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© 2015 Elsevier B.V. All rights reserved. Abstract The current trend in development of parallel programming models is to combine different well established models into a single programming model in order to support efficient implementation of a wide range of real world applications. The dataflow model has particularly managed to recapture the interest of the research community due to its ability to express parallelism efficiently. Thus, a number of recently proposed hybrid parallel programming models combine dataflow and traditional shared memory models. Their findings have influenced the introduction of task dependency in the OpenMP 4.0 standard. This article presents DaSH - the first comprehensive benchmark suite for hybrid dataflow and shared memory programming models. DaSH features 11 benchmarks, each representing one of the Berkeley dwarfs that capture patterns of communication and computation common to a wide range of emerging applications. DaSH also includes sequential and shared-memory implementations based on OpenMP and Intel TBB to facilitate easy comparison between hybrid dataflow implementations and traditional shared memory implementations based on work-sharing and/or tasks. Finally, we use DaSH to evaluate three different hybrid dataflow models, identify their advantages and shortcomings, and motivate further research on their characteristics., This work has been supported by the Spanish Severo Ochoa award SEV-2011-00067 and project TIN2012-34557 from the Ministry of Science and Innovation, by the Generalitat de Catalunya 2014-SGR-1051 award, the Spanish Ministry of Education (TIN2007-60625 and CSD2007-00050) and the RoMoL project (ERC Advanced Grant Agreement 321253). We thankfully acknowledge the Microsoft Research support through the BSC-Microsoft Research Center and the European Commission through the HiPEAC-3 Network of Excellence.

Many researchers have developed applications using transactional memory (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 optimizing programs which use transactions. In this paper we introduce a series of profiling and optimization techniques for TM applications. The profiling techniques are of three types: (i) techniques to identify multiple potential conflicts from a single program run, (ii) techniques to identify the data structures involved in conflicts by using a symbolic path through the heap, rather than a machine address, and (iii) visualization techniques to summarize how threads spend their time and which of their transactions conflict most frequently. Altogether they provide in-depth and comprehensive information about the wasted work caused by aborting transactions. To reduce the contention between transactions we suggest several TM specific optimizations which leverage nested transactions, transaction checkpoints, early release and etc. To examine the effectiveness of the profiling and optimization techniques, we provide a series of illustrations from the STAMP TM benchmark suite and from the synthetic WormBench workload. First we analyze the performance of TM applications using our profiling techniques and then we apply various optimizations to improve the performance of the Bayes, Labyrinth and Intruder applications. We discuss the design and implementation of the profiling techniques in the Bartok-STM system. We process data offline or during garbage collection, where possible, in order to minimize the probe effect introduced by profiling. © 2011 Springer Science+Business Media, LLC., This work is supported by the cooperation agreement between the Barcelona Supercomputing Center National Supercomputer Facility and Microsoft Research, by the Ministry of Science and Technology of Spain and the European Union (FEDER funds) under contract TIN2007-60625, by the European Network of Excellence on High-Performance Embedded Architecture and Compilation (HiPEAC) and by the European Commission FP7 project VELOX (216852). Ferad Zyulkyarov is also supported by a scholarship from the Government of Catalonia.

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.In this paper we introduce a series of profiling techniques for TM applications that provide in-depth and comprehensive information about the wasted work caused by aborting transactions. We explore three directions: (i) techniques to identify multiple potential conflicts from a single program run, (ii) techniques to identify the data structures involved in conflicts by using a symbolic path through the heap, rather than a machine address, and (iii) visualization techniques to summarize how threads spend their time and which of their transactions conflict most frequently.To examine the effectiveness of the profiling techniques, we provide a series of illustrations from the STAMP TM benchmark suite and from the synthetic WormBench workload. We show how to use our profiling techniques to optimize the performance of the Bayes, Labyrinth and Intruder applications.We discuss the design and implementation of our techniques in the Bartok-STM system. We process data offline or during garbage collection, where possible, in order to minimize the probe effect introduced by profiling.

In this paper, we present a Haskell Transactional Memory benchmark to provide a comprehensive application suite for the use of Software Transactional Memory (STM) researchers. We develop a framework to profile the execution of the benchmark applications and to collect detailed runtime data on their transactional behavior, running them on a 128-core multiprocessor. Using a composite of the collected raw data, we propose new transactional performance metrics. We analyze key statistics related to scalability, atomic sections, transactional events, overall transactional overhead and the relative hardware performance, accordingly drawing conclusions on the results. Our findings advance our comprehension on the STM runtime and the characteristics of different applications under the transactional management of the pure, functional programming language, Haskell.