Giandomenico Spezzano

A parallel cellular tool for interactive modeling and simulation

The paper discusses Camel, an interactive parallel programming environment based on cellular automata. With Camel users can develop high-performance applications in science and engineering. Examples in geology, traffic planning, image processing, and genetic algorithms show its usefulness.

A parallel cellular automata environment on multicomputers for computational science

This paper describes CAMEL (Cellular Automata environMent for systEms modeLing), a scalable software environment based on the cellular automata theory implemented on a Transputer-based parallel computer. Cellular automata were originally defined as a theory to model the basic mechanisms of dynamic systems, permitting a new approach which is in many cases simpler and more efficient than the traditional approach based on partial differential equations. Today, cellular automata become more attractive because they are suitable to be effectively and naturally implemented on parallel computers achieving high performance. CAMEL allows a user to program computational science applications exploiting the computing power offered by highly parallel computers in a transparent way. CAMEL implements a cellular automaton as a SPMD program. A load balancing strategy is used to minimize time costs in case of not uniform intervals for transition steps. In the paper the programming environment and the parallel architecture of CAMEL are presented and some experiments are discussed.

High performance scientific computing by a parallel cellular environment

Abstract This paper describes CAMEL, a parallel environment for designing scientific applications based on the cellular automata mathematical model. CAMEL is an interactive environment designed to support the development of high performance applications in science and engineering. It offers the computing power of a highly parallel computer, hiding the architecture issues from a user. The system can be used both as a tool to model dynamic complex phenomena and as a computational model for parallel processing. By CAMEL a user might write programs to describe the actions of thousands of simple active agents, then observe the global complex evolution that arises from all the local interactions. The paper presents the programming environment and a significant application in the area of soil decontamination.

Performance Evaluation and Modeling of MPI Communications on the Meiko CS-2

This paper presents, evaluates and compares the performance of the point-to-point and broadcast communication primitives of the MPI-1 standard library on the Meiko CS-2 parallel machine. Furthermore, a benchmark model of MPI communications is proposed. It is based on the size of messages exchanged and the number of processors involved.

A Parallel Cellular Environment for High Performance Scientific Computing

In this paper we describe CAMEL, an interactive parallel environment based on the cellular automata model. CAMEL is an environment designed to support the development of high performance applications in science and engineering. It offers the computing power of a highly parallel computer, hiding the architecture issues from a user. The system can be used both as a tool to model dynamic complex phenomena and as a computational model for parallel processing. By CAMEL a user might write programs to describe the actions of thousands of simple active agents, then observe the global complex evolution that arise from all the local interactions. The paper presents the programming environment and a significant application in the area of soil decontamination.

Advances in Artificial Life and Evolutionary Computation

In this paper a fault detection analysis through a neural networks ensembling approach and statistical pattern recognition techniques is presented. Abnormal consumption or faults are detected by analyzing the residual values, which are the difference between the expected and the real operating data. The residuals are more sensitive to faults and insensitive to noise. In this study, first, the experimentation is carried out over two months monitoring data set for the lighting energy consumption of an actual office building. Using a fault free data set for the training, an artificial neural networks ensemble (ANNE) is used for the estimation of hourly lighting energy consumption in normal operational conditions. The fault detection is performed through the analysis of the magnitude of residuals using peak outliers detection method. Second, the fault detection analysis is also carried out through statistical pattern recognition techniques on structured residuals of lighting power consumption considering different influencing attributes i.e. number of people, global solar radiation etc. Moreover the results obtained from these methods are compared to minimize the false anomalies and to improve the FDD process. Experimental results show the effectiveness of the ensembling approach in automatic detection of abnormal building lighting energy consumption. The results also indicate that statistical pattern recognition techniques applied to residuals are useful for detecting and isolating the faults as well as noise.

Evaluating and Modeling Communication Overhead of MPI Primitives on the Meiko CS-2

The MPI (Message Passing Interface) is a standard communication library implemented on a large number of parallel computers. It is used for the development of portable parallel software. This paper presents, evaluates and compares the performance of the point-to-point and broadcast communication primitives of the MPI standard library on the Meiko CS-2 parallel machine. Furthermore, the paper proposes a benchmark model of MPI communications based on the size of messages exchanged and the number of involved processors. Finally, the MPI performance results on the CS-2 are compared with the performance of the Meiko Elan Widget library and the IBM SP2.

A parallel cellular simulator for bioremediation of contaminated soils

The bioremediation of contaminated soils is one of main strategies for site clean-up. The most important principle of bioremediation is that microorganisms (mainly bacteria) can be used to destroy hazardous contaminants or transform them into a less harmful form. Currently, we are facing this problem in the CABOTO project within the PCI ESPRIT framework. The CABOTO objective concerns the design and implementation of a parallel simulator for the bioremediation of contaminated soils by using models based on the cellular automata (CA) theory. For the parallel implementing of the simulator has been used the CAMEL system, a parallel environment for the simulation and modelling of complex systems based on CA. This paper describes the model used to simulate the contamination and the bioremediation of the soil, the main features of the CAMEL system and the parallel implementation of the simulator by CAMEL. Finally, experimental results are described.

Scalable classification of large data sets by parallel genetic programming

A parallel genetic programming approach to data classification is presented. The method uses cellular automata as a framework to enable a fine-grained parallel implementation of GP through the grid model. Experiments on real datasets from the UCI machine learning repository show good results with respect to C4.5. The generated trees are smaller, they have a misclassification error on the training set comparable, but, more important, they generalise better than C4.5. Furthermore, performance results show a nearly linear speedup.

A High-Level Language for ProgrammingCellular Algorithms on Parallel Machines

This paper describes CARPET, a parallel programming language based on the cellular automata model. A CARPET implementation has been used for programming cellular algorithms in the CAMEL parallel environment. CAMEL is an environment designed to support the development of high performance applications in science and engineering. It offers the computing power of a highly parallel computer, hiding the architecture issues from a user. By CARPET a user might write programs to describe the actions of thousands of simple active agents interacting locally, then the CAMEL system allows a user to observe the global complex evolution that arises from all the local interactions.