Simulação de reservatórios de petróleo em ambiente MPSoC

The constant increase of complexity in computer applications demands the development of more powerful hardware support for them. With processor's operational frequency reaching its limit, the most viable solution is the use of parallelism. Based on parallelism techniques and the progressive growth in the capacity of transistors integration in a single chip is the concept of MPSoCs (Multi-Processor System-on-Chip). MPSoCs will eventually become a cheaper and faster alternative to supercomputers and clusters, and applications developed for these high performance systems will migrate to computers equipped with MP-SoCs containing dozens to hundreds of computation cores. In particular, applications in the area of oil and natural gas exploration are also characterized by the high processing capacity required and would benefit greatly from these high performance systems. This work intends to evaluate a traditional and complex application of the oil and gas industry known as reservoir simulation, developing a solution with integrated computational systems in a single chip, with hundreds of functional unities. For this, as the STORM (MPSoC Directory-Based Platform) platform already has a shared memory model, a new distributed memory model were developed. Also a message passing library has been developed folowing MPI standard

Um protocolo de comunicação multicast na camada de aplicação com Consciência de Localização

Internet applications such as media streaming, collaborative computing and massive multiplayer are on the rise,. This leads to the need for multicast communication, but unfortunately group communications support based on IP multicast has not been widely adopted due to a combination of technical and non-technical problems. Therefore, a number of different application-layer multicast schemes have been proposed in recent literature to overcome the drawbacks. In addition, these applications often behave as both providers and clients of services, being called peer-topeer applications, and where participants come and go very dynamically. Thus, servercentric architectures for membership management have well-known problems related to scalability and fault-tolerance, and even peer-to-peer traditional solutions need to have some mechanism that takes into account member's volatility. The idea of location awareness distributes the participants in the overlay network according to their proximity in the underlying network allowing a better performance. Given this context, this thesis proposes an application layer multicast protocol, called LAALM, which takes into account the actual network topology in the assembly process of the overlay network. The membership algorithm uses a new metric, IPXY, to provide location awareness through the processing of local information, and it was implemented using a distributed shared and bi-directional tree. The algorithm also has a sub-optimal heuristic to minimize the cost of membership process. The protocol has been evaluated in two ways. First, through an own simulator developed in this work, where we evaluated the quality of distribution tree by metrics such as outdegree and path length. Second, reallife scenarios were built in the ns-3 network simulator where we evaluated the network protocol performance by metrics such as stress, stretch, time to first packet and reconfiguration group time

Análise de desempenho da rede neural artificial do tipo multilayer perceptron na era multicore

Artificial neural networks are usually applied to solve complex problems. In problems with more complexity, by increasing the number of layers and neurons, it is possible to achieve greater functional efficiency. Nevertheless, this leads to a greater computational effort. The response time is an important factor in the decision to use neural networks in some systems. Many argue that the computational cost is higher in the training period. However, this phase is held only once. Once the network trained, it is necessary to use the existing computational resources efficiently. In the multicore era, the problem boils down to efficient use of all available processing cores. However, it is necessary to consider the overhead of parallel computing. In this sense, this paper proposes a modular structure that proved to be more suitable for parallel implementations. It is proposed to parallelize the feedforward process of an RNA-type MLP, implemented with OpenMP on a shared memory computer architecture. The research consistes on testing and analizing execution times. Speedup, efficiency and parallel scalability are analyzed. In the proposed approach, by reducing the number of connections between remote neurons, the response time of the network decreases and, consequently, so does the total execution time. The time required for communication and synchronization is directly linked to the number of remote neurons in the network, and so it is necessary to investigate which one is the best distribution of remote connections

Implementação paralela escalável e eficiente do algoritmo simplex padrão em arquitetura multicore

This work presents a scalable and efficient parallel implementation of the Standard Simplex algorithm in the multicore architecture to solve large scale linear programming problems. We present a general scheme explaining how each step of the standard Simplex algorithm was parallelized, indicating some important points of the parallel implementation. Performance analysis were conducted by comparing the sequential time using the Simplex tableau and the Simplex of the CPLEXR IBM. The experiments were executed on a shared memory machine with 24 cores. The scalability analysis was performed with problems of different dimensions, finding evidence that our parallel standard Simplex algorithm has a better parallel efficiency for problems with more variables than constraints. In comparison with CPLEXR , the proposed parallel algorithm achieved a efficiency of up to 16 times better