Pool Evolution: A Parallel Pattern for Evolutionary and Symbolic Computing

Aldinucci, Marco; Campa, Sonia; Danelutto, Marco; Kilpatrick, Peter; Torquati, Massimo

doi:10.1007/s10766-015-0358-5

Cited by 7 publications

(7 citation statements)

References 11 publications

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“…− Stochastic-Local-Search for Cutting-Stock Industrial Optimization (CSO) that optimizes classical bin-packing and cutting-stock optimization problems using an evolutionary stochastic-local-search (SLS) algorithm. The use case and the type of parallelization (which is based on the Fast-Flow parallelization library [27]) has been described in detail in [28]. In particular, we used the Scholl 1-3 datasets for classical bin packing problems provided in [29].…”

Section: A Benchmark Applicationsmentioning

confidence: 99%

Learning-Based Dynamic Pinning of Parallelized Applications in Many-Core Systems

Chasparis

Rossbory

Janjić

et al. 2019

2019 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)

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Motivated by the need for adaptive, secure and responsive scheduling in a great range of computing applications, including human-centered and time-critical applications, this paper proposes a scheduling framework that seamlessly adds resource-awareness to any parallel application. In particular, we introduce a learning-based framework for dynamic placement of parallel threads to Non-Uniform Memory Access (NUMA) architectures. Decisions are taken independently by each thread in a decentralized fashion that significantly reduces computational complexity. The advantage of the proposed learning scheme is the ability to easily incorporate any multi-objective criterion and easily adapt to performance variations during runtime. Under the multi-objective criterion of maximizing total completed instructions per second (i.e., both computational and memory-access instructions), we provide analytical guarantees with respect to the expected performance of the parallel application. We also compare the performance of the proposed scheme with the Linux operating system scheduler in an extensive set of applications, including both computationally and memory intensive ones. We have observed that performance improvement could be significant especially under limited availability of resources and under irregular memory-access patterns.

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Section: A Benchmark Applicationsmentioning

confidence: 99%

Learning-Based Dynamic Pinning of Parallelized Applications in Many-Core Systems

Chasparis

Rossbory

Janjić

et al. 2019

2019 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)

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“…In this sense, we only find that some pattern-based frameworks in the literature have pushed forward the development of such high-level patterns. For instance, the FastFlow [5] library recently provided the Stream-Pool [4] and the Windowed-Farm [13] patterns, two common structures in evolutionary and streamintensive applications, respectively. On the other hand, the MALLBA library [2] accommodates a collection of high-level skeletons for combinatorial optimization which deals with parallelism in a user-friendly and efficient manner.…”

Section: Paving the Way Towards High-level Parallel Pattern Interfaces For Data Stream Processingmentioning

confidence: 99%

“…Stream-Pool This pattern models the evolution of a population of individuals matching many evolutionary computing algorithms in the state-of-the-art [4]. Specifically, the Stream-Pool pattern is comprised of four different functions that are applied iteratively to the individuals of type α belonging to a population P managed as a stream (see Figure 2a).…”

Section: Advanced Stream Parallel Patternsmentioning

confidence: 99%

Paving the way towards high-level parallel pattern interfaces for data stream processing

Astorga

Dolz

Fernández

et al. 2018

Future Generation Computer Systems

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The emergence of data stream applications has posed a number of new challenges to existing infrastructures, processing engines and programming models. In this sense, high-level interfaces, encapsulating algorithmic aspects in pattern-based constructions, have considerable reduced the development and parallelization efforts of these type of applications. An example of parallel pattern interface is GrPPI, a C++ generic high-level library that acts as a layer between developers and existing parallel programming frameworks, such as C++ threads, OpenMP and Intel TBB. In this paper, we complement the basic patterns supported by GrPPI with the new stream operators Split-Join and Window, and the advanced parallel patterns Stream-Pool, Windowed-Farm and Stream-Iterator for the aforementioned back ends. Thanks to these new stream operators, complex compositions among streaming patterns can be expressed. On the other hand, the collection of advanced patterns allows users to tackle some domain-specific applications, ranging from the evolutionary to the real-time computing areas, where compositions of basic patterns are not capable of fully mimicking the algorithmic behavior of their original sequential codes. The experimental evaluation of the new advanced patterns and the stream operators on a set of domain-specific use-cases, using different back ends and pattern-specific parameters, reports remarkable performance gains with respect to the sequential versions. Additionally, we demonstrate the benefits of the GrPPI pattern interface from the usability, flexibility and maintainability points of view. IntroductionWith the rise of big data, services and instruments, such as mobile devices, social media and sensor networks, are constantly producing huge amounts of data [22]. This extreme on-line generated data growth poses profound challenges to existing processing engines, programming models and infrastructures. In this sense, existing big data application models, such as MapReduce, have become popular for batch processing. Nevertheless, these models cannot fulfill the strict requirements of low latency and high throughput demanded by data streaming applications (DaSP). To address these issues, the DaSP paradigm is a more adequate approach to deal with their real-time requirements [8]. Basically, the DaSP model considers that the data is not fully available nor stored in disk or memory, but rather they continuously arrive from one or more streams. The idea behind this paradigm is that data has to be processed as soon as it is received.

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“…Algorithmic skeletons [13] have been the focus of much research since the 1980s, with a number of skeleton libraries being produced in a range of languages [1,18]. These include our own Skel library [7], which is currently the only available skeleton library for Erlang.…”

Section: Related Workmentioning

confidence: 99%

The Missing Link! A New Skeleton for Evolutionary Multi-agent Systems in Erlang

Stypka

Turek

Byrski

et al. 2017

Int J Parallel Prog

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Evolutionary multi-agent systems (EMAS) play a critical role in many artificial intelligence applications that are in use today. In this paper, we present a new generic skeleton in Erlang for parallel EMAS computations. The skeleton enables us to capture a wide variety of concrete evolutionary computations that can exploit the same underlying parallel implementation. We demonstrate the use of our skeleton on two different evolutionary computing applications: (1) computing the minimum of the Rastrigin function; and (2) solving an urban traffic optimisation problem. We show that we can obtain very good speedups (up to 142.44× the sequential performance) B Christopher Brown

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Pool Evolution: A Parallel Pattern for Evolutionary and Symbolic Computing

Cited by 7 publications

References 11 publications

Learning-Based Dynamic Pinning of Parallelized Applications in Many-Core Systems

Learning-Based Dynamic Pinning of Parallelized Applications in Many-Core Systems

Paving the way towards high-level parallel pattern interfaces for data stream processing

The Missing Link! A New Skeleton for Evolutionary Multi-agent Systems in Erlang

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