Over the past decade, GPUs have demonstrated significant potential in accelerating Online Analytical Processing (OLAP) operations. However, there remains a substantial gap in their application to Online Transaction Processing (OLTP), as GPUs were traditionally considered unsuitable for such workloads. Despite this perception, the massive parallelism and high memory bandwidth of GPUs offer a unique opportunity to process thousands of transactions concurrently, making them promising candidates for OLTP acceleration. Concurrency control schemes, which play a critical role in determining the performance of OLTP systems, may behave differently on GPUs due to their architectural differences from CPUs. This raises a key question: How well do concurrency control schemes designed for CPUs adapt to GPU environments? To answer this, we present gCCTB, the first testbed designed to evaluate concurrency control schemes on GPUs. We implement and benchmark eight CC schemes, including six classic CPU-oriented schemes and two designed specifically for GPUs, on both the YCSB and TPC-C benchmarks under varied contention levels and GPU configurations. Our findings reveal that GPU-optimized schemes do not consistently outperform CPU-oriented schemes, particularly under specific workloads and contention levels. Moreover, GPU-specific parameters, such as the number of threads per warp and warps per block, significantly impact performance and require careful tuning. Finally, we find that conflict resolution overhead is a crucial factor influencing the performance of CPU-oriented schemes on GPUs, with optimistic concurrency control consistently minimizing this overhead and outperforming other CPU-oriented schemes across all workloads.

翻译：过去十年中，GPU在加速联机分析处理方面展现出显著潜力。然而，由于传统上认为GPU不适合处理联机事务处理负载，其在OLTP领域的应用仍存在巨大鸿沟。尽管存在这种认知，GPU的巨量并行性与高内存带宽为同时处理数千个事务提供了独特机遇，使其成为OLTP加速的潜在候选方案。作为决定OLTP系统性能的关键因素，并发控制方案在GPU上可能因架构差异而表现出与CPU不同的行为特性。这引发了一个核心问题：为CPU设计的并发控制方案能否良好适配GPU环境？为解答该问题，我们提出gCCTB——首个面向GPU环境设计并发控制方案评估的测试平台。我们在YCSB和TPC-C基准测试中，针对不同竞争等级与GPU配置，实现并基准测试了八种CC方案，包括六种经典面向CPU方案与两种专为GPU设计的方案。研究结果表明：GPU优化方案并非始终优于CPU方案，尤其在特定工作负载与竞争等级下。此外，GPU特有的参数（如每线程束线程数与每块线程束数）对性能具有显著影响，需谨慎调优。最后，我们发现冲突解决开销是影响CPU方案在GPU上性能的关键因素，乐观并发控制始终能最小化此开销，并在所有工作负载中超越其他CPU方案。