Simulation of parallel similarity measure computations for large data sets

The paper presents our approach to implementation of similarity measure for big data analysis in a parallel environment. We describe the algorithm for parallelisation of the computations. We provide results from a real MPI application for computations of similarity measures as well as results achieved with our simulation software. The simulation environment allows us to model parallel systems of various sizes with various components such as CPUs, GPUs, network interconnects, and model parallel applications in a meta language. The simulations allow us to determine in details how computations will be performed on a particular hardware. They also allow to predict the shapes of time curves beyond the area where empirical results can be obtained due to limited computational resources such as memory capacity.