Deterministic execution of a multithreaded application guarantees the same output as long as the application runs with the same input parameters. Determinism helps a programmer to test and debug an application and to provide fault-tolerance in the systems based on replicas. Additionally, Transactional Memory (TM) greatly simplifies development of multithreaded applications where applications use transactions (instead of locks) as a concurrency control mechanism to synchronize accesses to shared memory. However, deterministic systems proposed so far are not TM-aware. They violate the main properties of TM (atomicity, consistency and isolation of transactions), and execute TM applications incorrectly. In this paper, we present DeTrans, a runtime system for deterministic execution of multithreaded TM applications. DeTrans executes TM applications deterministically, it executes nontransactional code serially in round-robin order, and transactional code in parallel. Also, we show how DeTrans works with both eager and lazy software TM. We compare DeTrans with Dthreads, a state-of-the-art deterministic execution system. Unlike Dthreads, DeTrans does not use memory protection hardware nor facilities of the underlying operating system (OS) to execute multithreaded applications deterministically. DeTrans uses properties of software TM to ensure deterministic execution. We evaluate DeTrans using the STAMP benchmark suite and we compare DeTrans and Dthreads performance costs. DeTrans incurs less overhead because threads execute in the same address space without any OS system calls overhead. According to our results, DeTrans is 3.99x, 3.39x, 2.44x faster on average than Dthreads for 2, 4 and 8 threads, respectively.

Previous studies in software transactional memory mostly focused on reducing the overhead of transactional read and write operations. In this article, we introduce transaction coalescing , a profile-guided compiler optimization technique that attempts to reduce the overheads of starting and committing a transaction by merging two or more small transactions into one large transaction. We develop a profiling tool and a transaction coalescing heuristic to identify candidate transactions suitable for coalescing. We implement a compiler extension to automatically merge the candidate transactions at the compile time. We evaluate the effectiveness of our technique using the hash table micro-benchmark and the STAMP benchmark suite. Transaction coalescing improves the performance of the hash table significantly and the performance of Vacation and SSCA2 benchmarks by 19.4% and 36.4%, respectively, when running with 12 threads.

This paper introduces Atomic Dataflow Model (ADF) - a programming model for shared-memory systems that combines aspects of dataflow programming with the use of explicitly mutable state. The model provides language constructs that allow a programmer to delineate a program into a set of tasks and to explicitly define input data for each task. This information is conveyed to the ADF runtime system which constructs the task dependency graph and builds the necessary infrastructure for dataflow execution. However, the key aspect of the proposed model is that it does not require the programmer to specify all of the task's dependencies explicitly, but only those that imply logical ordering between tasks. The ADF model manages the remainder of inter-task dependencies automatically, by executing the body of the task within an implicit memory transaction. This provides an easy-to-program optimistic concurrency substrate and enables a task to safely share data with other concurrent tasks. In this paper, we describe the ADF model and show how it can increase the programmability of shared memory systems.

In this paper we introduce TagTM, a Software Transactional Memory (STM) system augmented with a new hardware mechanism that we call GTags. GTags are new hardware cache coherent tags that are used for fast meta-data access. TagTM uses GTags to reduce the cost associated with accesses to the transactional data and corresponding metadata. For the evaluation of TagTM, we use the STAMP TM benchmark suite. In the average case TagTM provides a speedup of 7–15% (across all STAMP applications), and in the best case shows up to 52% speedup of committed transaction execution time (for SSCA2 application).

In this paper we present Atomic Dataflow model (ADF), a new task-based parallel programming model for C/C++ which integrates dataflow abstractions into the shared memory programming model. The ADF model provides pragma directives that allow a programmer to organize a program into a set of tasks and to explicitly define input data for each task. The task dependency information is conveyed to the ADF runtime system which constructs the dataflow task graph and builds the necessary infrastructure for dataflow execution. Additionally, the ADF model allows tasks to share data. The key idea is that computation is triggered by dataflow between tasks but that, within a task, execution occurs by making atomic updates to common mutable state. To that end, the ADF model employs transactional memory which guarantees atomicity of shared memory updates. We show examples that illustrate how the programmability of shared memory can be improved using the ADF model. Moreover, our evaluation shows that the ADF model performs well in comparison with programs parallelized using OpenMP and transactional memory.

Many researchers have developed applications using transactionalmemory (TM) with the purpose of benchmarking different implementations, and studying whether or not TM is easy to use. However, comparatively little has been done to provide general-purpose tools for profiling and tuning programs which use transactions.