Parallel techniques in irregular codes: cloth simulation as case of study

When parallelizing irregular applications on ccNUMA machines several issues should be taken into account in order to achieve high code performance. These factors include locality exploitation and parallelism, as well as careful use of memory resources (memory overhead). An important number of numerical simulation codes are clear examples of irregular applications. Frequently these kinds of codes include reduction operations in their core, so that an important fraction of the computational time is spent on such operations. Specifically, cloth simulation belongs to this class of applications, being a topic of increasing interest in diverse areas, like in the multimedia industry. Moreover, when real time simulation is the aim, its parallelization becomes an important option. This paper discusses and compares different irregular reduction parallelization techniques on ccNUMA share memory machines. Broadly speaking, we may classify them into two groups: privatization-based and data partitioning-based methods. In this paper we describe a framework, based on data affinity, that permits to develop various algorithms inside the group of the data partitioning-based techniques. All these techniques and approaches are analyzed and adapted to the computational structure of a real, physically based, cloth simulator.