Programming distributed and adaptable autonomous components--the GCM/ProActive framework

International audienceComponent-oriented software has become a useful tool to build larger and more complex systems by describing the application in terms of encapsulated, loosely coupled entities called components. At the same time, asynchronous programming patterns allow for the development of efficient distributed applications. While several component models and frameworks have been proposed, most of them tightly integrate the component model with the middleware they run upon. This intertwining is generally implicit and not discussed, leading to entangled, hard to maintain code. This article describes our efforts in the development of the GCM/ProActive framework for providing distributed and adaptable autonomous components. GCM/ProActive integrates a component model designed for execution on large-scale environments, with a programming model based on active objects allowing a high degree of distribution and concurrency. This new integrated model provides a more powerful development, composition, and execution environment than other distributed component frameworks. We illustrate that GCM/ProActive is particularly adapted to the programming of autonomic component systems, and to the integration into a service-oriented environment

Performance Observability and Monitoring of High Performance Computing with Microservices

Traditionally, High Performance Computing (HPC) softwarehas been built and deployed as bulk-synchronous, parallel executables based on the message-passing interface (MPI) programming model. The rise of data-oriented computing paradigms and an explosion in the variety of applications that need to be supported on HPC platforms have forced a re-think of the appropriate programming and execution models to integrate this new functionality. In situ workflows demarcate a paradigm shift in HPC software development methodologies enabling a range of new applications --- from user-level data services to machine learning (ML) workflows that run alongside traditional scientific simulations. By tracing the evolution of HPC software developmentover the past 30 years, this dissertation identifies the key elements and trends responsible for the emergence of coupled, distributed, in situ workflows. This dissertation's focus is on coupled in situ workflows involving composable, high-performance microservices. After outlining the motivation to enable performance observability of these services and why existing HPC performance tools and techniques can not be applied in this context, this dissertation proposes a solution wherein a set of techniques gathers, analyzes, and orients performance data from different sources to generate observability. By leveraging microservice components initially designed to build high performance data services, this dissertation demonstrates their broader applicability for building and deploying performance monitoring and visualization as services within an in situ workflow. The results from this dissertation suggest that: (1) integration of performance data from different sources is vital to understanding the performance of service components, (2) the in situ (online) analysis of this performance data is needed to enable the adaptivity of distributed components and manage monitoring data volume, (3) statistical modeling combined with performance observations can help generate better service configurations, and (4) services are a promising architecture choice for deploying in situ performance monitoring and visualization functionality. This dissertation includes previously published and co-authored material and unpublished co-authored material