Optimizing High-Performance Computing Systems for Biomedical Workloads

Kovatch, Patricia; Gai, Lili; Cho, Hyung Min; Fluder, Eugene; Jiang, Dansha

doi:10.1109/ipdpsw50202.2020.00040

Cited by 7 publications

(3 citation statements)

References 20 publications

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“…This is partly due to the factors discussed in §3.4. Other barriers to quantum advantages include (i) the sophistication of existing classical heuristic algorithms and the inherent parallelism of many of the problems they solve, (ii) the scale of both existing classical hardware and practical problem instances within the context of contemporary research [ 385 ], (iii) the broad institutional support and incumbent advantage benefiting existing classical approaches (including extensive clinical validation in the medical setting), and (iv) the likely precondition of FTQC to realize polynomial advantages based on amplitude amplification in practice [ 41 ]. Thus, while current research in this direction shows long-term promise and should be explored further, many of these quantum advantages appear unlikely to be practical in the near term.…”

Section: Future Prospects In Biology and Medicinementioning

confidence: 99%

Biology and medicine in the landscape of quantum advantages

Cordier

Sawaya

Guerreschi

et al. 2022

J. R. Soc. Interface.

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Quantum computing holds substantial potential for applications in biology and medicine, spanning from the simulation of biomolecules to machine learning methods for subtyping cancers on the basis of clinical features. This potential is encapsulated by the concept of a quantum advantage, which is contingent on a reduction in the consumption of a computational resource, such as time, space or data. Here, we distill the concept of a quantum advantage into a simple framework to aid researchers in biology and medicine pursuing the development of quantum applications. We then apply this framework to a wide variety of computational problems relevant to these domains in an effort to (i) assess the potential of practical advantages in specific application areas and (ii) identify gaps that may be addressed with novel quantum approaches. In doing so, we provide an extensive survey of the intersection of biology and medicine with the current landscape of quantum algorithms and their potential advantages. While we endeavour to identify specific computational problems that may admit practical advantages throughout this work, the rapid pace of change in the fields of quantum computing, classical algorithms and biological research implies that this intersection will remain highly dynamic for the foreseeable future.

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Section: Future Prospects In Biology and Medicinementioning

confidence: 99%

Biology and medicine in the landscape of quantum advantages

Cordier

Sawaya

Guerreschi

et al. 2022

J. R. Soc. Interface.

View full text Add to dashboard Cite

show abstract

“…The intensive workloads of AI operating on big data demand computational resources that must be able to achieve extreme scale and high performance while being cost-effective and environmentally sustainable [39]. High performance computing (HPC), or supercomputing, architectures are facilitating the deployment of pioneering AI applications in biomedicine [40,41]. In this view, HPC represents a critical capacity to gain competitive advantages, including not only faster and more complex computation schemes but also at lower costs and higher impact.…”

Section: The Role Of Ai In Cancer Researchmentioning

confidence: 99%

Artificial intelligence in cancer research: learning at different levels of data granularity

2021

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From genome‐scale experimental studies to imaging data, behavioral footprints, and longitudinal healthcare records, the convergence of big data in cancer research and the advances in Artificial Intelligence (AI) is paving the way to develop a systems view of cancer. Nevertheless, this biomedical area is largely characterized by the co‐existence of big data and small data resources, highlighting the need for a deeper investigation about the crosstalk between different levels of data granularity, including varied sample sizes, labels, data types, and other data descriptors. This review introduces the current challenges, limitations, and solutions of AI in the heterogeneous landscape of data granularity in cancer research. Such a variety of cancer molecular and clinical data calls for advancing the interoperability among AI approaches, with particular emphasis on the synergy between discriminative and generative models that we discuss in this work with several examples of techniques and applications.

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“…This is partly due to the factors discussed in Section 2.4. Other barriers to quantum advantages include i) the sophistication of existing classical heuristic algorithms and the inherent parallelism of many of the problems they solve, ii) the scale of both existing classical hardware and practical problem instances within the context of contemporary research [410], iii) the broad institutional support and incumbent advantage benefiting existing classical approaches (including extensive clinical validation in the medical setting), and iv) the likely precondition of FTQC to realize polynomial advantages based on amplitude amplification in practice [59]. Thus, while current research in this direction shows long term promise and should be explored further, many of these quantum advantages appear unlikely to be practical the near term.…”

Section: Prospects For Bioinformaticsmentioning

confidence: 99%

Biology and medicine in the landscape of quantum advantages

Cordier¹,

Sawaya²,

Guerreschi³

et al. 2021

Preprint

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Quantum computing holds significant potential for applications in biology and medicine, spanning from the simulation of biomolecules to machine learning approaches for subtyping cancers on the basis of clinical features. This potential is encapsulated by the concept of a quantum advantage, which is typically contingent on a reduction in the consumption of a computational resource, such as time, space, or data. Here, we distill the concept of a quantum advantage into a simple framework that we hope will aid researchers in biology and medicine pursuing the development of quantum applications. We then apply this framework to a wide variety of computational problems relevant to these domains in an effort to i) assess the potential of quantum advantages in specific application areas and ii) identify gaps that may be addressed with novel quantum approaches. Bearing in mind the rapid pace of change in the fields of quantum computing and classical algorithms, we aim to provide an extensive survey of applications in biology and medicine that may lead to practical quantum advantages.

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Optimizing High-Performance Computing Systems for Biomedical Workloads

Cited by 7 publications

References 20 publications

Biology and medicine in the landscape of quantum advantages

Biology and medicine in the landscape of quantum advantages

Artificial intelligence in cancer research: learning at different levels of data granularity

Biology and medicine in the landscape of quantum advantages

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