并发控制。第三，我们调查的策略，以映射数据库事务/查询，这些原语通过建立一个全面的框架，原语和评估他们的上下文中的图形数据库处理。

Ongoing developments in the field of memory technology open up great potential for building highly efficient data-intensive systems addressing the challenges of modern applications. At the same time, it requires to rethink traditional assumptions to fully exploit this technology.In this project we plan to investigate the benefit of memory-centric computing paradigms: we will conduct research on offloading computation to memory in order to reduce the amount of data to be transferred between memory and CPU and in this way increase bandwidth, to reduce the CPU load for computation, and in the end, help to reduce the energy consumption of modern IT systems.We plan to particularly conduct research in three areas where the overarching goal is to develop a framework of primitives as building blocks for typical data management tasks such as query processing, logging for recovery, and transaction management. The three areas are Processing-in-Memory (PIM) where operations are executed directly in memory, Processing-near-Memory (PNM) bringing processing closer to memory and relieving the CPU, e.g. by FPGA-based accelerator cards with device memory, and Transactional Memory to provide the abstraction of atomicity for critical code sections.The project has three main objectives: first, we plan to design and implement PIM and PNM primitives for query processing on graph data, index management, and log management. Second, we want to improve concurrency control in databases by exploiting memory operations and transactional memory. Third, we investigate strategies to map database transactions/queries to these primitives by building a comprehensive framework of primitives and evaluating them in the context of graph database processing.