Loop Parallelism: A New Skeleton Perspective on Data Parallel Patterns

Danelutto, Marco; Torquati, Massimo

doi:10.1109/pdp.2014.13

Cited by 17 publications

(15 citation statements)

References 10 publications

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“…However, it enables the possibility to define custom scheduling policies, improving the flexibility of the approach. Moreover, the scheduler implementation we used has been proven efficient and feasible in similar contexts for state-of-the-art multicore architectures up to 32/64 cores using a variety of different applications [12]. This is also confirmed by this work (Section 5.2, application characterised by very finegrained computation).…”

Section: Methodssupporting

confidence: 63%

A Power-Aware, Self-Adaptive Macro Data Flow Framework

Danelutto

Sensi

Torquati

2017

Parallel Process. Lett.

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The dataflow programming model has been extensively used as an effective solution to implement efficient parallel programming frameworks. However, the amount of resources allocated to the runtime support is usually fixed once by the programmer or the runtime, and kept static during the entire execution. While there are cases where such a static choice may be appropriate, other scenarios may require to dynamically change the parallelism degree during the application execution. In this paper we propose an algorithm for multicore shared memory platforms, that dynamically selects the optimal number of cores to be used as well as their clock frequency according to either the workload pressure or to explicit user requirements. We implement the algorithm for both structured and unstructured parallel applications and we validate our proposal over three real applications, showing that it is able to save a significant amount of power, while not impairing the performance and not requiring additional effort from the application programmer.

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Section: Methodssupporting

confidence: 63%

A Power-Aware, Self-Adaptive Macro Data Flow Framework

Danelutto

Sensi

Torquati

2017

Parallel Process. Lett.

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“…It provides developers with a set of high-level, parallel programming patterns (aka algorithmic skeletons), obtained by the composition of two basic algorithmic skeletons: a farm skeleton, and a pipeline skeleton. Leveraging the farm skeleton, FastFlow exposes a ParallelFor pattern [16], where farm workers are sequential wrappers that execute chunks of a loop iterations having the form for(idx=start;idx<stop;idx+=step). Just like TBB, FastFlow's ParallelFor pattern uses C++11 lambda functions as a concise and elegant way to create a function object: lambdas can "capture" the state of non-local variables by value or by reference and allow functions to be syntactically defined when needed.…”

Section: Fastflowmentioning

confidence: 99%

Parallel Exploration of the Nuclear Chromosome Conformation with NuChart-II

Tordini

Drocco

Misale

et al. 2015

2015 23rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing

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Abstract-High-throughput molecular biology techniques are widely used to identify physical interactions between genetic elements located throughout the human genome. Chromosome Conformation Capture (3C) and other related techniques allow to investigate the spatial organisation of chromosomes in the cell's natural state. Recent results have shown that there is a large correlation between co-localization and co-regulation of genes, but these important information are hampered by the lack of biologists-friendly analysis and visualisation software. In this work we introduce NuChart-II, a tool for Hi-C data analysis that provides a gene-centric view of the chromosomal neighbourhood in a graph-based manner. NuChart-II is an efficient and highly optimized C++ re-implementation of a previous prototype package developed in R. Representing Hi-C data using a graphbased approach overcomes the common view relying on genomic coordinates and permits the use of graph analysis techniques to explore the spatial conformation of a gene neighbourhood.

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“…On the contrary, the termination phase is always computed sequentially in the current implementation. Both the map and map-reduce phases have been implemented using the ParallelForReduce high-level pattern [8] already available in the FastFlow framework. The ParallelForReduce pattern allows efficient parallelisation of parallel loops and parallel loops with reduction variables.…”

Section: Skeleton Implementationmentioning

confidence: 99%

Pool Evolution: A Parallel Pattern for Evolutionary and Symbolic Computing

Aldinucci

Campa

Danelutto

et al. 2015

Int J Parallel Prog

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We introduce a new parallel pattern derived from a specific application domain and show how it turns out to have application beyond its domain of origin.The pool evolution pattern models the parallel evolution of a population subject to mutations and evolving in such a way that a given fitness function is optimized. The pattern has been demonstrated to be suitable for capturing and modeling the parallel patterns underpinning various evolutionary algorithms, as well as other parallel patterns typical of symbolic computation.In this paper we introduce the pattern, we discuss its implementation on modern multi/many core architectures and finally present experimental results obtained with FastFlow and Erlang implementations to assess its feasibility and scalability.

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Loop Parallelism: A New Skeleton Perspective on Data Parallel Patterns

Cited by 17 publications

References 10 publications

A Power-Aware, Self-Adaptive Macro Data Flow Framework

A Power-Aware, Self-Adaptive Macro Data Flow Framework

Parallel Exploration of the Nuclear Chromosome Conformation with NuChart-II

Pool Evolution: A Parallel Pattern for Evolutionary and Symbolic Computing

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