Query Processing on Heterogeneous Hardware
In modern processor design, power efficiency has become the primary constraint, prompting manufacturers to develop processors that balance energy consumption with the growing demand for speed. This shift has initiated an era of heterogeneous multi-core computing, characterized by machines utilizing various processors such as GPUs, MICs, and FPGAs. These processors significantly enhance performance due to their computational capabilities and memory bandwidth, essential for optimizing query processing performance. However, executing database queries efficiently across diverse processors presents challenges due to architectural differences, leading to varied performance outcomes for different operator implementations.
This chapter explores methodologies for executing database queries on any processor with maximum efficiency without manual adjustments. We propose compiling database queries into optimized code that can adapt continuously to achieve optimal performance across a wide array of processors. Key areas of focus include the use of GPUs in database systems, addressing challenges such as workload distribution and data transfer bottlenecks, and introducing a classification scheme for strategies developed to tackle these issues. Additionally, we examine NVLink 2.0 technology’s potential to improve data transfer efficiency between GPUs and CPUs, enhancing GPU-accelerated query processing. Furthermore, we present a novel adaptive query compilation-based stream processing engine (SPE) that surpasses traditional interpretation-based SPEs by incorporating runtime optimizations and task-based parallelization. This approach allows for dynamic adjustments to data characteristics, significantly improving query execution efficiency and throughput. Through these explorations, we aim to provide insights into current systems and highlight areas for future research, ultimately contributing to the advancement of heterogeneous query processing systems.
