Domingo Giménez

Parameterized Schemes of Metaheuristics: Basic Ideas and Applications With Genetic Algorithms, Scatter Search, and GRASP

Some optimization problems can be tackled only with metaheuristic methods, and to obtain a satisfactory metaheuristic, it is necessary to develop and experiment with various methods and to tune them for each particular problem. The use of a unified scheme for metaheuristics facilitates the development of metaheuristics by reutilizing the basic functions. In our proposal, the unified scheme is improved by adding transitional parameters. Those parameters are included in each of the functions, in such a way that different values of the parameters provide different metaheuristics or combinations of metaheuristics. Thus, the unified parameterized scheme eases the development of metaheuristics and their application. In this paper, we expose the basic ideas of the parameterization of metaheuristics. This methodology is tested with the application of local and global search methods (greedy randomized adaptive search procedure [GRASP], genetic algorithms, and scatter search), and their combinations, to three scientific problems: obtaining satisfactory simultaneous equation models from a set of values of the variables, a task-to-processor assignment problem with independent tasks and memory constrains, and the p-hub median location-allocation problem.

Architecture of an automatically tuned linear algebra library

One approach for a hierarchical architecture of a set of linear algebra libraries with self-optimisation capacity is shown. In previous works the optimisation of several routines was studied separately, and in this work the ideas applied to individual routines are combined with the classical hierarchy of linear algebra libraries. Each self-optimised library consists of the former routines of the library and additional special routines which obtain information of the characteristics on the system and tune certain parameters of the former routines accordingly. The relationship between libraries of the different levels of the hierarchy is also strengthened. Just as each routine has in its code different calls to lower levels, so this routine will use the self-optimisation information of these other routines to generate its own information. Experiments with routines of different levels and on different kinds of platforms with constant, variable and heterogeneous load have been carried out. The results obtained allow us to conclude that the proposed methodology is valid for obtaining self-optimised linear algebra libraries.

Heuristics for work distribution of a homogeneous parallel dynamic programming scheme on heterogeneous systems

In this paper the possibility of including automatic optimization techniques in the design of parallel dynamic programming algorithms in heterogeneous systems is analyzed. The main idea is to automatically approach the optimum values of a number of algorithmic parameters (number of processes, number of processors, processes per processor), and thus obtain low execution times. Hence, users could be provided with routines which execute efficiently, and independently of the experience of the user in heterogeneous computing and dynamic programming, and which can adapt automatically to a new network of processors or a new network configuration.

Automatic optimisation of parallel linear algebra routines in systems with variable load

The architecture of an automatically tuned linear algebra library proposed in previous works is extended in order to adapt itself to platforms where both the CPU load and the network traffic vary. During the installation process in a system, the linear algebra routines will be tuned automatically to the system conditions: hardware characteristics and basic libraries used in the linear algebra routines. At run-time the parameters that define the system characteristics are adjusted to the actual load of the platform. The design methodology is analysed with a block LU factorisation. Variants for sequential and parallel versions of this routine on a logical rectangular mesh of processors are considered. The behavior of the algorithm is studied with message-passing, using MPI on a cluster of PCs. The experiments show that the configurable parameters of the linear algebra routines can be adjusted during the run-time process despite the variability of the environment.

Modeling Shared-Memory Metaheuristic Schemes for Electricity Consumption

This paper tackles the problem of modeling a shared-memory metaheuristic scheme. The use of a model of the execution time allows us to decide at running time the number of threads to use to obtain a reduced execution time. A parameterized metaheuristic scheme is used, so different metaheuristics and hybridations can be applied to a particular problem, and it is easier to obtain a satisfactory metaheuristic for the problem. The model of the execution time and consequently the optimum number of threads depend on a number of factors: the problem to be solved, the metaheuristic scheme and the implementation of the basic functions in it, the computational system where the problem is being solved, etc. So, obtaining a satisfactory model and an autotuning methodology is not an easy task. This paper presents an autotuning methodology for shared-memory parameterized metaheuristic schemes, and its application to a problem of minimization of electricity consumption in exploitation of wells. The model and the methodology work satisfactorily, which allows us to reduce the execution time and to obtain lower electricity consumptions than previously obtained.

