Energy efficient GPU transactional memory via space-time optimizations

Many applications with regular parallelism have been shown to benefit from using Graphics Processing Units (GPUs). However, employing GPUs for applications with irregular parallelism tends to be a risky process, involving significant effort from the programmer. One major, non-trivial effort/risk is to expose the available parallelism in the application as 1000s of concurrent threads without introducing data races or deadlocks via fine-grained data synchronization. To reduce this effort, prior work has proposed supporting transactional memory on GPU architectures. One hardware proposal, Kilo TM, can scale to 1000s of concurrent transaction. However, performance and energy overhead of Kilo TM may deter GPU vendors from incorporating it into future designs. In this paper, we analyze the performance and energy efficiency of Kilo TM and propose two enhancements: (1) Warp-level transaction management allows transactions within a warp to be managed as a group. This aggregates protocol messages to reduce communication overhead and captures spatial locality from multiple transactions to increase memory subsystem utility. (2) Temporal conflict detection uses globally synchronized timers to detect conflicts in read-only transactions with low overhead. Our evaluation shows that combining the two enhancements in combination can improve the overall performance and energy efficiency of Kilo TM by 65% and 34% respectively. Kilo TM with the above two enhancements achieves 66% of the performance of fine-grained locking with 34% energy overhead.