Understanding Parallel Performance Under Interferences in Multi-tenant Clouds

Abstract

The performance of parallel programs is notoriously difficult to reason in virtualized environments. Although performance degradations caused by virtualization and interferences have been well studied, there is little understanding why different parallel programs have unpredictable slow- downs. We find that unpredictable performance is the result of complex interplays between the design of the program, the memory hierarchy of the hosting system, and the CPU scheduling in the hypervisor. We develop a profiling tool, vProfile, to decompose parallel runtime into three parts: compute, steal and synchronization. With the help of time breakdown, we devise two optimizations at the hypervisor to reduce slowdowns.