Fine-grained or coarse-grained? Strategies for implementing parallel genetic algorithms in a programmable neuromorphic platform

Genetic Algorithm (GA) is one of popular heuristic-based optimization methods that attracts engineers and scientists for many years. With the advancement of multi- and many-core technologies, GAs are transformed into more powerful tools by parallelising their core processes. This paper describes a feasibility study of implementing parallel GAs (pGAs) on a SpiNNaker. As a many-core neuromorphic platform, SpiNNaker offers a possibility to scale-up a parallelised algorithm, such as a pGA, whilst offering low power consumption on its processing and communication overhead. However, due to its small packets distribution mechanism and constrained processing resources, parallelising processes of a GA in SpiNNaker is challenging. In this paper we show how a pGA can be implemented on SpiNNaker and analyse its performance. Due to inherently numerous parameter and classification of pGAs, we evaluate only the most common aspects of a pGA and use some artificial benchmarking test functions. The experiments produced some promising results that may lead to further developments of massively parallel GAs on SpiNNaker

A Novel Mechanism for Gridification of Compiled Java Applications

Exploiting Grids intuitively requires developers to alter their applications, which calls for expertise on Grid programming. Gridification tools address this problem by semi-automatically making user applications to be Grid-aware. However, most of these tools produce monolithic Grid applications in which common tuning mechanisms (e.g. parallelism) are not applicable, and do not reuse existing Grid middleware services. We propose BYG (BYtecode Gridifier), a gridification tool that relies on novel bytecode rewriting techniques to parallelize and easily execute existing applications via Grid middlewares. Experiments performed by using several computing intensive applications on a cluster and a simulated wide-area Grid suggest that our techniques are effective while staying competitive compared to programmatically using such services for gridifying applications

Mirroring Mobile Phone in the Clouds

This paper presents a framework of Mirroring Mobile Phone in the Clouds (MMPC) to speed up data/computing intensive applications on a mobile phone by taking full advantage of the super computing power of the clouds. An application on the mobile phone is dynamically partitioned in such a way that the heavy-weighted part is always running on a mirrored server in the clouds while the light-weighted part remains on the mobile phone. A performance improvement (an energy consumption reduction of 70% and a speed-up of 15x) is achieved at the cost of the communication overhead between the mobile phone and the clouds (to transfer the application codes and intermediate results) of a desired application. Our original contributions include a dynamic profiler and a dynamic partitioning algorithm compared with traditional approaches of either statically partitioning a mobile application or modifying a mobile application to support the required partitioning

An Efficient Ant Colony Optimization Framework for HPC Environments

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract] Combinatorial optimization problems arise in many disciplines, both in the basic sciences and in applied fields such as engineering and economics. One of the most popular combinatorial optimization methods is the Ant Colony Optimization (ACO) metaheuristic. Its parallel nature makes it especially attractive for implementation and execution in High Performance Computing (HPC) environments. Here we present a novel parallel ACO strategy making use of efficient asynchronous decentralized cooperative mechanisms. This strategy seeks to fulfill two objectives: (i) acceleration of the computations by performing the ants’ solution construction in parallel; (ii) convergence improvement through the stimulation of the diversification in the search and the cooperation between different colonies. The two main features of the proposal, decentralization and desynchronization, enable a more effective and efficient response in environments where resources are highly coupled. Examples of such infrastructures include both traditional HPC clusters, and also new distributed environments, such as cloud infrastructures, or even local computer networks. The proposal has been evaluated using the popular Traveling Salesman Problem (TSP), as a well-known NP-hard problem widely used in the literature to test combinatorial optimization methods. An exhaustive evaluation has been carried out using three medium and large size instances from the TSPLIB library, and the experiments show encouraging results with superlinear speedups compared to the sequential algorithm (e.g. speedups of 18 with 16 cores), and a very good scalability (experiments were performed with up to 384 cores improving execution time even at that scale).This work was supported by the Ministry of Science and Innovation of Spain (PID2019-104184RB-I00 / AEI / 10.13039/501100011033), and by Xunta de Galicia and FEDER funds of the EU (Centro de Investigación de Galicia accreditation 2019–2022, ref. ED431G 2019/01; Consolidation Program of Competitive Reference Groups, ref. ED431C 2021/30). JRB acknowledges funding from the Ministry of Science and Innovation of Spain MCIN / AEI / 10.13039/501100011033 through grant PID2020-117271RB-C22 (BIODYNAMICS), and from MCIN / AEI / 10.13039/501100011033 and “ERDF A way of making Europe” through grant DPI2017-82896-C2-2-R (SYNBIOCONTROL). Authors also acknowledge the Galician Supercomputing Center (CESGA) for the access to its facilities. Funding for open access charge: Universidade da Coruña/CISUGXunta de Galicia; ED431G 2019/01Xunta de Galicia; ED431C 2021/3

DNAgents: Genetically Engineered Intelligent Mobile Agents

Mobile agents are a useful paradigm for network coding providing many advantages and disadvantages. Unfortunately, widespread adoption of mobile agents has been hampered by the disadvantages, which could be said to outweigh the advantages. There is a variety of ongoing work to address these issues, and this is discussed. Ultimately, genetic algorithms are selected as the most interesting potential avenue. Genetic algorithms have many potential benefits for mobile agents. The primary benefit is the potential for agents to become even more adaptive to situational changes in the environment and/or emergent security risks. There are secondary benefits such as the natural obfuscation of functions inherent to genetic algorithms. Pitfalls also exist, namely the difficulty of defining a satisfactory fitness function and the variable execution time of mobile agents arising from the fact that it exists on a network. DNAgents 1.0, an original application of genetic algorithms to mobile agents is implemented and discussed, and serves to highlight these difficulties. Modifications of traditional genetic algorithms are also discussed. Ultimately, a combination of genetic algorithms and artificial life is considered to be the most appropriate approach to mobile agents. This allows the consideration of agents to be organisms, and the network to be their environment. Towards this end, a novel framework called DNAgents 2.0 is designed and implemented. This framework allows the continual evolution of agents in a network without having a seperate training and deployment phase. Parameters for this new framework were defined and explored. Lastly, an experiment similar to DNAgents 1.0 is performed for comparative purposes against DNAgents 1.0 and to prove the viability of this new framework