Abstract
A parallel system to solve complex computational problems involve multiple instruction, simultaneous flows, communication structures, synchronisation and competition conditions between processes, as well as mapping and balance of workload in each processing unit. The algorithm design and the facilities of processing units will affect the cost-performance ratio of any algorithm. We propose a generic methodology to capture the main characteristics of parallel algorithm design methodologies, and to add the experience of expert programmers through pattern languages. Robust design considering the relations between architectures and programs is a crucial item to implement high-quality parallel algorithms. We aim for a methodology to exploit algorithmic concurrencies and to establish optimal process allocation into processing units, exploring the lowest implementation details. Some basic examples are described, such as the k-means algorithm, to illustrate and to show the effectiveness of our methodology. Our proposal identifies essential design patterns to find models of Data Mining algorithms with string self-adaptive mechanisms for homogeneous and heterogeneous parallel architectures.
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Acknowledgment
The authors thank the financial support by the Mexican CONACyT, as well as ABACUS: Laboratory of Applied Mathematics and High-Performance Computing of the Mathematics Department of CINVESTAV-IPN. Our institution provided the facilities to accomplish this work.
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Serrano-Rubio, A.A., Meneses-Viveros, A., Morales-Luna, G.B., Paredes-López, M. (2019). Generic Methodology for the Design of Parallel Algorithms Based on Pattern Languages. In: Torres, M., Klapp, J., Gitler, I., Tchernykh, A. (eds) Supercomputing. ISUM 2018. Communications in Computer and Information Science, vol 948. Springer, Cham. https://doi.org/10.1007/978-3-030-10448-1_4
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