Francisco Almeida

MALLBA: a library of skeletons for combinatorial optimisation

The MALLBA project tackles the resolution of combinatorial optimization problems using algorithmic skeletons implemented in C++. mallba offers three families of generic resolution methods: exact, heuristic and hybrid. Moreover, for each resolution method, MALLBA provides three different implementations: sequential, parallel for local area networks, and parallel for wide area networks (currently under development). This paper explains the architecture of the MALLBA library, presents some of its skeletons, and offers several computational results to show the viability of the approach.

Efficient parallel LAN/WAN algorithms for optimization: the MALLBA project

The MALLBA project tackles the resolution of combinatorial optimization problems using generic algorithmic skeletons implemented in C++. A skeleton in the MALLBA library implements an optimization method in one of the three families of generic optimization techniques offered: exact, heuristic and hybrid. Moreover, for each of those methods, MALLBA provides three different implementations: sequential, parallel for Local Area Networks, and parallel for Wide Area Networks. This paper introduces the architecture of the MALLBA library, details some of the implemented skeletons, and offers computational results for some classical optimization problems to show the viability of our library. Among other conclusions, we claim that the design used to develop the optimization techniques included in the library is generic and efficient at the same time.

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.

Dynamic Load Balancing on Dedicated Heterogeneous Systems

Parallel computing in heterogeneous environments is drawing considerable attention due to the growing number of these kind of systems. Adapting existing code and libraries to such systems is a fundamental problem. The performance of this code is affected by the large interdependence between the code and these parallel architectures. We have developed a dynamic load balancing library that allows parallel code to be adapted to heterogeneous systems for a wide variety of problems. The overhead introduced by our system is minimal and the cost to the programmer negligible. The strategy was validated on several problems to confirm the soundness of our proposal.

The master-slave paradigm on heterogeneous systems: a dynamic programming approach for the optimal mapping

We study the master-slave paradigm over heterogeneous systems. According to an analytical model, we develop a dynamic programming algorithm that allows to solve the optimal mapping for such paradigm. Our proposal considers heterogeneity due both to computation and also to communication. The optimization strategy used allows us to obtain the set of processors for an optimal computation. The computational results show that considering heterogeneity also on the communication increases the performance of the parallel algorithm.

Integral knapsack problems: parallel algorithms and their implementations on distributed systems

The parallelization of the dynamic programming algorithm for the integral knapsack problem is approached from several perspectives. Two of them proceed by dividing the set of objects, while a third one proceeds by partitioning the set of capacities. Furthermore, we propose a new sequential algorithm and its parallelization by reducing the integral knapsack problem to a maximum path problem. The theoretical complexity analysis of the algorithms proves that for all the algorithms the product of the number of processors by the parallel time equals the corresponding sequential time. Computational results are presented both for transputer networks using occam and LAN using PVM. Although for many cases the best running times are obtained for the LAN, the speedup and the scalability are better for the transputer network.

Towards the Dynamic Load Balancing on Heterogeneous Multi-GPU Systems.

The advent of multicore systems, joined to the potential acceleration of the graphics processing units, alleviates some well known important architectural problems at the expense of a considerable increment of the programmability wall. The heterogeneity, both at architectural and programming level at the same time, raises the programming difficulties. Adapting existing code and libraries to such systems is a fundamental problem. The performance of this code is affected by the large interdependence between the code and the parallel architecture. We have developed a dynamic load balancing library that allows parallel code to be adapted to a wide variety of heterogeneous systems. The overhead introduced by our system is minimal and the cost to the programmer negligible. This system has been applied to solve load imbalance problems appearing in homogeneous and heterogeneous multi-GPU platforms. As case studies, we consider matrix multiply and resource allocation problems, in different heterogeneous scenarios in multi-GPU systems. The unbalanced nature of these algorithms and heterogeneous systems allowed us to test the success of our load balancing approach.

Towards the automatic optimal mapping of pipeline algorithms

The assignment of computations to processors is a crucial factor determining the effectiveness of a parallel algorithm. The portability of parallel programs has involved lot of effort during the last decade. However, the performance of a parallel code suffers, in many cases, from inherent effects of the target architectures. The optimal mapping of a parallel program is strongly dependent on the granularity and network architecture. We focus on the problem of finding the optimal mapping of pipeline algorithms on a ring of processors. We propose an analytical model that allows an easy estimation of the parameters needed to obtain the mapping. The model can be introduced in a suitable tool to automatically produce this mapping. Both the accuracy of the model and the optimal efficiency of the algorithm found are contrasted on pipeline algorithms for the knapsack problem, for the resource allocation problem and for the path planning problem.

Design of parallel algorithms for the single resource allocation problem

Abstract Three new optimal parallel algorithms are presented for the single resource allocation problem. They run in the simplest networks: pipelines and rings. All of them have been implemented using PVM and MPI. Four representative platforms of the current state of parallel computing hardware were used for the experiences presented in this paper: the IBM SP2, the Cray T3E, the Silicon Origin 2000 and the Digital Alpha Server 8400. Computational results prove the good scalability of these three algorithms for practical cases.

Adaptive load balancing of iterative computation on heterogeneous nondedicated systems

Dynamic load balancing in heterogeneous systems is a fundamental research topic in parallel computing due to the high availability of such systems. The efficient utilization of the heterogeneous resources can significantly enhance the performance of the parallel system. At the same time, adapting parallel codes to state-of-the-art parallel computers composed of heterogeneous multinode–multicore processors becomes a very hard task because parallel codes are highly dependent on the parallel architectures. That means that applications must be tailored requiring a great deal of programming effort. We have developed the ALBIC (Adaptive Load Balancing of Iterative Computation) system that allows for the dynamic load balancing of iterative codes in heterogeneous dedicated and nondedicated Linux based systems. In order to validate the system several parallel codes have been analyzed in different scenarios. The results show that the ALBIC approach achieves better performance than the other proposal. This lightweighted library eases porting homogeneous parallel codes to heterogeneous platforms, since the code intrusion is low and the programming effort is quite reduced.