To achieve the most optimal simulation performance, a parallel computing framework known as Computer Unified Device Architecture (CUDA), introduced by NVIDIA Corporation, was employed.
Furthermore, the ARWS focus on the individual, fitting to the decentralized computing framework, such as distributed computation and cloud computing.
Moreover, since there are massive independent approximation processes in the polynomial approximation-based approach, a distributed computing framework is designed to carry out the computation for this method.
Our reference architecture for the computing framework is an in-memory cluster computing framework like Apache Spark, on which we perform our experimental session.
Spark [6, 21] is a new computing framework that supports iterative and interactive applications.
We developed our program under Nvidia's CUDA [32] parallel computing framework.
Microsoft today announced that it is making a serious commitment to the open source Apache Spark cluster computing framework.
MapReduce is a scalable parallel computing framework for big data processing.
We present a scalable computing framework for the solution stochastic multiobjective optimization problems.
We further parallelize and optimize our algorithm on state-of-the-art Spark cluster computing framework.
This chapter describes the Open Grid Services Architecture (OGSA)—the standards-based Grid computing framework.
