Analyzing Simulation Model Profile Data to Assist Synthetic Model Generation Synthetic workloads are commonly used to exercise simulation tools for performance, performance tuning, and scalability studies. Sometimes these workloads are simple streams of test data following various distributions and in other cases these workloads are generated by more complex, configurable systems. An example of the former is a stream of input events at different arrival rates that might be used to test the performance of an event queue data structure. An example of the latter is the PHOLD simulation model that is often used to contrast the performance implications of different design solutions in a parallel simulation engine. One of the key challenges for synthetic workloads is the question of setting the parameters so that the workload properly reflects the behavior of actual workloads. This paper collects profile data from multiple realworld discreteevent simulation models in multiple configurations and sizes from the ROSS and WARPED2 repositories. A principle focus of this paper is the capture and reporting of profiling data to understand event granularities and event profile data to assist in the configuration of synthetic discrete event model generators.

Using SampleBased Time Series Data for Automated Diagnosis of Scalability Losses in Parallel Programs The performance of many parallel applications has failed to scale as fast as successive generations of hardware on which these applications execute. To understand the cause of scalability losses, experts use performance tools to monitor and analyze application behavior. Profiles generated by performance tools can usually indicate the presence of scalability losses while time series data are generally necessary to pinpoint the root causes of such losses. However, manual analysis of time series data can be difficult in executions with a large number of processes, long running times, and deep call chains. This paper describes an automated framework that analyzes samplebased time series data to diagnose scalability losses in parallel executions. The frameworkxe2x80x99s automated diagnosis of scalability losses indicates their symptoms, severity, and causes. Two case studies illustrate the effectiveness of this framework. When compared to a tool that analyzes performance using instrumentationbased traces, our overhead for collecting samplebased time series is 128 in time and 11600 in space while our automated analysis takes 125 of the time.