@conference {,
	title = {Pushing the Boundaries of Small Tasks: Scalable Low-Overhead Data-Flow Programming in TTG},
	booktitle = {2022 IEEE International Conference on Cluster Computing (CLUSTER)},
	year = {2022},
	month = {2022-09},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Heidelberg, Germany},
	abstract = {Shared memory parallel programming models strive to provide low-overhead execution environments. Task-based programming models, in particular, are well-suited to cope with the ubiquitous multi- and many-core systems since they allow applications to express all available concurrency to a scheduler, which is tasked with exploiting the available hardware resources. It is general consensus that atomic operations should be preferred over locks and mutexes to avoid inter-thread serialization and the resulting loss in efficiency. However, even atomic operations may serialize threads if not used judiciously. In this work, we will discuss several optimizations applied to TTG and the underlying PaRSEC runtime system aiming at removing contentious atomic operations to reduce the overhead of task management to a few hundred clock cycles. The result is an optimized data-flow programming system that seamlessly scales from a single node to distributed execution and which is able to compete with OpenMP in shared memory.},
	keywords = {Dataflow graph, Hardware, Instruction sets, Memory management, PaR-SEC, parallel programming, runtime, scalability, Task analysis, task-based programming, Template Task Graph, TTG},
	doi = {10.1109/CLUSTER51413.2022.00026},
	url = {https://ieeexplore.ieee.org/document/9912704/},
	author = {Schuchart, Joseph and Nookala, Poornima and Herault, Thomas and Valeev, Edward F. and George Bosilca}
}
@inproceedings {,
	title = {Scalability Issues in FFT Computation},
	journal = {International Conference on Parallel Computing Technologies},
	year = {2021},
	pages = {279{\textendash}287},
	publisher = {Springer},
	abstract = {The fast Fourier transform (FFT), is one the most important tools in mathematics, and it is widely required by several applications of science and engineering. State-of-the-art parallel implementations of the FFT algorithm, based on Cooley-Tukey developments, are known to be communication-bound, which causes critical issues when scaling the computational and architectural capabilities. In this paper, we study the main performance bottleneck of FFT computations on hybrid CPU and GPU systems at large-scale. We provide numerical simulations and potential acceleration techniques that can be easily integrated into FFT distributed libraries. We present different experiments on performance scalability and runtime analysis on the world{\textquoteright}s most powerful supercomputers today: Summit, using up to 6,144 NVIDIA V100 GPUs, and Fugaku, using more than one million Fujitsu A64FX cores.},
	keywords = {Hybrid systems, Parallel FFT, scalability},
	isbn = {978-3-030-86359-3},
	doi = {10.1007/978-3-030-86359-3_21},
	author = {Alan Ayala and Stanimire Tomov and Stoyanov, Miroslav and Jack Dongarra}
}
@inbook {1213,
	title = {Interoperable Convergence of Storage, Networking, and Computation},
	booktitle = {Advances in Information and Communication: Proceedings of the 2019 Future of Information and Communication Conference (FICC)},
	number = {2},
	year = {2020},
	pages = {667-690},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {In every form of digital store-and-forward communication, intermediate forwarding nodes are computers, with attendant memory and processing resources. This has inevitably stimulated efforts to create a wide-area infrastructure that goes beyond simple store-and-forward to create a platform that makes more general and varied use of the potential of this collection of increasingly powerful nodes. Historically, these efforts predate the advent of globally routed packet networking. The desire for a converged infrastructure of this kind has only intensified over the last 30 years, as memory, storage, and processing resources have increased in both density and speed while simultaneously decreasing in cost. Although there is a general consensus that it should be possible to define and deploy such a dramatically more capable wide-area platform, a great deal of investment in research prototypes has yet to produce a credible candidate architecture. Drawing on technical analysis, historical examples, and case studies, we present an argument for the hypothesis that in order to realize a distributed system with the kind of convergent generality and deployment scalability that might qualify as "future-defining," we must build it from a small set of simple, generic, and limited abstractions of the low level resources (processing, storage and network) of its intermediate nodes.},
	keywords = {active networks, distributed cloud, distributed processing, distributed storage, edge computing, network convergence, network layering, scalability},
	isbn = {978-3-030-12385-7},
	author = {Micah Beck and Terry Moore and Piotr Luszczek and Anthony Danalis},
	editor = {Kohei Arai and Rahul Bhatia}
}
