# A Survey of Phase Classification Techniques for Characterizing Variable Application Behavior

## TL;DR

This survey reviews recent phase classification techniques crucial for enabling adaptable computing through phase-based optimization, highlighting their characteristics, differences, and future research directions.

## Contribution

It provides a comprehensive classification and comparison of recent phase classification techniques, identifying gaps and suggesting future research avenues.

## Key findings

- Techniques are divided into online/offline and serial/parallel categories.
- Discussion of prediction and detection methods used in phase classification.
- Identification of gaps and future directions in phase classification research.

## Abstract

Adaptable computing is an increasingly important paradigm that specializes system resources to variable application requirements, environmental conditions, or user requirements. Adapting computing resources to variable application requirements (or application phases) is otherwise known as phase-based optimization. Phase-based optimization takes advantage of application phases, or execution intervals of an application, that behave similarly, to enable effective and beneficial adaptability. In order for phase-based optimization to be effective, the phases must first be classified to determine when application phases begin and end, and ensure that system resources are accurately specialized. In this paper, we present a survey of phase classification techniques that have been proposed to exploit the advantages of adaptable computing through phase-based optimization. We focus on recent techniques and classify these techniques with respect to several factors in order to highlight their similarities and differences. We divide the techniques by their major defining characteristics---online/offline and serial/parallel. In addition, we discuss other characteristics such as prediction and detection techniques, the characteristics used for prediction, interval type, etc. We also identify gaps in the state-of-the-art and discuss future research directions to enable and fully exploit the benefits of adaptable computing.