Parallel Genetic Algorithms with GPU Computing

Genetic algorithms (GAs) are powerful solutions to optimization problems arising from manufacturing and logistic fields. It helps to find better solutions for complex and difficult cases, which are hard to be solved by using strict optimization methods. Accelerating parallel GAs with GPU computing have received significant attention from both practitioners and researchers, ever since the emergence of GPU-CPU heterogeneous architectures. Designing a parallel algorithm on GPU is different fundamentally from designing one on CPU. On CPU architecture, typically data or tasks are distributed across tens of threads or processes, while on GPU architecture, more than hundreds of thousands of threads run. In order to fully utilize the computing power of GPUs, the design approaches and implementation strategies of parallel GAs should be re-probed. In the chapter, a concise overview of parallel GAs on GPU is given from the perspective of GPU architecture. The concept of parallelism granularity is redefined, the aspect of data layout is discussed on how it will affect the kernel performance, and the hierarchy of threads is examined on how threads are organized in the grid and blocks to expose sufficient parallelism to GPU. Some future research is discussed. A hybrid parallel model, based on the feature of GPU architecture, is suggested to build up efficient parallel GAs for hyper-scale problems

Graphics Processing Unit–Enhanced Genetic Algorithms for Solving the Temporal Dynamics of Gene Regulatory Networks

Understanding the regulation of gene expression is one of the key problems in current biology. A promising method for that purpose is the determination of the temporal dynamics between known initial and ending network states, by using simple acting rules. The huge amount of rule combinations and the nonlinear inherent nature of the problem make genetic algorithms an excellent candidate for finding optimal solutions. As this is a computationally intensive problem that needs long runtimes in conventional architectures for realistic network sizes, it is fundamental to accelerate this task. In this article, we study how to develop efficient parallel implementations of this method for the fine-grained parallel architecture of graphics processing units (GPUs) using the compute unified device architecture (CUDA) platform. An exhaustive and methodical study of various parallel genetic algorithm schemes—master-slave, island, cellular, and hybrid models, and various individual selection methods (roulette, elitist)—is carried out for this problem. Several procedures that optimize the use of the GPU’s resources are presented. We conclude that the implementation that produces better results (both from the performance and the genetic algorithm fitness perspectives) is simulating a few thousands of individuals grouped in a few islands using elitist selection. This model comprises 2 mighty factors for discovering the best solutions: finding good individuals in a short number of generations, and introducing genetic diversity via a relatively frequent and numerous migration. As a result, we have even found the optimal solution for the analyzed gene regulatory network (GRN). In addition, a comparative study of the performance obtained by the different parallel implementations on GPU versus a sequential application on CPU is carried out. In our tests, a multifold speedup was obtained for our optimized parallel implementation of the method on medium class GPU over an equivalent sequential single-core implementation running on a recent Intel i7 CPU. This work can provide useful guidance to researchers in biology, medicine, or bioinformatics in how to take advantage of the parallelization on massively parallel devices and GPUs to apply novel metaheuristic algorithms powered by nature for real-world applications (like the method to solve the temporal dynamics of GRNs)

A Lightweight Island Model for the Genetic Algorithm over GPGPU

This paper presents a parallel approach of the genetic algorithm (GA) over the Graphical Processing Unit (GPU) to solve the Traveling Salesman Problem (TSP). Since the earlier studies did not focus on implementing the island model in a persistent way, this paper introduces an approach, named Lightweight Island Model (LIM), that aims to implement the concept of persistent threads in the island model of the genetic algorithm. For that, we present the implementation details to convert the traditional island model, which is separated into multiple kernels, into a computing paradigm based on a persistent kernel. Many synchronization techniques, including cooperative groups and implicit synchronization, are discussed to reduce the CPU-GPU interaction that existed in the traditional island model. A new parallelization strategy is presented for distributing the work among live threads during the selection and crossover steps. The GPU configurations that lead to the best possible performance are also determined. The introduced approach will be compared, in terms of speedup and solution quality, with the traditional island model (TIM) as well as with related works that concentrated on suggesting a lighter version of the master-slave model, including switching among kernels (SAK) and scheduled light kernel (SLK) approaches. The results show that the new approach can increase the speed-up to 27x over serial CPU, 4.5x over the traditional island model, and up to 1.5–2x over SAK and SLK approaches

