Optimizing matrix multiply using PHiPAC: a portable, high-performance, ANSI C coding methodology
ICS '97 Proceedings of the 11th international conference on Supercomputing
SPL: a language and compiler for DSP algorithms
Proceedings of the ACM SIGPLAN 2001 conference on Programming language design and implementation
Continuous Program Optimization: Design and Evaluation
IEEE Transactions on Computers
Automatically tuned linear algebra software
SC '98 Proceedings of the 1998 ACM/IEEE conference on Supercomputing
Automatically characterizing large scale program behavior
Proceedings of the 10th international conference on Architectural support for programming languages and operating systems
OpenMP: An Industry-Standard API for Shared-Memory Programming
IEEE Computational Science & Engineering
Runtime Power Monitoring in High-End Processors: Methodology and Empirical Data
Proceedings of the 36th annual IEEE/ACM International Symposium on Microarchitecture
Using multiple energy gears in MPI programs on a power-scalable cluster
Proceedings of the tenth ACM SIGPLAN symposium on Principles and practice of parallel programming
Statistical Models for Empirical Search-Based Performance Tuning
International Journal of High Performance Computing Applications
Think globally, search locally
Proceedings of the 19th annual international conference on Supercomputing
Minimizing execution time in MPI programs on an energy-constrained, power-scalable cluster
Proceedings of the eleventh ACM SIGPLAN symposium on Principles and practice of parallel programming
Online power-performance adaptation of multithreaded programs using hardware event-based prediction
Proceedings of the 20th annual international conference on Supercomputing
STAR-MPI: self tuned adaptive routines for MPI collective operations
Proceedings of the 20th annual international conference on Supercomputing
Profitable loop fusion and tiling using model-driven empirical search
Proceedings of the 20th annual international conference on Supercomputing
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Multithreaded programs executing on modern high-end computing systems have many potential avenues to adapt their execution to improve performance, energy consumption, or both. Program adaptation occurs anytime multiple execution modes are available to the application and one is selected based on information collected during program execution. As a result, some degree of online or offline analysis is required to come to a decision of how best to adapt and there are a variety of tradeoffs to consider when deciding which form of analysis to use, as the overheads they carry with them can vary widely in degree as well as type, as can their effectiveness. In this paper, we attempt to qualitatively and quantitatively analyze the pros and cons of specific types of online and offline forms of information collection and analysis for use in dynamic program adaptation in the context of high performance computing. We focus on providing recommendations of which strategy to employ for users with specific requirements. To justify our recommendations we use data collected from two offline and three online analysis strategies used with a specific power-performance adaptation technique, concurrency throttling. We provide a two-level analysis, comparing online and offline strategies and then comparing strategies within each category. Our results show clear trends in the appropriateness of particular strategies depending on the length of application execution - more specifically the number of iterations in the program - as well as different expected use characteristics ranging from one execution to many, with fixed versus variable program inputs across executions.