Simone Sbaraglia

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In this paper we present SIGMA (Simulation Infrastructure to Guide Memory Analysis), a new data collection framework and family of cache analysis tools. The SIGMA environment provides detailed cache information by gathering memory reference data using software-based instrumentation. This infrastructure can facilitate quick probing into the factors that(More)
Detailed cache simulation can be useful to both system developers and application writers to understand an applica-tion's performance. However, measuring long running programs can be extremely slow. In this paper we present a technique to use dynamic sampling of trace snippets throughout an application's execution. We demonstrate that our approach improves(More)
In this paper we describe pSigma, which is an infrastructure for instrumenting parallel applications, enabling the users to probe into the execution of an application by intercepting its control-flow at selected points, the selection being made by lucid specifications using symbolic names of data structures and functions in the source program and(More)
In this paper, we present the architecture design and implementation of a framework for automated performance bottleneck detection. The framework analyzes the time-spent distribution in the application and discovers the performance bottlenecks by using given bottleneck definitions. The user can query the application execution performance to identify(More)
Locality behavior study is crucial for achieving good performance for irregular problems. Graph algorithms with large, sparse inputs, for example, of-tentimes achieve only a tiny fraction of the potential peak performance on current architectures. Compared with most numerical algorithms graph algorithms lay higher pressure on the memory system. In this(More)
This paper describes an overview of environment for memory performance studies (EMPS). EMPS is a framework to allow different data gathering and simulation tools to be composed together to predict the performance of parallel programs on a variety of current and future high end computing (HEC) systems. The framework seeks to combine the automated nature of(More)
In response to the productivity challenge of the U.S. DARPA HPCS initiative, we have developed a methodology that provides an extremely simple and pain-free interface through which scientists can collect rich performance data from selected parts of an execution, digest the data at a very high level, and plan for improvements. This process can be easily(More)
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