@article {1176,
	title = {Argobots: A Lightweight Low-Level Threading and Tasking Framework},
	journal = {IEEE Transactions on Parallel and Distributed Systems},
	year = {2017},
	month = {2017-10},
	abstract = {In the past few decades, a number of user-level threading and tasking models have been proposed in the literature to address the shortcomings of OS-level threads, primarily with respect to cost and flexibility. Current state-of-the-art user-level threading and tasking models, however, are either too specific to applications or architectures or are not as powerful or flexible. In this paper, we present Argobots, a lightweight, low-level threading and tasking framework that is designed as a portable and performant substrate for high-level programming models or runtime systems. Argobots offers a carefully designed execution model that balances generality of functionality with providing a rich set of controls to allow specialization by the user or high-level programming model. We describe the design, implementation, and optimization of Argobots and present integrations with three example high-level models: OpenMP, MPI, and co-located I/O service. Evaluations show that (1) Argobots outperforms existing generic threading runtimes; (2) our OpenMP runtime offers more efficient interoperability capabilities than production OpenMP runtimes do; (3) when MPI interoperates with Argobots instead of Pthreads, it enjoys reduced synchronization costs and better latency hiding capabilities; and (4) I/O service with Argobots reduces interference with co-located applications, achieving performance competitive with that of the Pthreads version.},
	keywords = {Argobots, context switch, I/O, interoperability, lightweight, MPI, OpenMP, stackable scheduler, tasklet, user-level thread},
	doi = {10.1109/TPDS.2017.2766062},
	url = {http://ieeexplore.ieee.org/document/8082139/},
	author = {Sangmin Seo and Abdelhalim Amer and Pavan Balaji and Cyril Bordage and George Bosilca and Alex Brooks and Philip Carns and Adrian Castello and Damien Genet and Thomas Herault and Shintaro Iwasaki and Prateek Jindal and Sanjay Kale and Sriram Krishnamoorthy and Jonathan Lifflander and Huiwei Lu and Esteban Meneses and Mar Snir and Yanhua Sun and Kenjiro Taura and Pete Beckman}
}
@inbook {1384,
	title = {Performance Analysis and Debugging Tools at Scale},
	booktitle = {Exascale Scientific Applications: Scalability and Performance Portability},
	year = {2017},
	month = {2017-11},
	pages = {17-50},
	publisher = {Chapman \& Hall / CRC Press},
	organization = {Chapman \& Hall / CRC Press},
	chapter = {2},
	abstract = {This chapter explores present-day challenges and those likely to arise as new hardware and software technologies are introduced on the path to exascale. It covers some of the underlying hardware, software, and techniques that enable tools and debuggers. Performance tools and debuggers are critical components that enable computational scientists to fully exploit the computing power of While high-performance computing systems. Instrumentation is the insertion of code to perform measurement in a program. It is vital step in performance analysis, especially for parallel programs. The essence of a debugging tool is enabling observation, exploration, and control of program state, such that a developer can, for example, verify that what is currently occurring correlates to what is intended. The increased complexity and volume of performance and debugging data likely to be seen on exascale systems risks overwhelming tool users. Tools and debuggers may need to develop advanced techniques such as automated filtering and analysis to reduce the complexity seen by the user.},
	isbn = {9781315277400},
	doi = {https://doi.org/10.1201/b21930},
	author = {Scott Parker and John Mellor-Crummey and Dong H. Ahn and Heike Jagode and Holger Brunst and Sameer Shende and Allen D. Malony and David DelSignore and Ronny Tschuter and Ralph Castain and Kevin Harms and Philip Carns and Ray Loy and Kalyan Kumaran}
}
