Method for memory on-line analytical processing (OLAP) query optimization based on field programmable gate array (FPGA)

Abstract

The invention relates to a method for memory on-line analytical processing (OLAP) query optimization based on a field programmable gate array (FPGA). The method comprises the steps of constructing a memory-memory-faced data warehouse heterogeneous storage model; performing query optimization facing a central processing unit (CPU)-FPGA heterogeneous processor based on the heterogeneous storage model: generating a grouping projection vector through subquery; performing dictionary table compression on the grouping projection vector; updating a grouping projection as a grouping projection vector based on dictionary table coding according to a projection dictionary table; performing connection operation on the grouping projection vector and a fact table foreign key, and generating a measurement vector based on measurement list aggregation computation; performing index aggregation computation based on the measurement vector; performing query optimization facing a CPU and FPGA heterogeneous computing platform based on the heterogeneous storage model: causing the FPGA and the CPU to perform shared access of an identical memory address space; when the FPGA is in PCI-E accelerator card configuration, using an FPGA acceleration connection performance and the FPGA to directly access a flash card through a PCI-E channel to perform data processing; and when the FPGA is integrated to a flash memory, accelerating data access and aggregation computation performances of the flash card through the FPGA.

Description

technical field [0001] The invention relates to an in-memory OLAP query method in the database field, in particular to an FPGA-based in-memory OLAP query optimization method. Background technique [0002] FPGA is a semi-custom circuit in the field of application-specific integrated circuit (ASIC). FPGA is a programmable device, and FPGA can be used as a full-custom or semi-custom ASIC circuit. FPGA has the characteristics of higher integration density, larger capacity, and lower power consumption. It is currently used as a hardware-level accelerator by some database manufacturers. The next-generation data center will introduce a large number of accelerator technologies to participate in the calculation of various applications, and improve the performance-to-power ratio and real-time calculation of the data center. In the development of future processors, there is also a trend of integrating FPGA into CPU's integrated heterogeneous computing architecture to improve the proce...

AI Technical Summary

Problems solved by technology

The current iterative processing model widely used in databases is a strongly coupled pipeline processing model. When faced with heterogeneous computing resources, such as CPU, GPU, Xeon Phi, FPGA, and heterogeneous storage resources, such as DRAM, NVRAM, Flash, etc., the iterative processing between heterogeneous computing units and storage units with large access delays produces huge data access and computing delays and high data migration costs

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