A comparative study of high-productivity high-performance programming languages for parallel metaheuristics

Gmys, Jan; Carneiro, Tiago; Melab, Nouredine; Talbi, El-Ghazali; Tuyttens, Daniel

doi:10.1016/j.swevo.2020.100720

Cited by 24 publications

(10 citation statements)

References 30 publications

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“…), which indeed are quite popular in the remote sensing community, are generally not recommended for heavy numerical computation. This is for two main reasons: First, they generally do not allow the programmer to explicitly specify the desired parallelism scheme because of a general lack in specific support for efficient parallel computing; second, they are significantly slower in terms of execution time, thus in some cases suffering from severe performance penalties [45].…”

Section: Openmp-based Parallel Implementationmentioning

confidence: 99%

Multithreading Based Parallel Processing for Image Geometric Coregistration in SAR Interferometry

Imperatore

Sansosti

2021

Remote Sensing

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Within the framework of multi-temporal Synthetic Aperture Radar (SAR) interferometric processing, image coregistration is a fundamental operation that might be extremely time-consuming. This paper explores the possibility of addressing fast and accurate SAR image geometric coregistration, with sub-pixel accuracy and in the presence of a complex 3-D object scene, by exploiting the parallelism offered by shared-memory architectures. An efficient and scalable processor is proposed by designing a parallel algorithm incorporating thread-level parallelism for solving the inherent computationally intensive problem. The adopted functional scheme is first mathematically framed and then investigated in detail in terms of its computational structures. Subsequently, a parallel version of the algorithm is designed, according to a fork-join model, by suitably taking into account the granularity of the decomposition, load-balancing, and different scheduling strategies. The developed parallel algorithm implements parallelism at the thread-level by using OpenMP (Open Multi-Processing) and it is specifically targeted at shared-memory multiprocessors. The parallel performance of the implemented multithreading-based SAR image coregistration prototype processor is experimentally investigated and quantitatively assessed by processing high-resolution X-band COSMO-SkyMed SAR data and using two different multicore architectures. The effectiveness of the developed multithreaded prototype solution in fully benefitting from the computing power offered by multicore processors has successfully been demonstrated via a suitable experimental performance analysis conducted in terms of parallel speedup and efficiency. The demonstrated scalable performance and portability of the developed parallel processor confirm its potential for operational use in the interferometric SAR data processing at large scales.

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Section: Openmp-based Parallel Implementationmentioning

confidence: 99%

Multithreading Based Parallel Processing for Image Geometric Coregistration in SAR Interferometry

Imperatore

Sansosti

2021

Remote Sensing

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“…This is a very useful property for a parallel implementation, requiring only N − 1 synchronizations to process O(KN 2 (1 + M)) operations. Hence, a parallel implementation of Algorithm 5 is straightforward in a shared memory parallelization, using OpenMP for instance in C/C++, or higher-level programming languages such as Python, Julia or Chapel [60]. One may also consider an intensive parallelization in a many-core environment, such as General Purpose Graphical Processing Units (GPGPU).…”

Section: Towards a Parallel Implementationmentioning

confidence: 99%

Unified Polynomial Dynamic Programming Algorithms for P-Center Variants in a 2D Pareto Front

2021

Self Cite

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With many efficient solutions for a multi-objective optimization problem, this paper aims to cluster the Pareto Front in a given number of clusters K and to detect isolated points. K-center problems and variants are investigated with a unified formulation considering the discrete and continuous versions, partial K-center problems, and their min-sum-K-radii variants. In dimension three (or upper), this induces NP-hard complexities. In the planar case, common optimality property is proven: non-nested optimal solutions exist. This induces a common dynamic programming algorithm running in polynomial time. Specific improvements hold for some variants, such as K-center problems and min-sum K-radii on a line. When applied to N points and allowing to uncover M<N points, K-center and min-sum-K-radii variants are, respectively, solvable in O(K(M+1)NlogN) and O(K(M+1)N2) time. Such complexity of results allows an efficient straightforward implementation. Parallel implementations can also be designed for a practical speed-up. Their application inside multi-objective heuristics is discussed to archive partial Pareto fronts, with a special interest in partial clustering variants.

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“…For the tests carried out, firstly, a base version of the Rust algorithm was used and the impact of applying each optimization incrementally was analyzed 9 . Then, the best version of Rust was selected to compare with its C equivalent.…”

Section: Experimental Designmentioning

confidence: 99%

“…This is why, in recent years, many programming languages with a high level of abstraction 2 have tried to add support for concurrency and parallelism, in an attempt to compete with C and Fortran. Among these, we can mention Java [6,7] and Python [8,9]; however, unfortunately, neither has been successful at becoming an alternative in the HPC community for the time being.…”

Section: Introductionmentioning

confidence: 99%

Performance vs Programming Effort between Rust and C on Multicore Architectures: Case Study in N-Body

Costanzo¹,

Rucci²,

Naiouf³

et al. 2021

Preprint

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Historically, Fortran and C have been the default programming languages in High-Performance Computing (HPC). In both, programmers have primitives and functions available that allow manipulating system memory and interacting directly with the underlying hardware, resulting in efficient code in both response times and resource use. On the other hand, it is a real challenge to generate code that is maintainable and scalable over time in these types of languages. In 2010, Rust emerged as a new programming language designed for concurrent and secure applications, which adopts features of procedural, object-oriented and functional languages. Among its design principles, Rust is aimed at matching C in terms of efficiency, but with increased code security and productivity. This paper presents a comparative study between C and Rust in terms of performance and programming effort, selecting as a case study the simulation of N computational bodies (N-Body), a popular problem in the HPC community. Based on the experimental work, it was possible to establish that Rust is a language that reduces programming effort while maintaining acceptable performance levels, meaning that it is a possible alternative to C for HPC.

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A comparative study of high-productivity high-performance programming languages for parallel metaheuristics

Cited by 24 publications

References 30 publications

Multithreading Based Parallel Processing for Image Geometric Coregistration in SAR Interferometry

Multithreading Based Parallel Processing for Image Geometric Coregistration in SAR Interferometry

Unified Polynomial Dynamic Programming Algorithms for P-Center Variants in a 2D Pareto Front

Performance vs Programming Effort between Rust and C on Multicore Architectures: Case Study in N-Body

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