A Survey of Phase Classification Techniques for Characterizing Variable Application Behavior

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.Comment: To appear in IEEE Transactions on Parallel and Distributed Systems (TPDS

RUN-TIME PROGRAM PHASE DETECTION AND PREDICTION

It is well-known that programs tend to have multiple phases in their execution. Because phases have impact on micro-architectural features such as caches and branch predictors, they are relevant to program performance (Xian et al., 2007; Roh et al., 2009; Gu and Verbrugge, 2008) and energy consumption. They are also relevant to detecting whether a program is executing as expected or is encountering unusual or exceptional conditions, a software engineering and program monitoring concern (Peleg and Mendelson, 2007; Singer and Kirkham, 2008; Pirzadeh et al., 2011; Benomar et al., 2014). We present methods for real-time phase change detection and phase prediction in Java, C, (etc.,) and Python programs. After applying a training protocol to a program of interest, our methods can detect and predict phase at run time for that program with good precision and recall (compared with a “ground truth” definition of phases) and with small performance impact. Furthermore, for concrete applications, we explore run-time energy-efficient clock frequency adjustment for statically compiled executables

Descobrindo o comportamento de fases através do agrupamento de características independentes de microarquitetura variantes no tempo

Orientador: Rodolfo Jardim de AzevedoDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: A análise de fases provou-se uma técnica eficiente para diminuir o tempo necessário para executar simulações detalhadas de microarquitetura. O objetivo deste estudo é solucionar duas dificuldades do estado da arte: (i) a maioria das abordagens feitas na análise de fases adota uma estratégia de granularidade fina, que em alguns casos pode ser interferida por ruídos temporários e não levar em conta um contexto mais amplo; e (ii) a interpretação da assinatura de cada fase de programa é uma tarefa difícil, dado que muitas vezes são empregadas assinaturas de alta dimensão. Para a problemática (i) adotamos a análise de fases de programas em dois níveis, cada qual com uma granularidade diferente (nível 1 -- método de agrupamento de subsequências de séries temporais multivariadas; nível 2 -- $k$-means). No entanto, concluímos que essa abordagem alcançou uma precisão comparável aos trabalhos anteriores. Chegamos então ao estado da arte de forma alternativa, mas com a vantagem de trazer subsídios para uma potencial solução para a problemática (ii), pois com o método empregado, as fases passaram a ter uma assinatura (MRF) muito mais interpretável, além de alinhada ao comportamento dos programas. Demonstramos a eficácia dessa interpretação usando uma medida de centralidade para identificar as principais características de uma fase de programa, contribuindo assim para o uso dessas assinaturas (MRF) de fases em estudos posterioresAbstract: Phase analysis has been shown to be an efficient technique to decrease the time needed to execute detailed micro-architectural simulations. Our study aimed to overcome two limitations of current methods that can be defined as follows: (i) most approaches adopt a fine-grained strategy, which in some cases can be interfered with temporary noises and do not account for a broader context; and (ii) interpreting the resulting program phases is often difficult since it is hard to draw meaningful conclusions from high-dimensional phase signatures. Regarding (i), we adopted a two-level phase analysis, each with different granularity (level 1 -- method of subsequence clustering of multivariate time series; level 2 -- $k$ -means). However, we found that, on average, this sampling approach achieved comparable accuracy in phase classification to prior work. Thus, we achieved state-of-the-art precision with a potential solution to the problem (ii), since with the method employed, the phases started to have a much more interpretable signature (MRF), in addition to be closely aligned with the behavior of a program. We demonstrated the effectiveness of such interpretation using a centrality measure to identify the most important characteristics within a program phaseMestradoCiência da ComputaçãoMestre em Ciência da Computação131024/2017-5CNP