@article {14805,
	title = {A tool to help tune where computation is performed},
	journal = {IEEE Transactions on Software Engineering},
	volume = {27},
	year = {2001},
	month = {2001/07//},
	pages = {618 - 629},
	abstract = {We introduce a new performance metric, called load balancing factor (LBF), to assist programmers when evaluating different tuning alternatives. The LBF metric differs from traditional performance metrics since it is intended to measure the performance implications of a specific tuning alternative rather than quantifying where time is spent in the current version of the program. A second unique aspect of the metric is that it provides guidance about moving work within a distributed or parallel program rather than reducing it. A variation of the LBF metric can also be used to predict the performance impact of changing the underlying network. The LBF metric is computed incrementally and online during the execution of the program to be tuned. We also present a case study that shows that our metric can accurately predict the actual performance gains for a test suite of six programs},
	keywords = {Computational modeling, Current measurement, Distributed computing, distributed program, distributed programming, load balancing factor, Load management, parallel program, parallel programming, Performance analysis, performance evaluation, Performance gain, performance metric, Programming profession, software metrics, software performance evaluation, Testing, Time measurement, tuning},
	isbn = {0098-5589},
	doi = {10.1109/32.935854},
	author = {Eom, Hyeonsang and Hollingsworth, Jeffrey K}
}
@conference {14784,
	title = {LBF: a performance metric for program reorganization},
	booktitle = {18th International Conference on Distributed Computing Systems, 1998. Proceedings},
	year = {1998},
	month = {1998/05/26/29},
	pages = {222 - 229},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We introduce a new performance metric, called Load Balancing Factor (LBF), to assist programmers with evaluating different tuning alternatives. The LBF metric differs from traditional performance metrics since it is intended to measure the performance implications of a specific tuning alternative rather than quantifying where time is spent in the current version of the program. A second unique aspect of the metric is that it provides guidance about moving work within a distributed or parallel program rather than reducing it. A variation of the LBF metric can also be used to predict the performance impact of changing the underlying network. The LBF metric can be computed incrementally and online during the execution of the program to be tuned. We also present a case study that shows that our metric can predict the actual performance gains accurately for a test suite of six programs},
	keywords = {case study, Computational modeling, computer network, Computer science, Debugging, distributed processing, distributed program, Educational institutions, Integrated circuit testing, LBF metric, load balancing factor, Load management, measurement, NIST, parallel program, parallel programming, performance metric, program reorganization, program tuning, Programming profession, resource allocation, software metrics, software performance evaluation, US Department of Energy},
	isbn = {0-8186-8292-2},
	doi = {10.1109/ICDCS.1998.679505},
	author = {Eom, H. and Hollingsworth, Jeffrey K}
}
