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Special issue on Parallel and distributed computing based on the functional programming paradigm
Author(s) -
Turek Wojciech,
Byrski Aleksander,
Hughes John,
Hammond Kevin,
Zaionc Marek
Publication year - 2018
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4842
Subject(s) - computer science , scalability , distributed computing , multi core processor , scheduling (production processes) , process (computing) , transparency (behavior) , parallel computing , programming language , computer security , mathematical optimization , mathematics , database
Over a decade after the beginning of the multicore revolution, researchers and industry are still struggling with problems related to using concurrent hardware. Discovering or developing proper means for creating efficient, scalable, and adaptable software for multicore and multimode computers is still an open and a very important problem. Great efforts are made to solve problems related to the efficiency of resource utilization,1-3 monitoring and failure handling,4,5 and, most importantly, development of highly concurrent systems.6-8 In addition to much work on adapting existing imperative technologies to the new challenges, we can observe a very interesting trend toward using the functional paradigm. The concepts of functional programming languages are very well suited to concurrent systems. Referential transparency, lazy evaluation, control over side-effects, immutable variables, and functions as first-class citizens help define maintainable and scalable solutions to the basic problems of parallel computing. The advantages of such an approach are clearly visible in HPC environments, where massively parallel solutions are needed. Providing novel services and solutions for the complex real-life problems of modern societies creates a growing demand for very fast solutions to computationally demanding problems. For example, planning complex urban road systems requires gathering results from large-scale simulations,9,10 which are only feasible using massive parallelization. Similarly, in real-time scheduling tasks, complex optimization problems have to be solved within a single second.11,12 Such challenges can greatly benefit from technologies based on the functional paradigm, which simplify the development process and lower the barrier for utilizing modern HPC hardware.