Integrating Database Technology with Comparison-based Parallel Performance Diagnosis: The PerfTrack Performance Experiment Management Tool

Abstract

PerfTrack is a data store and interface for managing performance data from large-scale parallel applications. Data IntroductionEffective performance tuning of large scale parallel and distributed applications on current and future clusters, supercomputers, and grids requires the ability to integrate performance data gathered with a variety of monitoring tools, stored in different formats, and possibly residing in geographically separate data stores. Performance data sharing between different performance studies or scientists is currently done manually or not done at all. Manual transfer of disorganized files with unique and varying storage formats is a time consuming and error prone approach. The granularity of exchange is often entire data sets, even if only a small subset of the transferred data is actually needed. All of these problems worsen as high end systems continue to scale up, greatly increasing the size of the data sets generated by performance studies. The overhead for sharing data discourages collaboration and data reuse. There are several key challenges associated with performance data sharing. First, performance tool output is not uniform, requiring the ability to translate between different metrics and tool outputs to compare the datasets. Second, the data sets resulting from performance studies of tera-and peta-scale level applications are potentially large and raise challenging scalability issues. For some types of summary data, database storage is a wellunderstood task; for others, such as trace data and data generated with a dynamic instrumentation based tool, further research is necessary to develop an efficient and flexible representation. In addition to increased ease of collaboration between scientists using or studying a common application, the potential benefits of a tool to collect

Machine learning based online performance prediction for runtime parallelization and task scheduling

“…Many projects, such as PerfDMF [26], PerfTrack [27] and Prophesy [28], combine the use of a repository with performance profiling and modeling to facilitate performance data storage and management. However, these existing tools use profile data in an offline manner for performance modeling and debugging.…”

Storage-Intensive Supercomputing Benchmark Study

“…The iotrace library can store trace data in a text format that can be imported into a spreadsheet or the PerfTrack [11] performance database. It can also store data in a more compact binary format.…”

Practical Differential Profiling

“…Both PerfDMF [3] and PerfTrack [5] provide a base infrastructure capable of supporting differential performance analysis. They both deploy relational databases to store the results of performance analysis across multiple runs.…”