Towards the design of an automatically tuned linear algebra library

In this work we propose the architecture of an automatically tuned linear algebra library, which is composed by a set of linear algebra routines along with their installation routines. During the installation process on a system, the linear algebra routines will be tuned automatically to the system conditions: hardware characteristics and basic libraries used in the linear algebra routines. The design methodology is analysed with a block LU factorisation. Variants for a sequential and parallel version of this, routine on a logical rectangular mesh of processors are, considered. An analytical model of the algorithm is developed as the basis of our methodology, and the behaviour of the algorithm is analysed with message-passing using MPI on several platforms: Network of SUN workstations, SGI Origin 2000 and IBM SP2, and with, different basic linear algebra libraries: reference BLAS, machine-specific BLAS and ATLAS. The experiments show that it is possible to make a good automatic choice of configurable parameters of the linear algebra routines during the installation process. The average execution time of the linear algebra routine is reduced by about 15% with respect to the non-tuned version.

Processes Distribution of Homogeneous Parallel Linear Algebra Routines on Heterogeneous Clusters

This paper presents a self-optimization methodology for parallel linear algebra routines on heterogeneous systems. For each routine, a series of decisions is taken automatically in order to obtain an execution time close to the optimum (without rewriting the routines code). Some of these decisions are: the number of processes to generate, the heterogeneous distribution of these processes over the network of processors, the logical topology of the generated processes,... To reduce the search space of such decisions, different heuristics have been used. The experiments have been performed with a parallel LU factorization routine similar to the ScaLAPACK one, and good results have been obtained on different heterogeneous platforms

METADOCK: A parallel metaheuristic schema for virtual screening methods:

Virtual screening through molecular docking can be translated into an optimization problem, which can be tackled with metaheuristic methods. The interaction between two chemical compounds (typically a protein, enzyme or receptor, and a small molecule, or ligand) is calculated by using highly computationally demanding scoring functions that are computed at several binding spots located throughout the protein surface. This paper introduces METADOCK, a novel molecular docking methodology based on parameterized and parallel metaheuristics and designed to leverage heterogeneous computers based on heterogeneous architectures. The application decides the optimization technique at running time by setting a configuration schema. Our proposed solution finds a good workload balance via dynamic assignment of jobs to heterogeneous resources which perform independent metaheuristic executions when computing different molecular interactions required by the scoring functions in use. A cooperative scheduling of jobs optimizes the quality of the solution and the overall performance of the simulation, so opening a new path for further developments of virtual screening methods on high-performance contemporary heterogeneous platforms.

Optimization Techniques for 3D-FWT on Systems with Manycore GPUs and Multicore CPUs☆

Abstract Programming manycore GPUs or multicore CPUs for high performance requires a careful balance of several hardware specific related factors, which is typically achieved by expert users through trial and error. To reduce the amount of hand-made optimization time required to achieve optimal performance, general guidelines can be followed or different metrics can be considered to predict performance, but ultimately a trial and error process is still prevalent. In this paper, we present an optimization method to run the 3D-Fast Wavelet Transform (3D-FWT) on hybrid systems. The optimization engine detects the different platforms found on a system, executing the appropriate kernel, implemented in both CUDA or OpenCL for GPUs, and programmed with pthreads for a CPU. Moreover, the proposed method selects automatically parameters such as the block size, the work-group size or the number of threads for reducing the execution time, obtaining the optimal performance in many cases. Finally, the optimization engine sends proportionally different parts of a video sequence to run concurrently in all platforms of the system. Speedups with respect to a normal user, who sends all frames to a GPU with a version of the 3D-FWT implemented in CUDA or OpenCL, presents an averaged gains of up to 7.93.

Designing polylibraries to speed up linear algebra computations

In this paper, we analyse the design of polylibraries, where the programs call for routines from different libraries according to the characteristics of the problem and of the system used to solve it. An architecture for this type of library is proposed. Our aim is to develop a methodology which can be used in the design of parallel libraries. To evaluate the viability of the proposed method, the typical linear algebra libraries hierarchy has been considered. Experiments have been performed in different systems and with linear algebra routines from different levels of the hierarchy. The results confirm the design of polylibraries as a good technique for speeding up computations.