International audienceThe growing complexity of embedded systems makes their behavior analysis a challenging task. In this context, tracing appears to be a promising solution as it provides relevant information about the system execution. However, trace management and analysis are hindered by the diversity of trace formats, the incompatibility of trace analysis methods, the problem of trace size and its storage as well as by the lack of visualization scalability. In this article we present FrameSoC, a new trace management infrastructure that solves these issues. It proposes generic solutions for trace storage and defines interfaces and plug in mechanisms for integrating diverse analysis tools. We illustrate the benefit of FrameSoC with a case study of a visualization module that provides representation scalability for large traces by using an aggregation algorithm

Abstract-This paper presents performance evaluation and analysis of well-known HPC applications and benchmarks running on low-power embedded platforms. The performance to power consumption ratios are compared to classical x86 systems. Scalability studies have been conducted on the Mont-Blanc Tibidabo cluster. We have also investigated optimization opportunities and pitfalls induced by the use of these new platforms, and proposed optimization strategies based on auto-tuning.

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The growing complexity of computer system hardware and software makes their behavior analysis a challenging task. In this context, tracing appears to be a promising solution as it provides relevant information about the system execution. However, trace analysis techniques and tools lack in providing the analyst the way to perform an efficient analysis flow because of several issues. First, traces contain a huge volume of data difficult to store, load in memory and work with. Then, the analysis flow is hindered by various result formats, provided by different analysis techniques, often incompatible. Last, analysis frameworks lack an entry point to understand the traced application general behavior. Indeed, traditional visualization techniques suffer from time and space scalability issues due to screen size, and are not able to represent the full trace. In this article, we present how to do an efficient analysis by using the Shneiderman's mantra: "Overview first, zoom and filter, then details on demand". Our methodology is based on FrameSoC, a trace management infrastructure that provides solutions for trace storage, data access, and analysis flow, managing analysis results and tool. Ocelotl, a visualization tool, takes advantage of FrameSoC and shows a synthetic representation of a trace by using a time aggregation. This visualization solves scalability issues and provides an entry point for the analysis by showing phases and behavior disruptions, with the objective of getting more details by focusing on the interesting trace parts.