A new approach to I/O performance evaluation: self-scaling I/O benchmarks, predicted I/O performance
ACM Transactions on Computer Systems (TOCS) - Special issue on computer architecture
Automated performance prediction of message-passing parallel programs
Supercomputing '95 Proceedings of the 1995 ACM/IEEE conference on Supercomputing
Analysis of benchmark characteristics and benchmark performance prediction
ACM Transactions on Computer Systems (TOCS)
Automatic performance prediction to support cross development of parallel programs
SPDT '96 Proceedings of the SIGMETRICS symposium on Parallel and distributed tools
Predicting parallel applications performance on non-dedicated cluster platforms
ICS '98 Proceedings of the 12th international conference on Supercomputing
Performance prediction of large parallel applications using parallel simulations
Proceedings of the seventh ACM SIGPLAN symposium on Principles and practice of parallel programming
Performance prediction tools for parallel discrete-event simulation
PADS '99 Proceedings of the thirteenth workshop on Parallel and distributed simulation
Adaptive performance prediction for distributed data-intensive applications
SC '99 Proceedings of the 1999 ACM/IEEE conference on Supercomputing
Performance prediction for random write reductions: a case study in modeling shared memory programs
SIGMETRICS '02 Proceedings of the 2002 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Automatic Performance Prediction of Parallel Programs
Automatic Performance Prediction of Parallel Programs
Active harmony: towards automated performance tuning
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
A framework for performance modeling and prediction
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
Prediction and Adaptation in Active Harmony
HPDC '98 Proceedings of the 7th IEEE International Symposium on High Performance Distributed Computing
Predictive Application-Performance Modeling in a Computational Grid Environment
HPDC '99 Proceedings of the 8th IEEE International Symposium on High Performance Distributed Computing
Exposing Application Alternatives
ICDCS '99 Proceedings of the 19th IEEE International Conference on Distributed Computing Systems
Automated Cluster-Based Web Service Performance Tuning
HPDC '04 Proceedings of the 13th IEEE International Symposium on High Performance Distributed Computing
MPI performance analysis tools on Blue Gene/L
Proceedings of the 2006 ACM/IEEE conference on Supercomputing
Design and implementation of a dynamic tuning environment
Journal of Parallel and Distributed Computing
Tuning mechanisms for two major parameters of Apache web servers
Software—Practice & Experience
An integrated framework for performance-based optimization of scientific workflows
Proceedings of the 18th ACM international symposium on High performance distributed computing
Tuning parallel applications in parallel
Parallel Computing
Soft computing approach to performance analysis of parallel and distributed programs
Euro-Par'05 Proceedings of the 11th international Euro-Par conference on Parallel Processing
Effective source-to-source outlining to support whole program empirical optimization
LCPC'09 Proceedings of the 22nd international conference on Languages and Compilers for Parallel Computing
Portable section-level tuning of compiler parallelized applications
SC '12 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
A script-based autotuning compiler system to generate high-performance CUDA code
ACM Transactions on Architecture and Code Optimization (TACO) - Special Issue on High-Performance Embedded Architectures and Compilers
AutoTune: a plugin-driven approach to the automatic tuning of parallel applications
PARA'12 Proceedings of the 11th international conference on Applied Parallel and Scientific Computing
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Active Harmony is an automated runtime performance tuning system. In this paper we describe a parameter prioritizing tool to help focus on those parameters that are performance critical. Historical data is also utilized to further speed up the tuning process. We first verify our proposed approaches with synthetic data and finally we verify all the improvements on a real cluster-based web service system. Taken together, these changes allow the Active Harmony system to reduce the time spent tuning from 35% up to 50% and at the same time, reduce the variation in performance while tuning.