A relational approach to monitoring complex systems
ACM Transactions on Computer Systems (TOCS)
Improving online performance diagnosis by the use of historical performance data
SC '99 Proceedings of the 1999 ACM/IEEE conference on Supercomputing
ACM Transactions on Mathematical Software (TOMS) - Special issue in honor of John Rice's 65th birthday
Experiment management support for performance tuning
SC '97 Proceedings of the 1997 ACM/IEEE conference on Supercomputing
POEMS: End-to-End Performance Design of Large Parallel Adaptive Computational Systems
IEEE Transactions on Software Engineering
Prophesy: an infrastructure for performance analysis and modeling of parallel and grid applications
ACM SIGMETRICS Performance Evaluation Review
ZENTURIO: An Experiment Management System for Cluster and Grid Computing
CLUSTER '02 Proceedings of the IEEE International Conference on Cluster Computing
An Algebra for Cross-Experiment Performance Analysis
ICPP '04 Proceedings of the 2004 International Conference on Parallel Processing
A framework for multi-execution performance tuning
On-line monitoring systems and computer tool interoperability
Design and Implementation of a Parallel Performance Data Management Framework
ICPP '05 Proceedings of the 2005 International Conference on Parallel Processing
A study of tracing overhead on a high-performance linux cluster
Proceedings of the 12th ACM SIGPLAN symposium on Principles and practice of parallel programming
Performance Measurement of Novice HPC Programmers Code
SE-HPC '07 Proceedings of the 3rd International Workshop on Software Engineering for High Performance Computing Applications
Practical differential profiling
Euro-Par'07 Proceedings of the 13th international Euro-Par conference on Parallel Processing
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PerfTrack is a data store and interface for managing performance data from large-scale parallel applications. Data collected in different locations and formats can be compared and viewed in a single performance analysis session. The underlying data store used in PerfTrack is implemented with a database management system (DBMS). PerfTrack includes interfaces to the data store and scripts for automatically collecting data describing each experiment, such as build and platform details. We have implemented a prototype of PerfTrack that can use Oracle or PostgreSQL for the data store. We demonstrate the prototype's functionality with three case studies: one is a comparative study of an ASC purple benchmark on high-end Linux and AIX platforms; the second is a parameter study conducted at Lawrence Livermore National Laboratory (LLNL) on two high end platforms, a 128 node cluster of IBM Power 4 processors and BlueGene/L; the third demonstrates incorporating performance data from the Paradyn Parallel Performance Tool into an existing PerfTrack data store.