Section: New Results
Interactive Analysis and Visualization of Large Distributed Systems
High performance applications are composed of many processes that are executed in large-scale systems with possibly millions of computing units. A possible way to conduct a performance analysis of such applications is to register in trace files the behavior of all processes belonging to the same application. The large number of processes and the very detailed behavior that we can record about them lead to a trace size explosion both in space and time dimensions. The performance visualization of such data is very challenging because of the quantities involved and the limited screen space available to draw them all. If the amount of data is not properly treated for visualization, the analysis may give the wrong idea about the behavior registered in the traces.
In  , we detail data aggregation techniques that are fully configurable by the user to control the level of details in both space and time dimensions. We also present two visualization techniques that take advantage of the aggregated data to scale. These features are part of the Viva and Triva open-source tools and framework.
The performance of parallel and distributed applications is also highly dependent on the characteristics of the execution environment. In such environments, the network topology and characteristics directly impact data locality and movements as well as contention, which are key phenomena to understand the behavior of such applications and possibly improve it. Unfortunately few visualizations available to the analyst are capable of accounting for such phenomena. In  , we propose an interactive topology-based visualization technique based on data aggregation that enables to correlate network characteristics, such as bandwidth and topology, with application performance traces. We claim that such kind of visualization enables to explore and understand non trivial behavior that are impossible to grasp with classical visualization techniques. We also claim that the combination of multi-scale aggregation and dynamic graph layout allows our visualization technique to scale seamlessly to large distributed systems. We support these claims through a detailed analysis of a high performance computing scenario and of a grid computing scenario.
Entropy Based Analysis
Although the previous approaches already improve upon state of the art and are useful on current scenarios, it is clear that at very large scale they would probably not be as effective, which led us to change perspective and to investigate how entropy can help building tractable macroscopic descriptions. Indeed, data aggregation can provide such abstractions by partitioning the systems dimensions into aggregated pieces of information. This process leads to information losses, so the partitions should be chosen with the greatest caution, but in an acceptable computational time. While the number of possible partitions grows exponentially with the size of the system, we propose in  an algorithm that exploits exogenous constraints regarding the system semantics to find best partitions in a linear or polynomial time. We detail two constrained sets of partitions that are respectively applied to temporal and spatial aggregation of an agent-based model of international relations. The algorithm succeeds in providing meaningful high-level abstractions for the system analysis.
Our approach is able to evaluate geographical abstractions used by the domain experts in order to provide efficient and meaningful macroscopic descriptions of the world global state  . We also successfully applied this technique to identify international media events by spatially and temporally aggregating RSS Flows of Newspapers  , in particular with the case of the Syrian civil war between May 2011 and December 2012  ,  .
We also applied this technique to the analysis of large distributed systems and combined it with the treemap visualization technique  ,  . These features have been integrated in the Viva and Triva open-source tools and framework.