@conference {,
	title = {A Framework to Exploit Data Sparsity in Tile Low-Rank Cholesky Factorization},
	booktitle = {IEEE International Parallel and Distributed Processing Symposium (IPDPS)},
	year = {2022},
	month = {2022-07},
	doi = {10.1109/IPDPS53621.2022.00047},
	url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=9820680\&isnumber=9820610},
	author = {Qinglei Cao and Rabab Alomairy and Yu Pei and George Bosilca and Hatem Ltaief and David Keyes and Jack Dongarra}
}
@conference {,
	title = {Leveraging PaRSEC Runtime Support to Tackle Challenging 3D Data-Sparse Matrix Problems},
	booktitle = {35th IEEE International Parallel \& Distributed Processing Symposium (IPDPS 2021)},
	year = {2021},
	month = {2021-05},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Portland, OR},
	abstract = {The task-based programming model associated with dynamic runtime systems has gained popularity for challenging problems because of workload imbalance, heterogeneous resources, or extreme concurrency. During the last decade, lowrank matrix approximations, where the main idea consists of exploiting data sparsity typically by compressing off-diagonal tiles up to an application-specific accuracy threshold, have been adopted to address the curse of dimensionality at extreme scale. In this paper, we create a bridge between the runtime and the linear algebra by communicating knowledge of the data sparsity to the runtime. We design and implement this synergistic approach with high user productivity in mind, in the context of the PaRSEC runtime system and the HiCMA numerical library. This requires to extend PaRSEC with new features to integrate rank information into the dataflow so that proper decisions can be taken at runtime. We focus on the tile low-rank (TLR) Cholesky factorization for solving 3D data-sparse covariance matrix problems arising in environmental applications. In particular, we employ the 3D exponential model of Matern matrix kernel, which exhibits challenging nonuniform {\textasciiacute}high ranks in off-diagonal tiles. We first provide a dynamic data structure management driven by a performance model to reduce extra floating-point operations. Next, we optimize the memory footprint of the application by relying on a dynamic memory allocator, and supported by a rank-aware data distribution to cope with the workload imbalance. Finally, we expose further parallelism using kernel recursive formulations to shorten the critical path. Our resulting high-performance implementation outperforms existing data-sparse TLR Cholesky factorization by up to 7-fold on a large-scale distributed-memory system, while minimizing the memory footprint up to a 44-fold factor. This multidisciplinary work highlights the need to empower runtime systems beyond their original duty of task scheduling for servicing next-generation low-rank matrix algebra libraries.},
	keywords = {asynchronous executions and load balancing, dynamic runtime system, environmental applications, High-performance computing, low-rank matrix computations, task-based programming model, user productivity},
	author = {Qinglei Cao and Yu Pei and Kadir Akbudak and George Bosilca and Hatem Ltaief and David Keyes and Jack Dongarra}
}
@conference {,
	title = {Extreme-Scale Task-Based Cholesky Factorization Toward Climate and Weather Prediction Applications},
	booktitle = {Platform for Advanced Scientific Computing Conference (PASC20)},
	year = {2020},
	month = {2020-06},
	publisher = {ACM},
	organization = {ACM},
	address = {Geneva, Switzerland},
	abstract = {Climate and weather can be predicted statistically via geospatial Maximum Likelihood Estimates (MLE), as an alternative to running large ensembles of forward models. The MLE-based iterative optimization procedure requires the solving of large-scale linear systems that performs a Cholesky factorization on a symmetric positive-definite covariance matrix---a demanding dense factorization in terms of memory footprint and computation. We propose a novel solution to this problem: at the mathematical level, we reduce the computational requirement by exploiting the data sparsity structure of the matrix off-diagonal tiles by means of low-rank approximations; and, at the programming-paradigm level, we integrate PaRSEC, a dynamic, task-based runtime to reach unparalleled levels of efficiency for solving extreme-scale linear algebra matrix operations. The resulting solution leverages fine-grained computations to facilitate asynchronous execution while providing a flexible data distribution to mitigate load imbalance. Performance results are reported using 3D synthetic datasets up to 42M geospatial locations on 130, 000 cores, which represent a cornerstone toward fast and accurate predictions of environmental applications.},
	doi = {https://doi.org/10.1145/3394277.3401846},
	author = {Qinglei Cao and Yu Pei and Kadir Akbudak and Aleksandr Mikhalev and George Bosilca and Hatem Ltaief and David Keyes and Jack Dongarra}
}
@conference {1452,
	title = {Performance Analysis of Tile Low-Rank Cholesky Factorization Using PaRSEC Instrumentation Tools},
	booktitle = {Workshop on Programming and Performance Visualization Tools (ProTools 19) at SC19},
	year = {2019},
	month = {2019-11},
	publisher = {ACM},
	organization = {ACM},
	address = {Denver, CO},
	author = {Qinglei Cao and Yu Pei and Thomas Herault and Kadir Akbudak and Aleksandr Mikhalev and George Bosilca and Hatem Ltaief and David Keyes and Jack Dongarra}
}
