Application Speedup Characterization: Modeling Parallelization Overhead and Variations of Problem Size and Number of Cores.

To make efficient use of multi-core processors, it is important to understand the performance behavior of parallel applications. Modeling this can enable the use of online approaches to optimize throughput or energy, or even guarantee a minimum QoS. Accurate models would avoid probe different runtime configurations, which causes overhead. Throughout the years, many speedup models were proposed. Most of them based on Amdahl's or Gustafson's laws. However, many of those make considerations such as a fixed parallel fraction, or a parallel fraction that varies linearly with problem size, and inexistent parallelization overhead. Although such models aid in the theoretical understanding, these considerations do not hold in real environments, which makes the modeling unsuitable for accurate characterization of parallel applications. The model proposed estimates the speedup taking into account the variation of its parallel fraction according to problem size, number of cores used and overhead. Using four applications from the PARSEC benchmark suite, the proposed model was able to estimate speedups more accurately than other models in recent literature.