@article {lively2011energy,
	title = {Energy and performance characteristics of different parallel implementations of scientific applications on multicore systems},
	journal = {International Journal of High Performance Computing Applications},
	volume = {25},
	number = {3},
	year = {2011},
	month = {2011-00},
	pages = {342-350},
	keywords = {mumi},
	author = {Charles Lively and Xingfu Wu and Valerie Taylor and Shirley Moore and Hung-Ching Chang and Kirk Cameron}
}
@conference {malony2011parallel,
	title = {Parallel Performance Measurement of Heterogeneous Parallel Systems with {GPUs}},
	booktitle = {International Conference on Parallel Processing (ICPP{\textquoteright}11)},
	year = {2011},
	month = {2011-09},
	publisher = {ACM},
	organization = {ACM},
	address = {Taipei, Taiwan},
	abstract = {The power of GPUs is giving rise to heterogeneous parallel computing, with new demands on programming environments, runtime systems, and tools to deliver high-performing applications. This paper studies the problems associated with performance measurement of heterogeneous machines with GPUs. A heterogeneous computation model and alternative host-GPU measurement approaches are discussed to set the stage for reporting new capabilities for heterogeneous parallel performance measurement in three leading HPC tools: PAPI, Vampir, and the TAU Performance System. Our work leverages the new CUPTI tool support in NVIDIA{\textquoteright}s CUDA device library. Heterogeneous benchmarks from the SHOC suite are used to demonstrate the measurement methods and tool support.},
	keywords = {magma, mumi, papi},
	isbn = {978-0-7695-4510-3},
	doi = {10.1109/ICPP.2011.71},
	author = {Allen D. Malony and Scott Biersdorff and Sameer Shende and Heike Jagode and Stanimire Tomov and Guido Juckeland and Robert Dietrich and Duncan Poole and Christopher Lamb}
}
@inproceedings {lively2011poweraware,
	title = {Power-Aware Prediction Models of Hybrid ({MPI/OpenMP)} Scientific Applications},
	booktitle = {International Conference on Energy-Aware High Performance Computing (EnA-HPC 2011)},
	year = {2011},
	month = {2011-09},
	address = {Hamburg, Germany},
	keywords = {mumi},
	author = {Charles Lively and Xingfu Wu and Valerie Taylor and Shirley Moore and Hung-Ching Chang and Chun-Yi Su and Kirk Cameron}
}
@inproceedings {ltaeif2011profiling,
	title = {Profiling High Performance Dense Linear Algebra Algorithms on Multicore Architectures for Power and Energy Efficiency},
	booktitle = {International Conference on Energy-Aware High Performance Computing (EnA-HPC 2011)},
	year = {2011},
	month = {2011-09},
	address = {Hamburg, Germany},
	keywords = {mumi},
	author = {Hatem Ltaeif and Piotr Luszczek and Jack Dongarra}
}
@article {moore2011userdefined,
	title = {User-Defined Events for Hardware Performance Monitoring},
	journal = {Procedia Computer Science},
	volume = {4},
	year = {2011},
	month = {2011-05},
	pages = {2096-2104},
	publisher = {Elsevier},
	abstract = {PAPI is a widely used cross-platform interface to hardware performance counters. PAPI currently supports native events, which are those provided by a given platform, and preset events, which are pre-defined events thought to be common across platforms. Presets are currently mapped and defined at the time that PAPI is compiled and installed. The idea of user-defined events is to allow users to define their own metrics and to have those metrics mapped to events on a platform without the need to re-install PAPI. User-defined events can be defined in terms of native, preset, and previously defined user-defined events. The user can combine events and constants in an arbitrary expression to define a new metric and give a name to the new metric. This name can then be specified as a PAPI event in a PAPI library call the same way as native and preset events. End-user tools such as TAU and Scalasca that use PAPI can also use the user-defined metrics. Users can publish their metric definitions so that other users can use them as well. We present several examples of how user-defined events can be used for performance analysis and modeling.},
	keywords = {mumi, papi},
	doi = {https://doi.org/10.1016/j.procs.2011.04.229},
	author = {Shirley Moore and James Ralph}
}
@article {terpstra2010collecting,
	title = {Collecting Performance Data with {PAPI-C}},
	journal = {Tools for High Performance Computing 2009},
	year = {2010},
	month = {2010-05},
	pages = {157-173},
	publisher = {Springer Berlin / Heidelberg},
	address = {3rd Parallel Tools Workshop, Dresden, Germany},
	abstract = {Modern high performance computer systems continue to increase in size and complexity. Tools to measure application performance in these increasingly complex environments must also increase the richness of their measurements to provide insights into the increasingly intricate ways in which software and hardware interact. PAPI (the Performance API) has provided consistent platform and operating system independent access to CPU hardware performance counters for nearly a decade. Recent trends toward massively parallel multi-core systems with often heterogeneous architectures present new challenges for the measurement of hardware performance information, which is now available not only on the CPU core itself, but scattered across the chip and system. We discuss the evolution of PAPI into Component PAPI, or PAPI-C, in which multiple sources of performance data can be measured simultaneously via a common software interface. Several examples of components and component data measurements are discussed. We explore the challenges to hardware performance measurement in existing multi-core architectures. We conclude with an exploration of future directions for the PAPI interface.},
	keywords = {mumi, papi},
	doi = {https://doi.org/10.1007/978-3-642-11261-4_11},
	author = {Dan Terpstra and Heike Jagode and Haihang You and Jack Dongarra}
}
@article {song2009analytical,
	title = {Analytical Modeling and Optimization for Affinity Based Thread Scheduling on Multicore Systems},
	journal = {IEEE Cluster 2009},
	year = {2009},
	month = {2009-08},
	address = {New Orleans},
	keywords = {gridpac, mumi},
	author = {Fengguang Song and Shirley Moore and Jack Dongarra}
}
@inproceedings {song2009dynamic,
	title = {Dynamic Task Scheduling for Linear Algebra Algorithms on Distributed-Memory Multicore Systems},
	booktitle = {International Conference for High Performance Computing, Networking, Storage, and Analysis (SC {\textquoteright}09)},
	year = {2009},
	month = {2009-11},
	address = {Portland, OR},
	keywords = {mumi, plasma},
	author = {Fengguang Song and Asim YarKhan and Jack Dongarra}
}
