Solutions to Parallel and Distributed Computing Problems: Lessons from Biological Sciences

1.1 INTRODUCTION

Many fundamental problems from natural sciences deal with complex systems. We define a complex system as a population of unique elements with well-defined microscopic attributes and interactions, showing emerging macroscopic behavior. This emergent behavior can, in general, not be predicted from the individual elements and their interactions. A typical example of emergent behavior is self-organization, e.g., Turing patterns in reaction–diffusion systems. Complex systems are often irreducible¹ and cannot be solved in an analytical way. The only available option to obtain more insight into these systems is through explicit simulation. Many of these problems are intractable: in order to obtain the required macroscopic information, extensive and computationally expensive simulation is necessary. Since simulation models of complex systems require an enormous computational effort, the only feasible way is to apply massively parallel computation. A major challenge is to apply high-performance computing in research on complex systems and, in addition, to offer a parallel computing environment that is easily accessible for applications [62, 63].

Traditionally, science has studied the properties of large systems composed of basic entities that obey simple microscopic equations reflecting the fundamental laws of nature. These natural systems can be studied by computer simulations in a variety of ways. Generally, the first step in any computer simulation is to develop some ...