Performance Analysis of Irregular Task-Based Applications on Hybrid Platforms: Structure Matters

International audienceEfficiently exploiting computational resources in heterogeneous platforms is a real challenge which has motivated the adoption of the task-based programming paradigm where resource usage is dynamic and adaptive. Unfortunately, classical performance visualization techniques used in routine performance analysis often fail to provide any insight in this new context, especially when the application structure is irregular. In this paper, we propose several performance visualization techniques and modeling strategies motivated by the analysis of task-based multifrontal sparse linear solvers whose structure is particularly complex. We show that by building on both a performance model of irregular tasks and on structure of the application (in particular the elimination tree), we can detect and highlight anomalies and understand resource utilization from the application point-of-view in a very insightful way. We validate these novel performance analysis techniques with the QR_mumps sparse parallel solver by describing a series of case studies where we identify and address non trivial performance issues thanks to our visualization methodology

Educação Musical Auxiliada por Computador: Algumas Considerações e Experiências

O objetivo deste trabalho é tecer considerações relativas a educação musical auxiliada por computador, salientar tópicos que consideramos necessários para este tipo de prática e algumas características de sistemas que os contemplam. Discute, ainda, como explorar a computação musical como meio de inclusão digital, citando experiências de pesquisas na área

StarVZ: Performance Analysis of Task-Based Parallel Applications

High-performance computing (HPC) applications enable the solution of compute-intensive problems in feasible time. Among many HPC paradigms, task-based programming has gathered community attention in recent years. This paradigm enables constructing an HPC application using a more declarative approach, structuring it in a direct acyclic graph (DAG). The performance evaluation of these applications is as hard as in any other programming paradigm. Understanding how to analyze these applications, employing the DAG and runtime metrics, presents opportunities to improve its performance. This article describes the StarVZ R-package available on CRAN for performance analysis of task-based applications. StarVZ enables transforms runtime trace data into different vi-sualizations of the application behavior. An analyst can understand their applications' performance limitations and compare multiple executions. StarVZ has been successfully applied to several study-cases, showing its applicability in a number of scenarios

Combinando modelos de predição e técnicas de visualização para melhorar a análise de desempenho de aplicações baseadas em tarefas irregulares

Parallel application performance analysis is an essential and a continuous step towards understanding and optimizing any high-performance program. Nowadays, ubiquitous and complex heterogeneous architectures turn this job even more burdensome. While paradigms like task-based ease programming through its abstractions and its runtime system, the analysis of such applications demand attention because of its specific view of the applications. Likewise, the analysis of irregular applications built upon specific data structures need to consider its abstractions and behavior to improve and facilitate an analyst’s work. Thus, the current work proposes strategies to enhance the performance analysis of irregular task-based applications and propose application-centric visualization panels to represent performance according to the elimination tree structure, the foundation of many direct sparse factorization methods. The strategies rely on tracing information for collecting task performance data. Since task-based applications can create many tasks and huge trace files, the proposed automatic mechanism for anomalous task classification based on regression models allows highlighting specific groups of problematic tasks and guiding the analysis process. The visualization techniques represent the tree structure and describe application-specific concepts like tree and node parallelism, child and parent dependencies, and communications. Those strategies are applied to the qr_mumps sparse task-based solver in an extensive set of experiments. The anomalous detection mechanism exposed four different task anomaly sources, guiding a solution that improved performance by up to 24% by reducing task interference. The elimination tree visualization panels allowed detailed comparisons between different application and runtime configurations, revealing other sources of inefficiency. The experiments also involved testing the qr_mumps application in a real computational simulation application, where it presented better performance than other parallel solvers. The results demonstrate the usefulness of the proposed strategies to guide the performance analysis of irregular task-based applications and enhance the performance representation of elimination-tree based applications.A análise de desempenho de aplicações paralelas trata-se de uma etapa essencial e contínua para entender e otimizar aplicações de alto desempenho. Arquiteturas heterogêneas hoje estão onipresentes e tornam esse trabalho ainda mais oneroso. Enquanto paradigmas como a programação baseada em tarefas facilitam o desenvolvimento por meio de abstrações e o sistema de runtime, sua análise exige mais atenção devido a sua visão específica da aplicação. Da mesma forma, análises de aplicações irregulares e construídas sobre estruturas de dados específicas precisam considerar tais características para facilitar o trabalho de analistas. Assim, este trabalho propõe estratégias para aprimorar a análise de desempenho de aplicações baseadas em tarefas irregulares usando painéis de visualização específicos, representando o desempenho de acordo com a estrutura da árvore de eliminação, alicerce de muitos métodos de fatoração esparsa direta. As estratégias utilizam informações de rastreamento para coletar dados de desempenho de tarefas. Como aplicações baseadas em tarefas podem gerar grandes arquivos de rastreamento, é proposto um mecanismo para classificação de tarefas anômalas com base em modelos de regressão que permite destacar tarefas problemáticas automaticamente, direcionando a análise. As técnicas de visualização representam a estrutura da árvore e comportamentos específicos da aplicação, como o paralelismo da árvore e dos nós, dependências entre nós filhos e pais, e comunicações. Essas estratégias são aplicadas ao solver esparso baseado em tarefas qr_mumps em um conjunto de experimentos. Os modelos de regressão expuseram quatro fontes de anomalias, guiando uma solução que melhorou o desempenho em até 24% ao reduzir a interferência entre tarefas. Os painéis de visualização da árvore de eliminação permitiram comparações detalhadas entre diferentes configurações da aplicação e runtime, revelando outras fontes de ineficiência. Também usamos o qr_mumps em uma aplicação de simulação computacional, onde ele apresentou melhor desempenho do que outros solvers paralelos. O estudo demonstrou a utilidade das técnicas propostas para guiar a análise de desempenho de aplicações baseadas em tarefas irregulares e melhorar a representação do desempenho de aplicações construídas sobre árvores de eliminação

Performance Analysis of Irregular Task-Based Applications on Hybrid Platforms: Structure Matters

International audienceEfficiently exploiting computational resources in heterogeneous platforms is a real challenge which has motivated the adoption of the task-based programming paradigm where resource usage is dynamic and adaptive. Unfortunately, classical performance visualization techniques used in routine performance analysis often fail to provide any insight in this new context, especially when the application structure is irregular. In this paper, we propose several performance visualization techniques and modeling strategies motivated by the analysis of task-based multifrontal sparse linear solvers whose structure is particularly complex. We show that by building on both a performance model of irregular tasks and on structure of the application (in particular the elimination tree), we can detect and highlight anomalies and understand resource utilization from the application point-of-view in a very insightful way. We validate these novel performance analysis techniques with the QR_mumps sparse parallel solver by describing a series of case studies where we identify and address non trivial performance issues thanks to our visualization methodology