{"doi":"10.2172/2006805","title":"Randomized Algorithms for Scientific Computing (RASC)","abstract":"Randomized algorithms have propelled advances in artificial intelligence (AI) and represent a foundational research area in advancing AI for Science.Future advancements in DOE Office of Science priority areas such as climate science, astrophysics, fusion, advanced materials, combustion, and quantum computing all require randomized algorithms for surmounting challenges of complexity, robustness, and scalability.Advances in data collection and numerical simulation have changed the dynamics of scientific research and motivate the need for randomized algorithms.For instance, advances in imaging technologies such as X-ray ptychography, electron microscopy, electron energy loss spectroscopy, or adaptive optics lattice light-sheet microscopy collect hyperspectral imaging and scattering data in terabytes, at breakneck speed enabled by state-of-the-art detectors.The data collection is exceptionally fast compared with its analysis.Likewise, advances in high-performance architectures have made exascale computing a reality and changed the economies of scientific computing in the process.Floating-point operations that create data are essentially free in comparison with data movement.Thus far, most approaches have focused on creating faster hardware.Ironically, this faster hardware has exacerbated the problem by making data still easier to create.Under such an onslaught, scientists often resort to heuristic deterministic sampling schemes (e.g., low-precision arithmetic, sampling every nth element) and sacrifice potentially valuable accuracy.Dramatically better results can be achieved via randomized algorithms, reducing the data size as much as or more than naive deterministic subsampling can achieve, while retaining the high accuracy of computing on the full data set.By randomized algorithms we mean those algorithms that employ some form of randomness in internal algorithmic decisions to accelerate time to solution, increase scalability, or improve reliability.Examples include matrix sketching for solving large-scale least-squares problems (see Figure 1) and stochastic gradient descent for training machine learning models.We are not recommending heuristic methods but rather randomized algorithms that have certificates of correctness and probabilistic guarantees of optimality and near-optimality.Such approaches can be useful beyond acceleration, for example, in understanding how to avoid measure zero worst-case scenarios that plague methods such as QR matrix factorization. data sketchRecommendation 5: Outreach Outreach to a broader community to facilitate engagement outside the traditional computational science community, including experts in statistics, applied probability, signal processing, and emerging hardware.The output of this effort will be community-building workshops and research efforts with topically diverse teams that break new frontiers.Recommendation 6: Workflow standardization Standardization of workflow, including debugging and test frameworks for methods with only probabilistic guarantees, software frameworks that both integrate randomized algorithms and provide new primitives for sampling and sketching, and modular frameworks for incorporating the methods into large-scale codes and deploying to new architectures.The output here will be community best practices and reduced barriers to contributing to scientific advances.","journal":"Lawrence Berkeley National Laboratory","year":2021,"id":217344,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9504,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":810565,"name":"Tamara G. 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