PhD Satish Narayana Srirama (University of Tartu, Estonia)
PhD Paolo Trunfio (University of Calabria, Italy)
PhD Vladimir Vlassov (KTH Royal Institute of Technology in Stockholm, Sweden)
Scientific computing uses computers and algorithms to solve problems in various sciences such as genetics, biology and chemistry. Often the goal is to model and simulate different natural phenomena which would otherwise be very difficult to study in real environments. For example, it is possible to create a model of a solar storm or a meteor hit and run computer simulations to assess the impact of the disaster on the environment. The more sophisticated and accurate the simulations are the more computing power is required. It is often necessary to use a large number of computers, all working simultaneously on a single problem. These kind of computations are called parallel or distributed computing. However, creating distributed computing programs is complicated and requires a lot more time and resources, because it is necessary to synchronize different computers working at the same time. A number of software frameworks have been created to simplify this process by automating part of a distributed programming. The goal of this research was to assess the suitability of such distributed computing frameworks for complex scientific computing algorithms. The results showed that existing frameworks are very different from each other and none of them are suitable for all different types of algorithms. Some frameworks are only suitable for simple algorithms; others are not suitable when data does not fit into the computer memory. Choosing the most appropriate distributed computing framework for an algorithm can be a very complex task, because it requires studying and applying the existing frameworks. While searching for a solution to this problem, it was decided to create a Dynamic Algorithms Modelling Application (DAMA), which is able to simulate the implementation of the algorithm in different distributed computing frameworks. DAMA helps to estimate which distributed framework is the most appropriate for a given algorithm, without actually implementing it in any of the available frameworks. This main contribution of this study is simplifying the adoption of distributed computing frameworks for researchers who are not yet familiar with using them. It should save significant time and resources as it is not necessary to study each of the available distributed computing frameworks in detail.