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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.

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.

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 comput ation 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 para llelized using OpenMP and transactional memory.

Future generations of Chip Multiprocessors (CMP) will provide dozens or even hundreds of cores inside the chip. Writing applications that benefit from the massive computational power offered by these chips is not going to be an easy task for mainstream programmers who are used to sequential algorithms rather than parallel ones. This paper explores the possibility of using Transactional Memory (TM) in OpenMP, the industrial standard for writing parallel programs on shared-memory architectures, for C, C++ and Fortran. One of the major complexities in writing OpenMP applications is the use of critical regions (locks), atomic regions and barriers to synchronize the execution of parallel activities in threads. TM has been proposed as a mechanism that abstracts some of the complexities associated with concurrent access to shared data while enabling scalable performance. The paper presents a first proof-of-concept implementation of OpenMP with TM. Some language extensions to OpenMP are proposed to express transactions. These extensions are implemented in our source-to-source OpenMP Mercurium compiler and our Software Transactional Memory (STM) runtime system Nebelung that supports the code generated by Mercurium. Hardware Transactional Memory (HTM) or Hardware-assisted STM (HaSTM) are seen as possible paths to make the tandem TM-OpenMP more scalable. In the evaluation section we show the preliminary results. The paper finishes with a set of open issues that still need to be addressed, either in OpenMP or in the hardware/software implementations of TM.

Transactional Memory (TM) is a key future technology for emerging many-cores. On the other hand, OpenMP provides a vast established base for writing parallel programs, especially for scientific applications. Combining TM with OpenMP provides a rich, enhanced programming environment and an attractive solution to the many-core software productivity problem.In this paper, we discuss the first multithreaded runtime environment for supporting our combined TM and OpenMP framework. We present the extensions of OpenMP for using Transactional Memory. We then present the novel multithreaded STM design with a dedicated thread for eager asynchronous conflict detection. Conflict detection will be executed in a separate thread so the transaction will not waste time on that. On the other hand, eager conflict detection is asynchronous which will increase speculative parallel execution. We also include an initial performance analysis of the runtime system and possible gains which can be achieved.

Recently proposed hybrid dataflow and shared memory programming models combine these two underlying models in order to support a wider range of problems naturally. The effectiveness of such hybrid models for parallel implementations of dense and sparse algebra problems is well known. In this paper, we show another real world example for which hybrid dataflow models provide better support than traditional shared memory models. Specifically, we compare these models using the game engine parallelization as a case study. We show that hybrid dataflow models decrease the complexity of the parallel game engine implementation by eliminating or restructuring the explicit synchronization that is necessary in shared memory implementations. The corresponding implementations also exhibit good scala-bility and better speedup than the shared memory parallel implementations, especially in the case of a highly congested game world that contains a large number of game objects. Ultimately, on an eight core machine we were able to obtain 4.72x speedup compared to the sequential baseline, and to improve 49% over the lock-based parallel implementation based on work-sharing.

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. Their findings have influenced the introduction of task dependency in the recently published OpenMP 4.0 standard. In this paper, we present 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. We also include sequential and shared-memory implementations based on OpenMP and 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.