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

• Applicants: Professor Dr.-Ing. Thilo Pionteck; Professor Dr. Gunter Saake
• Subject Area: Security and Dependability, Operating-, Communication- and Distributed Systems ; Computer Architecture, Embedded and Massively Parallel Systems
• Term: from 2017 to 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 361499466

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.

• DFG Programme: Priority Programmes
• Subproject of: SPP 2037: Scalable Data Management on Future Hardware
• Co-Investigator: Dr.-Ing. David Broneske