Abstract: Cube v.3 has been a powerful tool to examine Scalasca performance reports, but was basically unable to perform analyses on its own. With Cube v.4, we addressed several shortcomings of Cube v.3. We generalized the Cube data model, extended the list of supported data types, and allow operations with nontrivial algebras, e.g. for performance models or statistical data. Additionally, we introduced two major new features that greatly enhance the performance analysis features of Cube: Derived metrics and GUI plugins. Derived metrics can be used to create and manipulate metrics directly within the GUI, using a powerful domain-specific language called CubePL. Cube GUI plugins allow the development of novel performance analysis techniques based on Cube data without changing the source code of the Cube GUI.

Abstract: Event flow graphs used in the context of performance monitoring combine the scalability and low overhead of profiling methods with lossless information recording of tracing tools. In other words, they capture statistics on the performance behavior of parallel applications while preserving the temporal ordering of events. Event flow graphs require significantly less storage than regular event traces and can still be used to recover the full ordered sequence of events performed by the application. In this paper we explore the usage of event flow graphs in the context of visual performance analysis. We show that graphs can be used to quickly spot performance problems, helping to better understand the behavior of an application. We demonstrate our performance analysis approach with MiniFE, a mini-application that mimics the key performance aspects of finite-element applications in High Performance Computing (HPC).

Abstract: Application analysis is facilitated through a number of program profiling tools. The tools vary in their complexity, ease of deployment, design, and profiling detail. Specifically, understanding, analyzing, and optimizing is of particular importance for scientific applications where minor changes in code paths and data-structure layout can have profound effects. Understanding how intricate data-structures are accessed and how a given memory system responds is a complex task. In this paper we describe a trace profiling tool, Glprof, specifically aimed to lessen the burden of the programmer to pin-point heavily involved data-structures during an application's run-time, and understand data-structure run-time usage. Moreover, we showcase the tool's modularity using additional cache simulation components. We elaborate on the tool's design, and features. Finally we demonstrate the application of our tool in the context of Spec benchmarks using the Glprof profiler and two concurrently running cache simulators, PPC440 and AMD Interlagos.

Abstract: Analysing distributed virtual reality applications communicating through message-passing is challenging. Their development is complex, and knowing if something is wrong depends on the states of each process, defects (bugs) cause software crashes, hangs, and generation of incorrect results. To address this daunting problem we specify functional behavior models (for example, using synchronization barriers and shared variables) for these applications that ensures correctness. We also developed the GTracer tool, which compares the functional behavior models developed with the messages transmitted among processes. GTracer checks for violations of these models automatically and displays the message traffic graphically. It is a tool made for libGlass, a message library for distributed computing. We have been able to find several non-trivial defects during the tests of this tool.