Hardware-accelerated parallel genetic algorithm for fitness functions with variable execution times

Genetic Algorithms (GAs) following a parallel master-slave architecture can be effectively used to reduce searching time when fitness functions have fixed execution time. This paper presents a parallel GA architecture along with two accelerated GA operators to enhance the performance of master-slave GAs, specially when considering fitness functions with variable execution times. We explore the performance of the proposed approach, and analyse its effectiveness against the state-of-the-art. The results show a significant improvement in search times and fitness function utilisation, thus potentially enabling the use of this approach as a faster searching tool for timing-sensitive optimisation processes such as those found in dynamic real-time systems

Paraiso : An Automated Tuning Framework for Explicit Solvers of Partial Differential Equations

We propose Paraiso, a domain specific language embedded in functional programming language Haskell, for automated tuning of explicit solvers of partial differential equations (PDEs) on GPUs as well as multicore CPUs. In Paraiso, one can describe PDE solving algorithms succinctly using tensor equations notation. Hydrodynamic properties, interpolation methods and other building blocks are described in abstract, modular, re-usable and combinable forms, which lets us generate versatile solvers from little set of Paraiso source codes. We demonstrate Paraiso by implementing a compressive hydrodynamics solver. A single source code less than 500 lines can be used to generate solvers of arbitrary dimensions, for both multicore CPUs and GPUs. We demonstrate both manual annotation based tuning and evolutionary computing based automated tuning of the program.Comment: 52 pages, 14 figures, accepted for publications in Computational Science and Discover

Integration through genetic programming on heterogeneous systems.

Nowadays, numerous applications in various scientific fields require the integration of mathematical functions that, due to some of their characteristics, do not have an analytical expression for their antiderivative. These definite integrals are usually solved by numerical integration methods, which provide an approximation of the numerical value of the integral in the integration range. With this type of solutions, a higher precision of the approximation entails a longer computation time, being necessary a trade-off between both aspects. In this work we present a genetic programming algorithm which provides mathematical expressions that approximate the antiderivative of analytically non-integrable functions. Heterogeneous devices, GPU and multicore CPU, have also been used in the development of the system to accelerate the parts suitable for it. The advantage of obtaining these approximate antiderivatives is the reduction of the computation time necessary to calculate the definite integral of the functions of interest, reducing it to simply evaluating the expression at the beginning and the end of the integration range.<br /

JPEG steganography with particle swarm optimization accelerated by AVX

Digital steganography aims at hiding secret messages in digital data transmitted over insecure channels. The JPEG format is prevalent in digital communication, and images are often used as cover objects in digital steganography. Optimization methods can improve the properties of images with embedded secret but introduce additional computational complexity to their processing. AVX instructions available in modern CPUs are, in this work, used to accelerate data parallel operations that are part of image steganography with advanced optimizations.Web of Science328art. no. e544

High performance graph analysis on parallel architectures

PhD ThesisOver the last decade pharmacology has been developing computational methods to enhance drug development and testing. A computational method called network pharmacology uses graph analysis tools to determine protein target sets that can lead on better targeted drugs for diseases as Cancer. One promising area of network-based pharmacology is the detection of protein groups that can produce better e ects if they are targeted together by drugs. However, the e cient prediction of such protein combinations is still a bottleneck in the area of computational biology. The computational burden of the algorithms used by such protein prediction strategies to characterise the importance of such proteins consists an additional challenge for the eld of network pharmacology. Such computationally expensive graph algorithms as the all pairs shortest path (APSP) computation can a ect the overall drug discovery process as needed network analysis results cannot be given on time. An ideal solution for these highly intensive computations could be the use of super-computing. However, graph algorithms have datadriven computation dictated by the structure of the graph and this can lead to low compute capacity utilisation with execution times dominated by memory latency. Therefore, this thesis seeks optimised solutions for the real-world graph problems of critical node detection and e ectiveness characterisation emerged from the collaboration with a pioneer company in the eld of network pharmacology as part of a Knowledge Transfer Partnership (KTP) / Secondment (KTS). In particular, we examine how genetic algorithms could bene t the prediction of protein complexes where their removal could produce a more e ective 'druggable' impact. Furthermore, we investigate how the problem of all pairs shortest path (APSP) computation can be bene ted by the use of emerging parallel hardware architectures as GPU- and FPGA- desktop-based accelerators. In particular, we address the problem of critical node detection with the development of a heuristic search method. It is based on a genetic algorithm that computes optimised node combinations where their removal causes greater impact than common impact analysis strategies. Furthermore, we design a general pattern for parallel network analysis on multi-core architectures that considers graph's embedded properties. It is a divide and conquer approach that decomposes a graph into smaller subgraphs based on its strongly connected components and computes the all pairs shortest paths concurrently on GPU. Furthermore, we use linear algebra to design an APSP approach based on the BFS algorithm. We use algebraic expressions to transform the problem of path computation to multiple independent matrix-vector multiplications that are executed concurrently on FPGA. Finally, we analyse how the optimised solutions of perturbation analysis and parallel graph processing provided in this thesis will impact the drug discovery process.This research was part of a Knowledge Transfer Partnership (KTP) and Knowledge Transfer Secondment (KTS) between e-therapeutics PLC and Newcastle University. It was supported as a collaborative project by e-therapeutics PLC and Technology Strategy boar

