ADAMANT: Adaptive Data Management in Evolving Heterogeneous Hardware/Software Systems

ADAMANT：不断发展的异构硬件/软件系统中的自适应数据管理

• 批准号: 361499466
• 负责人: Professor Dr.-Ing. Thilo Pionteck
• 金额: --
• 依托单位: Arbeitsgruppe Datenbanken und Software Engineering
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/361499466?language=en
• 关键词: ADAMANT; Adaptive; Data; Management; Evolving

Heterogeneous system architectures consisting of CPUs, GPUs and FPGAs offer a variety of optimization possibilities for database systems compared to pure CPU-based systems. However, it has been shown that it is not sufficient to just map existing software concepts one-to-one to non von-Neumann hardware architectures such as FPGAs to fully exploit their optimization potential. Rather, new processing capabilities require the design of novel processing concepts, which have to be considered at the planning level of query processing. A basic processing concept has already been developed in the first project phase by considering device-specific features in our plug’n’play system architecture. In fact, more advanced concepts are required to achieve an optimal exploitation of the capabilities of the hardware architectures. While significant speed-ups were achieved on the level of individual operators mapped to GPUs and FPGAs, the performance gain at the level of complete queries was unsatisfying. Hence, we derived the hypothesis for the second project phase that standard query-mapping approaches with their consideration of queries on the level of individual operators is not sufficient to explore the extended processing features of heterogeneous system architectures.We will address this shortcoming by researching new processing and query mapping methods for heterogeneous systems, which question the commonly used granularity level of operators. Therefore, we will provide processing entities that encapsulate a greater functionality than standard database operators and may span multiple hardware devices. Thus, processing entities are intrinsically heterogeneous and combine the specific features of individual devices. As a result, our heterogeneous system architecture enables database operations and features that are not available or cannot be implemented efficiently in classical database systems. To explore this extended feature set, we have identified three application domains that are still challenging for classical database systems and for which we assume that they will benefit greatly from heterogeneous system architectures: High-volume data feeds, approximate query processing and dynamic multi-query processing. The stream-based nature of high-volume data feeds asks for a hardware architecture where processing can be done on the fly without the need to store data beforehand. Hence, FPGAs are a promising hardware platform for processing high-volume data feed applications. Furthermore, FPGAs as well as GPUs are good platforms for approximate query processing, as they allow for approximate arithmetics and hardware-influenced sampling techniques. Dynamic multi-query processing is very challenging from the system management point of view, as query plans that have performed well for one workload can be inefficient for a different workload. Here, the multi-level parallelism of heterogeneous systems offers better opportunities to handle heavy workloads.

与纯基于CPU的系统相比，由CPU、GPU和FPGA组成的异构系统架构为数据库系统提供了各种优化可能性。然而，已经表明，仅仅将现有的软件概念一对一地映射到非冯·诺依曼硬件架构（如FPGA）以充分利用其优化潜力是不够的。相反，新的处理能力需要设计新的处理概念，这必须在查询处理的规划级别考虑。一个基本的处理概念已经在第一个项目阶段通过考虑我们的即插即用系统架构中的设备特定功能开发。事实上，需要更先进的概念来实现硬件架构能力的最佳利用。虽然在映射到GPU和FPGA的各个运算符的级别上实现了显着的速度提升，但在完整查询级别上的性能提升并不令人满意。因此，我们得出的假设，为第二个项目阶段，标准的查询映射方法与他们的考虑，查询的水平上的个人operator是不够的，以探索的扩展处理功能的异构系统architecture.We将解决这个缺点，通过研究新的处理和查询映射方法的异构系统，质疑常用的粒度级别的操作。因此，我们将提供处理实体，这些实体封装了比标准数据库操作符更大的功能，并且可以跨越多个硬件设备。因此，处理实体本质上是异构的，并且联合收割机组合了各个设备的特定特征。 因此，我们的异构系统架构，使数据库操作和功能，是不可用的，或不能有效地实现在经典的数据库系统。为了探索这个扩展的功能集，我们已经确定了三个应用领域，仍然是经典的数据库系统的挑战，我们认为，他们将大大受益于异构系统架构：大容量的数据源，近似查询处理和动态多查询处理。大容量数据馈送的基于流的性质要求一种硬件架构，在这种架构中，处理可以在不需要事先存储数据的情况下进行。因此，FPGA是处理大容量数据馈送应用的有前途的硬件平台。此外，FPGA和GPU是近似查询处理的良好平台，因为它们允许近似算法和受硬件影响的采样技术。从系统管理的角度来看，动态多查询处理非常具有挑战性，因为对于一个工作负载表现良好的查询计划对于不同的工作负载可能是低效的。在这里，异构系统的多级并行性为处理繁重的工作负载提供了更好的机会。