An Expanded Speedup Model for the Early Phases of High Performance Computing Cluster (HPCC) Design

Abstract

The size and complexity of many scientific and enterprise-level applications require a high degree of parallelization in order to produce outputs within an acceptable period of time. This often necessitates the uses of high performance computing clusters (HPCCs) and parallelized applications which are carefully designed and optimized. A myriad of papers study the various factors which influence performance and then attempt to quantify the maximum theoretical speedup that can be achieved by a cluster relative to a sequential processor.

The studies tend to only investigate the influences in isolation, but in practice these factors tend to be interdependent. It is the interaction rather than any solitary influence which normally creates the bounds of the design trade space. In the attempt to address this disconnect, this thesis blends the studies into an expanded speedup model which captures the interplay. The model is intended to help the cluster engineer make initial estimates during the early phases of design while the system is not mature enough for refinement using timing studies.

The model pulls together factors such as problem scaling, resource allocation, critical sections, and the problem's inherent parallelizability. The derivation was examined theoretically and then validated by timing studies on a physical HPCC. The validation studies found that the model was an adequate generic first approximation. However, it was also found that customizations may be needed in order to account for application-specific influences such as bandwidth limitations and communication delays which are not readily incorporated into a generic model.