Digital Ecosystems: Ecosystem-Oriented Architectures

We view Digital Ecosystems to be the digital counterparts of biological ecosystems. Here, we are concerned with the creation of these Digital Ecosystems, exploiting the self-organising properties of biological ecosystems to evolve high-level software applications. Therefore, we created the Digital Ecosystem, a novel optimisation technique inspired by biological ecosystems, where the optimisation works at two levels: a first optimisation, migration of agents which are distributed in a decentralised peer-to-peer network, operating continuously in time; this process feeds a second optimisation based on evolutionary computing that operates locally on single peers and is aimed at finding solutions to satisfy locally relevant constraints. The Digital Ecosystem was then measured experimentally through simulations, with measures originating from theoretical ecology, evaluating its likeness to biological ecosystems. This included its responsiveness to requests for applications from the user base, as a measure of the ecological succession (ecosystem maturity). Overall, we have advanced the understanding of Digital Ecosystems, creating Ecosystem-Oriented Architectures where the word ecosystem is more than just a metaphor.Comment: 39 pages, 26 figures, journa

High-Performance Parallel Implementation of Genetic Algorithm on FPGA

Genetic algorithms (GAs) are used to solve search and optimization problems in which an optimal solution can be found using an iterative process with probabilistic and non-deterministic transitions. However, depending on the problem’s nature, the time required to find a solution can be high in sequential machines due to the computational complexity of genetic algorithms. This work proposes a full-parallel implementation of a genetic algorithm on field-programmable gate array (FPGA). Optimization of the system’s processing time is the main goal of this project. Results associated with the processing time and area occupancy (on FPGA) for various population sizes are analyzed. Studies concerning the accuracy of the GA response for the optimization of two variables functions were also evaluated for the hardware implementation. However, the high-performance implementation proposed in this paper is able to work with more variable from some adjustments on hardware architecture. The results showed that the GA full-parallel implementation achieved throughput about 16 millions of generations per second and speedups between 17 and 170,000 associated with several works proposed in the literature

GPU Implementation of DPSO-RE Algorithm for Parameters Identification of Surface PMSM Considering VSI Nonlinearity

In this paper, an accurate parameter estimation model of surface permanent magnet synchronous machines (SPMSMs) is established by taking into account voltage-source-inverter (VSI) nonlinearity. A fast dynamic particle swarm optimization (DPSO) algorithm combined with a receptor editing (RE) strategy is proposed to explore the optimal values of parameter estimations. This combination provides an accelerated implementation on graphics processing unit (GPU), and the proposed method is, therefore, referred to as G-DPSORE. In G-DPSO-RE, a dynamic labor division strategy is incorporated into the swarms according to the designed evolutionary factor during the evolution process. Two novel modifications of the movement equation are designed to update the velocity of particles. Moreover, a chaotic-logistic-based immune RE operator is developed to facilitate the global best individual (gBest particle) to explore a potentially better region. Furthermore, a GPU parallel acceleration technique is utilized to speed up parameter estimation procedure. It has been demonstrated that the proposed method is effective for simultaneous estimation of the PMSM parameters and the disturbance voltage (Vdead) due to VSI nonlinearity from experimental data for currents and rotor speed measured with inexpensive equipment. The influence of the VSI nonlinearity on the accuracy of parameter estimation is analyzed