Techniques for improving the performance of software transactional memory

Transactional Memory (TM) gives software developers the opportunity to write concurrent programs more easily compared to any previous programming paradigms and gives a performance comparable to lock-based synchronizations. Current Software TM (STM) implementations have performance overheads that can be reduced by introducing new abstractions in Transactional Memory programming model. In this thesis we present four new techniques for improving the performance of Software TM: (i) Abstract Nested Transactions (ANT), (ii) TagTM, (iii) profile-guided transaction coalescing, and (iv) dynamic transaction coalescing. ANT improves performance of transactional applications without breaking the semantics of the transactional paradigm, TagTM speeds up accesses to transactional meta-data, profile-guided transaction coalescing lowers transactional overheads at compile time, and dynamic transaction coalescing lowers transactional overheads at runtime. Our analysis shows that Abstract Nested Transactions, TagTM, profile-guided transaction coalescing, and dynamic transaction coalescing improve the performance of the original programs that use Software Transactional Memory

1st BSC Doctoral Symposium : book of abstracts

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Dynamic transaction coalescing

Prior work in Software Transactional Memory has identified high overheads related to starting and committing transactions that may degrade the application performance. To amortize these overheads, transaction coalescing techniques have been proposed that coalesce two or more small transactions into one large transaction. However, these techniques either coalesce transactions statically at compile time, or lack on-line profiling mechanisms that allow coalescing transactions dynamically. Thus, such approaches lead to sub-optimal execution or they may even degrade the performance. In this paper, we introduce Dynamic Transaction Coalescing (DTC), a compile-time and run-time technique that improves transactional throughput. DTC reduces the overheads of starting and committing a transaction. At compile-time, DTC generates several code paths with a different number of coalesced transactions. At runtime, DTC implements low overhead online profiling and dynamically selects the corresponding code path that improves throughput. Compared to coalescing transactions statically, DTC provides two main improvements. First, DTC implements online profiling which removes the dependency on a pre-compilation profiling step. Second, DTC dynamically selects the best transaction granularity to improve the transaction throughput taking into consideration the abort rate. We evaluate DTC using common TM benchmarks and micro-benchmarks. Our findings show that: (i) DTC performs like static transaction coalescing in the common case, (ii) DTC does not suffer from performance degradation, and (iii) DTC outperforms static transaction coalescing when an application exposes phased behavior.Peer ReviewedPostprint (published version

Dynamic transaction coalescing

Prior work in Software Transactional Memory has identified high overheads related to starting and committing transactions that may degrade the application performance. To amortize these overheads, transaction coalescing techniques have been proposed that coalesce two or more small transactions into one large transaction. However, these techniques either coalesce transactions statically at compile time, or lack on-line profiling mechanisms that allow coalescing transactions dynamically. Thus, such approaches lead to sub-optimal execution or they may even degrade the performance. In this paper, we introduce Dynamic Transaction Coalescing (DTC), a compile-time and run-time technique that improves transactional throughput. DTC reduces the overheads of starting and committing a transaction. At compile-time, DTC generates several code paths with a different number of coalesced transactions. At runtime, DTC implements low overhead online profiling and dynamically selects the corresponding code path that improves throughput. Compared to coalescing transactions statically, DTC provides two main improvements. First, DTC implements online profiling which removes the dependency on a pre-compilation profiling step. Second, DTC dynamically selects the best transaction granularity to improve the transaction throughput taking into consideration the abort rate. We evaluate DTC using common TM benchmarks and micro-benchmarks. Our findings show that: (i) DTC performs like static transaction coalescing in the common case, (ii) DTC does not suffer from performance degradation, and (iii) DTC outperforms static transaction coalescing when an application exposes phased behavior.Peer Reviewe