www.scalasca.org SCALASCA is a performance toolset that has been specifically designed to analyze parallel application execution behavior on large-scale systems. It offers an incremental performanceanalysis procedure that integrates runtime summaries with in-depth studies of concurrent behavior via event tracing, adopting a strategy of successively refined measurement configurations. Distinctive features are its ability to identify wait states in applications with very large numbers of processes and combine these with efficiently summarized local measurements. In this article, we review the current toolset architecture, emphasizing its scalable design and the role of the different components in transforming raw measurement data into knowledge of application execution behavior. The scalability and effectiveness of SCALASCA are then surveyed from experience measuring and analyzing real-world applications on a range of computer systems. 1

Abstract—In message-passing applications, the temporal or spatial distance between cause and symptom of a performance problem constitutes a major difficulty in deriving helpful conclusions from performance data. Just knowing the locations of wait states in the program is often insufficient to understand the reason for their occurrence. We present a method for verifying hypotheses on causality between temporally or spatially distant performance phenomena in message-passing applications without altering the application itself. The verification is accomplished by modifying MPI event traces and using them to simulate the hypothetical message-passing behavior. By performing a parallel real-time reenactment of the communication to be simulated using the original execution configuration, we can achieve high scalability and good predictive accuracy in relation to the measured behavior. Not relying on a potentially complex model of the message-passing subsystem, our method is also platform independent. I.

Abstract. In our previous work [1], we introduced performance simulation as an instrument to verify hypotheses on causality between locally and spatially distant performance phenomena without altering the application itself. This is accomplished by modifying mpi event traces and using them to simulate hypothetical message-passing behavior. Here, we present enhancements to our approach, which was previously restricted to blocking communication, that now allow us to correctly simulate mpi non-blocking communication. We enhanced the underlying trace data format to record communication requests, and extended the simulator to even retain the inherently non-deterministic behavior of operations such as MPI Waitany. 1

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toolset architecture

In message-passing applications, the temporal or spatial distance between cause and symptom of a performance problem constitutes a major difficulty in deriving helpful conclusions from performance data. So just knowing the locations of wait states in the program is often insufficient to understand the reason for their occurrence. We therefore present a method for verifying hypotheses on causal connections between temporally or spatially distant performance phenomena without altering the application itself. The verification is accomplished by modifying MPI event traces and using them to simulate the hypothetical message-passing behavior. By performing a parallel real-time reenactment of the communication to be simulated using the original execution configuration, we can achieve high scalability and satisfactory predictive accuracy in relation to the measured behavior. Not relying on a potentially complex model of the message-passing subsystem, our method is also platform independent. 1