Making nested parallel transactions practical using lightweight hardware support

Woongki Baek, Nathan Bronson, Christos Kozyrakis, Kunle Olukotun
2010 Proceedings of the 24th ACM International Conference on Supercomputing - ICS '10  
Transactional Memory (TM) simplifies parallel programming by supporting parallel tasks that execute in an atomic and isolated way. To achieve the best possible performance, TM must support the nested parallelism available in real-world applications and supported by popular programming models. A few recent papers have proposed support for nested parallelism in software TM (STM) and hardware TM (HTM). However, the proposed designs are still impractical, as they either introduce excessive runtime
more » ... excessive runtime overheads or require complex hardware structures. This paper presents filter-accelerated, nested TM (FaNTM). We extend a hybrid TM based on hardware signatures to provide practical support for nested parallel transactions. In the FaNTM design, hardware filters provide continuous and nesting-aware conflict detection, which effectively eliminates the excessive overheads of software nested transactions. In contrast to a full HTM approach, FaNTM simplifies hardware by decoupling nested parallel transactions from caches using hardware filters. We also describe subtle correctness and liveness issues that do not exist in the non-nested baseline TM. We quantify the performance of FaNTM using STAMP applications and microbenchmarks that use concurrent data structures. First, we demonstrate that the runtime overhead of FaNTM is small (2.3% on average) when applications use only single-level parallelism. Second, we show that the incremental performance overhead of FaNTM is reasonable when the available parallelism is used in deeper nesting levels. We also demonstrate that nested parallel transactions on FaNTM run significantly faster (e.g., 12.4×) than those on a nested STM. Finally, we show how nested parallelism is used to improve the overall performance of a transactional microbenchmark.
doi:10.1145/1810085.1810097 dblp:conf/ics/BaekBKO10 fatcat:niwzvwapk5garfwj3d7sgsos6u