TRACTABLE DATA-FLOW ANALYSIS FOR DISTRIBUTED SYSTEMS

Automated behavior analysis is a valuable technique in the development and maintainence of distributed systems. In this paper, we present a tractable dataflow analysis technique for the detection of unreachable states and actions in distributed systems. The technique follows an approximate approach described by Reif and Smolka, but delivers a more accurate result in assessing unreachable states and actions. The higher accuracy is achieved by the use of two concepts: action dependency and history sets. Although the technique, does not exhaustively detect all possible errors, it detects nontrivial errors with a worst-case complexity quadratic to the system size. It can be automated and applied to systems with arbitrary loops and nondeterministic structures. The technique thus provides practical and tractable behavior analysis for preliminary designs of distributed systems. This makes it an ideal candidate for an interactive checker in software development tools. The technique is illustrated with case studies of a pump control system and an erroneous distributed program. Results from a prototype implementation are presented

A Framework for Agile Development of Component-Based Applications

Agile development processes and component-based software architectures are two software engineering approaches that contribute to enable the rapid building and evolution of applications. Nevertheless, few approaches have proposed a framework to combine agile and component-based development, allowing an application to be tested throughout the entire development cycle. To address this problematic, we have built CALICO, a model-based framework that allows applications to be safely developed in an iterative and incremental manner. The CALICO approach relies on the synchronization of a model view, which specifies the application properties, and a runtime view, which contains the application in its execution context. Tests on the application specifications that require values only known at runtime, are automatically integrated by CALICO into the running application, and the captured needed values are reified at execution time to resume the tests and inform the architect of potential problems. Any modification at the model level that does not introduce new errors is automatically propagated to the running system, allowing the safe evolution of the application. In this paper, we illustrate the CALICO development process with a concrete example and provide information on the current implementation of our framework

LEGaTO: first steps towards energy-efficient toolset for heterogeneous computing

LEGaTO is a three-year EU H2020 project which started in December 2017. The LEGaTO project will leverage task-based programming models to provide a software ecosystem for Made-in-Europe heterogeneous hardware composed of CPUs, GPUs, FPGAs and dataflow engines. The aim is to attain one order of magnitude energy savings from the edge to the converged cloud/HPC.Peer ReviewedPostprint (author's final draft

A runtime heuristic to selectively replicate tasks for application-specific reliability targets

In this paper we propose a runtime-based selective task replication technique for task-parallel high performance computing applications. Our selective task replication technique is automatic and does not require modification/recompilation of OS, compiler or application code. Our heuristic, we call App_FIT, selects tasks to replicate such that the specified reliability target for an application is achieved. In our experimental evaluation, we show that App FIT selective replication heuristic is low-overhead and highly scalable. In addition, results indicate that complete task replication is overkill for achieving reliability targets. We show that with App FIT, we can tolerate pessimistic exascale error rates with only 53% of the tasks being replicated.This work was supported by FI-DGR 2013 scholarship and the European Community’s Seventh Framework Programme [FP7/2007-2013] under the Mont-blanc 2 Project (www.montblanc-project.eu), grant agreement no. 610402 and in part by the European Union (FEDER funds) under contract TIN2015-65316-P.Peer ReviewedPostprint (author's final draft