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Shared memory multiprocessors come back to popularity thanks to rapid spreading of commodity multi-core architectures. As ever, shared memory programs are fairly easy to write and quite hard to optimise; providing multi-core programmers with optimising tools and programming frameworks is a nowadays challenge. Few efforts have been done to support effective streaming applications on these architectures. In this paper we introduce FastFlow, a low-level programming framework based on lock-free queues explicitly designed to support high-level languages for streaming applications. We compare FastFlow with state-of-the-art programming frameworks such as Cilk, OpenMP, and Intel TBB. We experimentally demonstrate that FastFlow is always more efficient than all of them in a set of micro-benchmarks and on a real world application; the speedup edge of FastFlow over other solutions might be bold for fine grain tasks, as an example +35% on OpenMP, +226% on Cilk, +96% on TBB for the alignment of protein P01111 against UniProt DB using Smith-Waterman algorithm.
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Shared memory multiprocessors have returned to popularity thanks to rapid spreading of commodity multi-core architectures. However, little attention has been paid to supporting effective streaming applications on these architectures. In this paper we describe FastFlow, a low-level programming framework based on lock-free queues explicitly designed to support high-level languages for streaming applications. We compare FastFlow with state-of-the-art programming frameworks such as Cilk, OpenMP, and Intel TBB. We experimentally demonstrate that FastFlow is always more efficient than them on a given real world application: the speedup of FastFlow over other solutions may be substantial for fine grain tasks, for example +35% over OpenMP, +226% over Cilk, +96% over TBB for the alignment of protein P01111 against UniProt DB using the Smith-Waterman algorithm.
We explore the possibilities offered by a programming model supporting components, workflows and skeletons. In particular we describe how Stcm (Spatio-Temporal Component Model), an already existing programming model supporting components and workflows, can be extended to also provide algorithmic skeleton concepts. Programmers are therefore enabled to assembly applications specifying both temporal and spatial relations among components and instantiating predefined skeleton composite components to implement all those application parts that can be easily modeled with the available skeletons. We discuss preliminary results as well as the benefits deriving from Stkm (Spatio-Temporal sKeleton Model) adoption in a couple of real applications.
Semantics of skeleton-based parallel programming languages comes usually as two distinct items: a functional semantics, modeling the function computed by the skeleton program, and a parallel semantics describing the ways used to exploit parallelism during the execution of the skeleton program. The former is usually expressed using some kind of semantic formalism, while the latter is almost always given in an informal way. Such a separation of functional and parallel semantics seriously impairs the possibility of programmers to use the semantic tools to prove properties of programs. In this work, we show how a formal semantic framework can be set up that handles both functional and parallel aspects of skeleton-based parallel programs. The framework is based on a labeled transition system. We show how different properties related to skeleton programs can be proved using such a system. We use Lithium, a skeleton-based full Java parallel programming environment, as the case study.
The ASSIST environment provides a high-level programming toolkit for the grid. ASSIST applications are described by means of a coordination language, which can express arbitrary graphs of modules. These modules (or a graph of them) may be enclosed in components specifically designed for the grid (GRID.it components). In this paper we describe how ASSIST modules can be wired through standard Web Services, and how GRID.it components may be made available as standard Web Services.
ASSIST is a parallel programming environment aimed at providing programmers of complex parallel application with a suitable and effective programming tool. Being based on algoritmical skeletons and coordination languages technologies, the programming environment relieves the programmer from a number of cumbersome, error prone activities that are required when using traditional parallel programming environments. ASSIST has been specifically designed to be easily customizable in order to experiment different implementation techniques, solutions, algorithms or back-ends any time new features are required or new technologies become available. In this work we discuss how this goal has been achieved and how the current ASSIST programming environment has been already used to experiment solutions not implemented in the first version of the tool.
A major weakness of the current programming systems based on skeletons is that parallel semantics is usually provided in an informal way, thus preventing any formal comparison about program behavior. We describe a schema suitable for the description of both functional and parallel semantics of skeletal languages which is aimed at filling this gap. The proposed schema of semantics represents a handy framework to prove the correctness and validate different rewriting rules. These can be used to transform a skeleton program into a functionally equivalent but possibly faster version.
We describe the implementation of ASSIST, a programming environment for parallel and distributed programs. Its coordination language is based of the parallel skeleton model, extended with new features to enhance expressiveness, parallel software reuse, software component integration and interfacing to external resources. The compilation process and the structure of the run-time support of ASSIST are discussed with respect to the issues introduced by the new characteristics, presenting an analysis of the first test results.
Some skeleton based parallel programming models allow the programmer to use both data and stream parallel skeletons within the same program. It is known that particular skeleton nestings can be formally rewritten into different nestings that preserve the functional semantics. Indeed, the kind and possibly the amount of parallelism usefully exploitable may change while rewriting takes place. Here we discuss an original framework allowing the user (and/or the compiling tools) of a skeleton based parallel programming language to evaluate whether or not the transformation of a skeleton program is worthwhile in terms of the final program performance. We address, in particular, the evaluation of transformations exchanging data parallel and stream parallel skeleton subtrees.