Parallel Multi-Objective Evolutionary Algorithms: A Comprehensive Survey

Multi-Objective Evolutionary Algorithms (MOEAs) are powerful search techniques that have been extensively used to solve difficult problems in a wide variety of disciplines. However, they can be very demanding in terms of computational resources. Parallel implementations of MOEAs (pMOEAs) provide considerable gains regarding performance and scalability and, therefore, their relevance in tackling computationally expensive applications. This paper presents a survey of pMOEAs, describing a refined taxonomy, an up-to-date review of methods and the key contributions to the field. Furthermore, some of the open questions that require further research are also briefly discussed

Recent Advances on GPU Computing in Operations Research

In the last decade, Graphics Processing Units (GPUs) have gained an increasing popularity as accelerators for High Performance Computing (HPC) applications. Recent GPUs are not only powerful graphics engines but also highly threaded parallel computing processors that can achieve sustainable speedup as compared with CPUs. In this context, researchers try to exploit the capability of this architecture to solve difficult problems in many domains in science and engineering. In this article, we present recent advances on GPU Computing in Operations Research. We focus in particular on Integer Programming and Linear Programming

Recent Advances on GPU Computing in Operations Research

Abstract-In the last decade, Graphics Processing Units (GPUs) have gained an increasing popularity as accelerators for High Performance Computing (HPC) applications. Recent GPUs are not only powerful graphics engines but also highly threaded parallel computing processors that can achieve sustainable speedup as compared with CPUs. In this context, researchers try to exploit the capability of this architecture to solve difficult problems in many domains in science and engineering. In this article, we present recent advances on GPU Computing in Operations Research. We focus in particular on Integer Programming and Linear Programming

Applied (Meta)-Heuristic in Intelligent Systems

Engineering and business problems are becoming increasingly difficult to solve due to the new economics triggered by big data, artificial intelligence, and the internet of things. Exact algorithms and heuristics are insufficient for solving such large and unstructured problems; instead, metaheuristic algorithms have emerged as the prevailing methods. A generic metaheuristic framework guides the course of search trajectories beyond local optimality, thus overcoming the limitations of traditional computation methods. The application of modern metaheuristics ranges from unmanned aerial and ground surface vehicles, unmanned factories, resource-constrained production, and humanoids to green logistics, renewable energy, circular economy, agricultural technology, environmental protection, finance technology, and the entertainment industry. This Special Issue presents high-quality papers proposing modern metaheuristics in intelligent systems

