Experiment management support for performance tuning
Authors: Karen L. Karavanic, Barton P. Miller
Venue: Proceedings of the 1997 ACM/IEEE conference on Supercomputing (CDROM) (1997)

The development of a high-performance parallel system or application is an evolutionary process. It may begin with models or simulations, followed by an initial implementation of the program. The code is then incrementally modified to tune its performance and continues to evolve throughout the applications's life span. At each step, the key question for developers is: how and how much did the performance change? This question arises comparing an implementation to models or simulations; considering versions of an implementation that use a different algorithm, communication or numeric library, or language; studying code behavior by varying number or type of processors, type of network, type of processes, input data set or work load, or scheduling algorithm; and in benchmarking or regression testing. Despite the broad utility of this type of comparison, no existing performance tool provides the necessary functionality to answer it; even state of the art research tools such as Paradyn[2] and Pablo[3] focus instead on measuring the performance of a single program execution. We describe an infrastructure for answering this question at all stages of the life of an application. We view each program run, simulation result, or program model as an experiment, and provide this functionality in an Experiment Management system. Our project has three parts: (1) a representation for the space of executions, (2) techniques for quantitatively and automatically comparing two or more executions, and (3) enhanced performance diagnosis abilities based on historic performance data. In this paper we present initial results on the first two parts. The measure of success of this project is that an activity that was complex and cumbersome to do manually, we can automate. The first part is a concise representation for the set of executions collected over the life of an application. We store information about each experiment in a Program Event, which enumerates the components of the code executed and the execution environment, and stores the performance data collected. The possible combinations of code and execution environment form the multi-dimensional Program Space, with one dimension for each axis of variation and one point for each Program Event. We enable exploration of this space with a simple naming mechanism, a selection and query facility, and a set of interactive visualizations. Queries on a Program Space may be made both on the contents of the performance data and on the metadata that describes the multi-dimensional program space. A graphical representation of the Program Space serves as the user interface to the Experiment Management system. The second part of the project is to develop techniques for automating comparison between experiments. Performance tuning across multiple executions must answer the deceptively simple question: what changed in this run of the program? We have developed techniques for determining the "difference" between two or more program runs, automatically describing both the structural differences (differences in program execution structure and resources used), and the performance variation (how were the resources used and how did this change from one run to the next). We can apply our technique to compare an actual execution with a predicted or desired performance measure for the application, and to compare distinct time intervals of a single program execution. Uses for this include performance tuning efforts, automated scalability studies, resource allocation for metacomputing [4], performance model validation studies, and dynamic execution models where processes are created, destroyed, migrated [5], communication patterns and use of distributed shared memory may be optimized [6,9], or data values or code may be changed by steering [7,8]. The difference information is not necessarily a simple measure such as total execution time, but may be a more complex measure derived from details of the program structure, an analytical performance prediction, an actual previous execution of the code, a set of performance thresholds that the application is required to meet or exceed, or an incomplete set of data from selected intervals of an execution. The third part of this research is to investigate the use of the predicted, summary, and historical data contained in the Program Events and Program Space for performance diagnosis. We are exploring novel opportunities for exploiting this collection of data to focus data gathering and analysis efforts to the critical sections of a large application, and for isolating spurious effects from interesting performance variations. Details of this are outside of the scope of this paper.