@article {,
	title = {Materials fingerprinting classification},
	journal = {Computer Physics Communications},
	year = {2021},
	month = {Jan-05-2021},
	pages = {108019},
	abstract = {Significant progress in many classes of materials could be made with the availability of experimentally-derived large datasets composed of atomic identities and three-dimensional coordinates. Methods for visualizing the local atomic structure, such as atom probe tomography (APT), which routinely generate datasets comprised of millions of atoms, are an important step in realizing this goal. However, state-of-the-art APT instruments generate noisy and sparse datasets that provide information about elemental type, but obscure atomic structures, thus limiting their subsequent value for materials discovery. The application of a materials fingerprinting process, a machine learning algorithm coupled with topological data analysis, provides an avenue by which here-to-fore unprecedented structural information can be extracted from an APT dataset. As a proof of concept, the material fingerprint is applied to high-entropy alloy APT datasets containing body-centered cubic (BCC) and face-centered cubic (FCC) crystal structures. A local atomic configuration centered on an arbitrary atom is assigned a topological descriptor, with which it can be characterized as a BCC or FCC lattice with near perfect accuracy, despite the inherent noise in the dataset. This successful identification of a fingerprint is a crucial first step in the development of algorithms which can extract more nuanced information, such as chemical ordering, from existing datasets of complex materials.},
	keywords = {Atom probe tomography, High entropy alloy, Machine Learning, Materials discovery, Topological data analysis},
	issn = {00104655},
	doi = {10.1016/j.cpc.2021.108019},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0010465521001314},
	author = {Spannaus, Adam and Law, Kody J.H. and Piotr Luszczek and Nasrin, Farzana and Micucci, Cassie Putman and Liaw, Peter K. and Santodonato, Louis J. and Keffer, David J. and Maroulas, Vasileios}
}
@conference {,
	title = {FFT-Based Gradient Sparsification for the Distributed Training of Deep Neural Networks},
	booktitle = {9th International Symposium on High-Performance Parallel and Distributed Computing (HPDC 20)},
	year = {2020},
	month = {2020-06},
	publisher = {ACM},
	organization = {ACM},
	address = {Stockholm, Sweden},
	abstract = {The performance and efficiency of distributed training of Deep Neural Networks (DNN) highly depend on the performance of gradient averaging among participating processes, a step bound by communication costs. There are two major approaches to reduce communication overhead: overlap communications with computations (lossless), or reduce communications (lossy). The lossless solution works well for linear neural architectures, e.g. VGG, AlexNet, but more recent networks such as ResNet and Inception limit the opportunity for such overlapping. Therefore, approaches that reduce the amount of data (lossy) become more suitable. In this paper, we present a novel, explainable lossy method that sparsifies gradients in the frequency domain, in addition to a new range-based float point representation to quantize and further compress gradients. These dynamic techniques strike a balance between compression ratio, accuracy, and computational overhead, and are optimized to maximize performance in heterogeneous environments.

Unlike existing works that strive for a higher compression ratio, we stress the robustness of our methods, and provide guidance to recover accuracy from failures. To achieve this, we prove how the FFT sparsification affects the convergence and accuracy, and show that our method is guaranteed to converge using a diminishing θ in training. Reducing θ can also be used to recover accuracy from the failure. Compared to STOA lossy methods, e.g., QSGD, TernGrad, and Top-k sparsification, our approach incurs less approximation error, thereby better in both the wall-time and accuracy. On an 8 GPUs, InfiniBand interconnected cluster, our techniques effectively accelerate AlexNet training up to 2.26x to the baseline of no compression, and 1.31x to QSGD, 1.25x to Terngrad and 1.47x to Top-K sparsification.},
	keywords = {FFT, Gradient Compression, Loosy Gradients, Machine Learning, Neural Networks},
	doi = {https://doi.org/10.1145/3369583.3392681},
	author = {Linnan Wang and Wei Wu and Junyu Zhang and Hang Liu and George Bosilca and Maurice Herlihy and Rodrigo Fonseca}
}
@conference {,
	title = {Predicting MPI Collective Communication Performance Using Machine Learning},
	booktitle = {2020 IEEE International Conference on Cluster Computing (CLUSTER)},
	year = {2020},
	month = {2020-09},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Kobe, Japan},
	abstract = {The Message Passing Interface (MPI) defines the semantics of data communication operations, while the implementing libraries provide several parameterized algorithms for each operation. Each algorithm of an MPI collective operation may work best on a particular system and may be dependent on the specific communication problem. Internally, MPI libraries employ heuristics to select the best algorithm for a given communication problem when being called by an MPI application. The majority of MPI libraries allow users to override the default algorithm selection, enabling the tuning of this selection process. The problem then becomes how to select the best possible algorithm for a specific case automatically. In this paper, we address the algorithm selection problem for MPI collective communication operations. To solve this problem, we propose an auto-tuning framework for collective MPI operations based on machine-learning techniques. First, we execute a set of benchmarks of an MPI library and its entire set of collective algorithms. Second, for each algorithm, we fit a performance model by applying regression learners. Last, we use the regression models to predict the best possible (fastest) algorithm for an unseen communication problem. We evaluate our approach for different MPI libraries and several parallel machines. The experimental results show that our approach outperforms the standard algorithm selection heuristics, which are hard-coded into the MPI libraries, by a significant margin.},
	keywords = {Auto-tuning, GAM, KNN, Machine Learning, message passing interface, Performance Prediction, XGBoost},
	doi = {https://doi.org/10.1109/CLUSTER49012.2020.00036},
	author = {Sascha Hunold and Abhinav Bhatele and George Bosilca and Peter Knees}
}