Systolic genetic search, a parallel metaheuristic for GPUs

La utilización de unidades de procesamiento gráfico (GPUs) para la resolución de problemas de propósito general ha experimentado un crecimiento vertiginoso en los últimos años, sustentado en su amplia disponibilidad, su bajo costo económico y en contar con una arquitectura inherentemente paralela, así como en la aparición de lenguajes de programación de propósito general que han facilitado el desarrollo de aplicaciones en estas plataformas. En este contexto, el diseño de nuevos algoritmos paralelos que puedan beneficiarse del uso de GPUs es una línea de investigación prometedora e interesante. Las metaheurísticas son algoritmos estocásticos capaces de encontrar soluciones muy precisas (muchas veces óptimas) a problemas de optimización en un tiempo razonable. Sin embargo, como muchos problemas de optimización involucran tareas que exigen grandes recursos computacionales y/o el tamaño de las instancias que se están abordando actualmente se están volviendo muy grandes, incluso las metaheurísticas pueden ser computacionalmente muy costosas. En este escenario, el paralelismo surge como una alternativa exitosa con el fin de acelerar la búsqueda de este tipo de algoritmos. Además de permitir reducir el tiempo de ejecución de los algoritmos, las metaheurísticas paralelas a menudo son capaces de mejorar la calidad de los resultados obtenidos por los algoritmos secuenciales tradicionales.Si bien el uso de GPUs ha representado un dominio inspirador también para la investigación en metaheurísticas paralelas, la mayoría de los trabajos previos tenían como objetivo portar una familia existente de algoritmos a este nuevo tipo de hardware. Como consecuencia, muchas publicaciones están dirigidas a mostrar el ahorro en tiempo de ejecución que se puede lograr al ejecutar los diferentes tipos paralelos de metaheurísticas existentes en GPU. En otras palabras, a pesar de que existe un volumen considerable de trabajo sobre este tópico, se han propuesto pocas ideas novedosas que busquen diseñar nuevos algoritmos y/o modelos de paralelismo que exploten explícitamente el alto grado de paralelismo disponible en las arquitecturas de las GPUs. Esta tesis aborda el diseño de una propuesta innovadora de algoritmo de optimización paralelo denominada Búsqueda Genética Sistólica (SGS), que combina ideas de los campos de metaheurísticas y computación sistólica. SGS, así como la computación sistólica, se inspiran en el mismo fenómeno biológico: la contracción sistólica del corazón que hace posible la circulación de la sangre. En SGS, las soluciones circulan de forma síncrona a través de una grilla (rejilla) de celdas. Cuando dos soluciones se encuentran en una celda se aplican operadores evolutivos adaptados para generar nuevas soluciones que continúan moviéndose a través de la grilla (rejilla). La implementación de esta nueva propuesta saca partido especialmente de las características específicas de las GPUs. Un extenso análisis experimental que considera varios problemas de benchmark clásicos y dos problemas del mundo real del área de Ingeniería de Software, muestra que el nuevo algoritmo propuesto es muy efectivo, encontrando soluciones óptimas o casi óptimas en tiempos de ejecución cortos. Además, los resultados numéricos obtenidos por SGS son competitivos con los resultados del estado del arte para los dos problemas del mundo real en cuestión. Por otro lado, la implementación paralela en GPU de SGS ha logrado un alto rendimiento, obteniendo grandes reducciones de tiempo de ejecución con respecto a la implementación secuencial y mostrando que escala adecuadamente cuando se consideran instancias de tamaño creciente. También se ha realizado un análisis teórico de las capacidades de búsqueda de SGS para comprender cómo algunos aspectos del diseño del algoritmo afectan a sus resultados numéricos. Este análisis arroja luz sobre algunos aspectos del funcionamiento de SGS que pueden utilizarse para mejorar el diseño del algoritmo en futuras variantes

The Parallel Genetic Algorithm for Multicore Systems

Genetický algoritmus je optimalizačná metóda zameraná na efektívne hľadanie riešení rozličných problémov. Je založená na princípe evolúcie a prirodzeného výberu najschopnejších jedincov v prírode. Keďže je táto metóda výpočtovo náročná, bolo vymyslených veľa spôsobov na jej paralelizáciu. Avšak väčšina týchto metód je z historických dôvodov založená na superpočítačoch alebo rozsiahlych počítačových systémoch. Moderný vývoj v oblasti informačných technológií prináša na trh osobných počítačov stále lacnejšie a výkonnejšie viacjadrové systémy. Táto práca sa zaoberá návrhom nových metód paralelizácie genetického algoritmu, ktoré sa snažia naplno využiť možnosti práve týchto počítačových systémov. Tieto metódy sú následne naimplementované v programovacom jazyku C za využitia knižnice OpenMP určenej na paralelizáciu. Implementácia je následne použitá na experimentálne ohodnotenie rozličných charakteristík každej z prezentovaných metód (zrýchlenie oproti sekvenčnej verzii, závislosť konvergencie výsledných hodnôt od miery paralelizácie alebo od vyťaženia procesoru, ...). V poslednej časti práce sú prezentované porovnania nameraných hodnôt a závery vyplývajúce z týchto meraní. Následne sú prediskutované možné vylepšenia daných metód vyplývajúce z týchto záverov, ako aj možnosti spracovania väčšieho množstva charakteristík na presnejšie ohodnotenie efektivity paralelizácie genetických algoritmov.Genetic algorithm is a powerful optimization and search method successfully used in practice to solve many different problems. Underlying concept is based on the evolutionary mechanics observed in nature. As the GAs are computationaly intense applications, it is natural that there are many efficient methods for parallelization of these algorithms. However, most of these methods deal with supercomputers or large computer clusters with specialized hardware, as these were the most common parallel architectures in the past. With modern-day computers the trend in personal computer design is also moving towards parallel architectures bringing small and cheap parallel multicore processors. That's why it is imperative to have efficient methods to exploit capabilities of this system. This document presents prototypes of new methods of parallel genetic algorithms designed especially for these multiprocessor computers with shared memory.