GPUs, whose performance has gone through a huge leap over the past decade, have proved their ability to accelerate Online Analytical Processing (OLAP) operations. On the other hand, there is still a huge gap in the field of GPU-accelerated Online Transaction Processing (OLTP) operations since it was generally believed that GPUswere not suitable for OLTP in the past. However, the massive parallelism and high memory bandwidth give GPUs the potential to process thousands of transactions concurrently. Among the components of OLTP systems, Concurrency Control (CC) schemes have a great impact on the performance of transaction processing and they may behave differently on GPUs because of the different hardware architectures between GPUs and CPUs. In this paper, we design and build the first test-bed gCCTB for CCschemes on GPUsandimplement eight CC schemes for gCCTB. These schemes include six common schemes previously designed for CPUs and two schemes designed for GPUs. Then we make a comprehensive evaluation of these CC schemes with YCSB and TPC-C benchmarks and a number of launch parameters on GPUs. The experience accumulated on our test-bed can assist researchers andengineers to design andimplementnewGPU-acceleratedOLTP systems. Furthermore, the results of our evaluation cast light on research directions of high performance CC schemes on GPUs.

翻译：GPU的性能在过去十年中实现了巨大飞跃，已证明其能够加速在线分析处理（OLAP）操作。然而，在GPU加速的在线事务处理（OLTP）领域仍存在巨大空白，因为过去普遍认为GPU不适合处理OLTP。尽管如此，GPU的大规模并行性和高内存带宽使其具备并发处理数千笔事务的潜力。在OLTP系统的各个组件中，并发控制（CC）方案对事务处理性能具有重大影响，且由于GPU与CPU硬件架构的差异，这些方案在GPU上的表现可能有所不同。本文设计并构建了首个面向GPU的CC方案测试平台gCCTB，并在该平台上实现了八种CC方案，包括六种先前为CPU设计的常见方案和两种专为GPU设计的方案。随后，我们使用YCSB与TPC-C基准测试以及多组GPU启动参数对这些CC方案进行了全面评估。本测试平台积累的经验可协助研究人员和工程师设计与实现新型GPU加速OLTP系统。此外，评估结果也为GPU高性能并发控制方案的研究方向提供了重要启示。