SIEVE: a performance debugging environment for parallel programs
Journal of Parallel and Distributed Computing - Special issue on tools and methods for visualization of parallel systems and computations
Modeling and detecting performance problems for distributed and parallel programs with JavaPSL
Proceedings of the 2001 ACM/IEEE conference on Supercomputing
HPCVIEW: A Tool for Top-down Analysis of Node Performance
The Journal of Supercomputing
Scalable analysis techniques for microprocessor performance counter metrics
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
Asserting performance expectations
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
Prophesy: an infrastructure for performance analysis and modeling of parallel and grid applications
ACM SIGMETRICS Performance Evaluation Review
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
PerfExplorer: A Performance Data Mining Framework For Large-Scale Parallel Computing
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
The Tau Parallel Performance System
International Journal of High Performance Computing Applications
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Knowledge engineering for automatic parallel performance diagnosis: Research Articles
Concurrency and Computation: Practice & Experience - European–American Working Group on Automatic Performance Analysis (APART)
Scalable parallel trace-based performance analysis
EuroPVM/MPI'06 Proceedings of the 13th European PVM/MPI User's Group conference on Recent advances in parallel virtual machine and message passing interface
Model-Based relative performance diagnosis of wavefront parallel computations
HPCC'06 Proceedings of the Second international conference on High Performance Computing and Communications
Model-based performance diagnosis of master-worker parallel computations
Euro-Par'06 Proceedings of the 12th international conference on Parallel Processing
Parametric Studies in Eclipse with TAU and PerfExplorer
Euro-Par 2008 Workshops - Parallel Processing
Automatic performance debugging of SPMD-style parallel programs
Journal of Parallel and Distributed Computing
Comprehensive job level resource usage measurement and analysis for XSEDE HPC systems
Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery
Enabling comprehensive data-driven system management for large computational facilities
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
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The integration of scalable performance analysis in parallel development tools is difficult. The potential size of data sets and the need to compare results from multiple experiments presents a challenge to manage and process the information. Simply to characterize the performance of parallel applications running on potentially hundreds of thousands of processor cores requires new scalable analysis techniques. Furthermore, many exploratory analysis processes are repeatable and could be automated, but are now implemented as manual procedures. In this paper, we will discuss the current version of PerfExplorer, a performance analysis framework which provides dimension reduction, clustering and correlation analysis of individual trails of large dimensions, and can perform relative performance analysis between multiple application executions. PerfExplorer analysis processes can be captured in the form of Python scripts, automating what would otherwise be time-consuming tasks. We will give examples of large-scale analysis results, and discuss the future development of the framework, including the encoding and processing of expert performance rules, and the increasing use of performance metadata.