@inproceedings {,
	title = {Memory Traffic and Complete Application Profiling with PAPI Multi-Component Measurements},
	journal = {2023 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)},
	year = {2023},
	month = {2023-08},
	publisher = {IEEE},
	address = {St. Petersburg, Florida},
	abstract = {Some of the most important categories of performance events count the data traffic between the processing cores and the main memory. However, since these counters are not core-private, applications require elevated privileges to access them. PAPI offers a component that can access this information on IBM systems through the Performance Co-Pilot (PCP); however, doing so adds an indirection layer that involves querying the PCP daemon. This paper performs a quantitative study of the accuracy of the measurements obtained through this component on the Summit supercomputer. We use two linear algebra kernels---a generalized matrix multiply, and a modified matrix-vector multiply---as benchmarks and a distributed, GPU-accelerated 3D-FFT mini-app (using cuFFT) to compare the measurements obtained through the PAPI PCP component against the expected values across different problem sizes. We also compare our measurements against an in-house machine with a very similar architecture to Summit, where elevated privileges allow PAPI to access the hardware counters directly (without using PCP) to show that measurements taken via PCP are as accurate as the those taken directly. Finally, using both QMCPACK and the 3D-FFT, we demonstrate the diverse hardware activities that can be monitored simultaneously via PAPI hardware components.},
	keywords = {GPU power, High Performance Computing, network traffic, papi, performance analysis, Performance Counters},
	doi = {10.1109/IPDPSW59300.2023.00070},
	url = {https://ieeexplore.ieee.org/document/10196656},
	author = {Daniel Barry and Heike Jagode and Anthony Danalis and Jack Dongarra}
}
@article {,
	title = {Memory Traffic and Complete Application Profiling with PAPI Multi-Component Measurements},
	year = {2023},
	month = {2023-05},
	publisher = {28th HIPS Workshop},
	address = {St. Petersburg, FL},
	author = {Daniel Barry and Heike Jagode and Anthony Danalis and Jack Dongarra}
}
@inbook {,
	title = {Effortless Monitoring of Arithmetic Intensity with PAPI{\textquoteright}s Counter Analysis Toolkit},
	booktitle = {Tools for High Performance Computing 2018/2019},
	year = {2021},
	pages = {195{\textendash}218},
	publisher = {Springer},
	organization = {Springer},
	abstract = {With exascale computing forthcoming, performance metrics such as memory traffic and arithmetic intensity are increasingly important for codes that heavily utilize numerical kernels. Performance metrics in different CPU architectures can be monitored by reading the occurrences of various hardware events. However, from architecture to architecture, it becomes more and more unclear which native performance events are indexed by which event names, making it difficult for users to understand what specific events actually measure. This ambiguity seems particularly true for events related to hardware that resides beyond the compute core, such as events related to memory traffic. Still, traffic to memory is a necessary characteristic for determining arithmetic intensity. To alleviate this difficulty, PAPI{\textquoteright}s Counter Analysis Toolkit measures the occurrences of events through a series of benchmarks, allowing its users to discover the high-level meaning of native events. We (i) leverage the capabilities of the Counter Analysis Toolkit to identify the names of hardware events for reading and writing bandwidth utilization in addition to floating-point operations, (ii) measure the occurrences of the events they index during the execution of important numerical kernels, and (iii) verify their identities by comparing these occurrence patterns to the expected arithmetic intensity of the numerical kernels.},
	isbn = {978-3-030-66057-4},
	doi = {10.1007/978-3-030-66057-4_11},
	author = {Daniel Barry and Danalis, Anthony and Heike Jagode}
}
@conference {,
	title = {Effortless Monitoring of Arithmetic Intensity with PAPI{\textquoteright}s Counter Analysis Toolkit},
	booktitle = {13th International Workshop on Parallel Tools for High Performance Computing},
	year = {2020},
	month = {2020-09},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Dresden, Germany},
	abstract = {With exascale computing forthcoming, performance metrics such as memory traffic and arithmetic intensity are increasingly important for codes that heavily utilize numerical kernels. Performance metrics in different CPU architectures can be monitored by reading the occurrences of various hardware events. However, from architecture to architecture, it becomes more and more unclear which native performance events are indexed by which event names, making it difficult for users to understand what specific events actually measure. This ambiguity seems particularly true for events related to hardware that resides beyond the compute core, such as events related to memory traffic. Still, traffic to memory is a necessary characteristic for determining arithmetic intensity. To alleviate this difficulty, PAPI{\textquoteright}s Counter Analysis Toolkit measures the occurrences of events through a series of benchmarks, allowing its users to discover the high-level meaning of native events. We (i) leverage the capabilities of the Counter Analysis Toolkit to identify the names of hardware events for reading and writing bandwidth utilization in addition to floating-point operations, (ii) measure the occurrences of the events they index during the execution of important numerical kernels, and (iii) verify their identities by comparing these occurrence patterns to the expected arithmetic intensity of the numerical kernels.},
	author = {Daniel Barry and Anthony Danalis and Heike Jagode}
}
@article {,
	title = {Performance Application Programming Interface for Extreme-Scale Environments (PAPI-EX) (Poster)},
	year = {2020},
	month = {2020-20},
	publisher = {2020 NSF Cyberinfrastructure for Sustained Scientific Innovation (CSSI) Principal Investigator Meeting},
	address = {Seattle, WA},
	author = {Jack Dongarra and Heike Jagode and Anthony Danalis and Daniel Barry and Vince Weaver}
}
@article {1390,
	title = {Understanding Native Event Semantics},
	year = {2019},
	month = {2019-04},
	publisher = {9th JLESC Workshop},
	address = {Knoxville, TN},
	author = {Anthony Danalis and Heike Jagode and Daniel Barry and Jack Dongarra}
}
