Journal of East China Normal University(Natural Science) ›› 2023, Vol. 2023 ›› Issue (5): 90-99.doi: 10.3969/j.issn.1000-5641.2023.05.008

• System for Learning from Data • Previous Articles     Next Articles

Acceleration technique for heterogeneous operators based on openGauss

Xiansen CHEN, Chen XU*()   

  1. School of Data Science and Engineering, East China Normal University, Shanghai 200062, China
  • Received:2023-06-30 Online:2023-09-25 Published:2023-09-15
  • Contact: Chen XU


The high parallelism and throughput of graphics processing unit (GPU) can improve the performance of on-line analytical processing (OLAP) queries in databases. However, openGauss currently cannot take advantage of the benefits of heterogeneous computing hardware such as GPU. Therefore, in this study, we explore using GPU to accelerate the OLAP processing in the system and achieve higher performance. The focus is on how to implement and optimize GPU acceleration modules for openGauss. To address the difference in execution granularity between openGauss and PostgreSQL, we propose a CPU (central processing unit)-GPU collaborative parallel solution based on chunked reading and key distribution. This solution can reduce the I/O (input/output) time of the GPU Scan operator to reduce idle waiting time, and run multiple instances of GPU Join to support multi-GPU environments. To address the architectural differences between openGauss and PostgreSQL, a heterogeneous operator acceleration technology compatible with vectorized engines is proposed. A custom operator framework is implemented that can embed a vectorized execution engine, and a vectorized GPU Scan operator capable of processing openGauss columnar data is employed based on this framework. A prototype system is implemented to verify the effectiveness of the proposed approach.

Key words: heterogeneous database, graphics processing unit, vectorized engine, on-line analytical processing

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