Estima

Abstract

This article presents estima, an easy-to-use tool for extrapolating the scalability of in-memory applications. estima is designed to perform a simple yet important task: Given the performance of an application on a small machine with a handful of cores, estima extrapolates its scalability to a larger machine with more cores, while requiring minimum input from the user. The key idea underlying estima is the use of stalled cycles (e.g., cycles that the processor spends waiting for missed cache line fetches or busy locks). estima measures stalled cycles on a few cores and extrapolates them to more cores, estimating the amount of waiting in the system. estima can be effectively used to predict the scalability of in-memory applications for bigger execution machines. For instance, using measurements of memcached and SQLite on a desktop machine, we obtain accurate predictions of their scalability on a server. Our extensive evaluation shows the effectiveness of estima on a large number of in-memory benchmarks. INTRODUCTIONCommodity machines nowadays have hundreds of gigabytes of memory. This enables building performance-critical parallel applications, such as databases and key-value stores, that keep their datasets in main memory. This way, applications avoid slow secondary storage and networks, leaving the CPU as the main performance bottleneck [9,12,26,30]. Understanding the performance of these applications proves to be hard, since the number of CPU cores available during the deployment of a parallel application can be significantly higher than that during its development and testing. Applications developed today can be tested on machines with 16 or 24 cores, but in a few years the same applications are likely to be run on machines with 64 or even more cores.