Mitos: A Simple Interface for Complex Hardware Sampling and Attribution
Authors: Alfredo Gimenez (University of California, Davis), Benafsh Husain (Lawrence Livermore National Laboratory), David Boehme (Lawrence Livermore National Laboratory), Todd Gamblin (Lawrence Livermore National Laboratory), Martin Schulz (Lawrence Livermore National Laboratory)
Abstract: As high-performance architectures become more intricate and complex, so too do the capabilities to monitor their performance. In particular, hardware sampling mechanisms have extended beyond the traditional scope of code profiling to include performance data relevant to the data address space, hardware topology, and load latency. However, these new mechanisms do not adhere to a common specification and as such, require architecture-specific knowledge and setup to properly function. In addition, the incoming data is often low-level and difficult to attribute to any useful context without a high level of expertise. This has resulted in a destructively steep barrier to entry, both in data acquisition and analysis.
Mitos provides a simple interface for modern hardware sampling mechanisms and the ability to define attributions of low-level samples to intuitive contexts. We present the Mitos API and demonstrate how to use it to create detailed yet understandable visualizations of performance data for analysis.
Two-page extended abstract: pdf