RECOMENDAÇÕES PARA TRABALHOS FUTUROS

Como resultado deste trabalho, nós acreditamos que as seguintes modificações merecem uma investigação mais profunda e podem vir a contribuir bastante com este trabalho e com a área:

1. Reduzir o conjunto de trabalho, principalmente utilizando alocação dinâmica. Atualmente o conjunto de trabalho é todo previamente alocado e, a depender do número de agentes e nós, pode se tornar muito grande.

2. Investigar a transferência de dados via Peer-to-Peer entre GPUs, presente na versão mais nova de CUDA. Deve ser levada em

consideração a replicação do mapa para cada GPU bem como o escalonamento do trabalho entre elas.

3. Investigar a possibilidade de abordagens multiagente, em que cada agente pode reusar o caminho previamente calculado por outro agente. Esse tipo de abordagem diminuiria o custo com o cálculo de caminhos que já tenham sido computados.

4. Investigar se existe a possibilidade de utilizar outra abordagem de paralelização. A mais utilizada atualmente está relacionada com o mapeamento de 1 agente por thread.

5. Utilizar-se de ferramentas que permitam a visualização dos caminhos calculados para cada agente, assim como a simulação da navegação em tempo real.

6. O principal trabalho futuro está no desenvolvimento de um benchmark para testes. Com isso, seria possível padronizar os testes, utilizando os mesmos parâmetros e recursos (número de agentes, tamanho do mapa, placa de vídeo utilizada, etc.), com diferentes algoritmos.

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