The impact of memory hierarchies on cluster computing

Using of-the-shelf commodity workstations and PCs to build a cluster for parallel computing has become a common practice. A choice of a cost-effective cluster computing platform for a given budget and for certain types of application workloads is mainly determined by its memory hierarchy and interconnection network of the cluster. Finding such a solution from exhaustive simulations would be highly time-consuming and expensive; and predictions from measurements on existing clusters would be impractical. We present an analytical model for evaluating performance impact of memory hierarchies and networks on cluster computing. The model covers the memory hierarchy of a single SMP, a cluster of workstations/PCs, or a cluster of SMPs by changing various modeling and architectural parameters. Network variations covering bus and switch networks are also included in the analysis. Applications of different types are characterized by parameterized workloads with different computation and communication requirements. The model has been validated by simulations. Our study shows that the length of memory hierarchy is the most sensitive factor to affect the execution time for many types of workloads. However, the interconnection network cost of a tightly coupled system with a short length of memory hierarchy, such as an SMP is significantly more expensive than a normal cluster network connecting independent computer nodes. Thus, the essential issue to be considered is the trade-off between the length of memory hierarchy and system cost. Based on analytical and case studies, we present our quantitative recommendations for building cost-effective clusters for different application workloads.