Contratos formais para derivação e verificação de componentes paralelos

Abstract

The use of cloud computing to offer High Performance Computing (HPC) services has been widely discussed in the academia and industry. In this respect, this dissertation is included in the context of designing a cloud computing platform for the development of component-based parallel computing applications, referred as cloud of components. Many important challenges about using the cloud of components relate to parallel programming, an error-prone task due to synchronization issues, which may lead to abortion and production of incorrect data during execution of applications, and the inefficient use of computational resources. These problems may be very relevant in the case of long running applications with tight timelines to obtain critical results, quite common in the context of HPC. One possible solution to these problems is the formal analysis of the behavior of the components of an application through the cloud services, before their execution. Thus, the users of the components may know if a component can be safely used in their application. In this scenario, formal methods becomes useful. In this dissertation, it is proposed a process for specification and derivation of parallel components implementation for the cloud of components. This process involves the formal specification of the components behavior through contracts described using the Circus formal specification language. Then, through a refinement and translation process, which takes the contract as a start point, one may produce an implementation of a component that may execute on a parallel computing platform. Through this process, it becomes possible to offer guarantees to developers about the components behavior in their applications. To validate the proposed idea, the process is applied to contracts that have been described based on two benchmarks belonging to the NAS Parallel Benchmarks, widely adopted in HPC for evaluate the performance of parallel programming and computing platforms.