Accelerating in-memory transaction processing using general purpose graphics processing units

High throughput is critical for on-line transaction processing (OLTP) applications with a large amount of users. With massive parallel processing units and high memory bandwidth, GPUs are suitable for accelerating OLTP transactions. However, it is challenge to implement transaction execution on GPUs, due to (1) the branch divergences caused by the single instruction multiple threads (SIMT) execution paradigm, and (2) the lack of fine-grained synchronization mechanism and pointer-based dynamic data structures in the GPU ecosystem. In this paper, we present a high-performance in-memory transaction processing system on GPUs to accelerate OLTP applications, named GPU-TPS. Firstly, we propose a transaction execution model to improve GPU hardware utilization and perform synchronization among transactions. Secondly, we optimize the indexing data structures that used extensively in OLTP systems (i.e., hash table for unordered store, and b＋ tree for ordered store) for fast storing on GPUs. To evaluate GPU-TPS, we apply it to two popular OLTP workloads (SmallBank and TPCC), and compare it with the state-of-the-art hardware transactional memory based CPU OLTP system (DrTM) and a GPU OLTP system (GPUTx). The experimental results show that GPU-TPS outperforms the CPU implementation by 3.8X for SmallBank and by 1.9X for TPCC, and outperforms the GPU implementation by 1.6X for SmallBank and by 1.8X for TPCC.