FLiT: Cross-platform floating-point result-consistency tester and workload

Sawaya, Geof; Bentley, Michael; Briggs, Ian; Gopalakrishnan, Ganesh; Ahn, Dong H.

doi:10.1109/iiswc.2017.8167780

Cited by 17 publications

(10 citation statements)

References 8 publications

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“…While Alpine comes with a minimal set of libraries, a core difference between these systems as noted by DistroWatch (https://distrowatch.com/) is their dependence on a different version of the Linux kernel. While numerical differences between operating systems are likely the result of compilers (Sawaya et al, 2017) and installed libraries, the purpose of testing across operating systems explicitly rather than combinations of specific tools is to recreate a real-world setting in which typical scientific users observe numerical differences across equivalent high-level pipelines.…”

Section: Operating System Variationmentioning

confidence: 99%

Comparing perturbation models for evaluating stability of neuroimaging pipelines

Kiar

Castro

Rioux

et al. 2020

The International Journal of High Performance Computing Applica

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With an increase in awareness regarding a troubling lack of reproducibility in analytical software tools, the degree of validity in scientific derivatives and their downstream results has become unclear. The nature of reproducibility issues may vary across domains, tools, data sets, and computational infrastructures, but numerical instabilities are thought to be a core contributor. In neuroimaging, unexpected deviations have been observed when varying operating systems, software implementations, or adding negligible quantities of noise. In the field of numerical analysis, these issues have recently been explored through Monte Carlo Arithmetic, a method involving the instrumentation of floating-point operations with probabilistic noise injections at a target precision. Exploring multiple simulations in this context allows the characterization of the result space for a given tool or operation. In this article, we compare various perturbation models to introduce instabilities within a typical neuroimaging pipeline, including (i) targeted noise, (ii) Monte Carlo Arithmetic, and (iii) operating system variation, to identify the significance and quality of their impact on the resulting derivatives. We demonstrate that even low-order models in neuroimaging such as the structural connectome estimation pipeline evaluated here are sensitive to numerical instabilities, suggesting that stability is a relevant axis upon which tools are compared, alongside more traditional criteria such as biological feasibility, computational efficiency, or, when possible, accuracy. Heterogeneity was observed across participants which clearly illustrates a strong interaction between the tool and data set being processed, requiring that the stability of a given tool be evaluated with respect to a given cohort. We identify use cases for each perturbation method tested, including quality assurance, pipeline error detection, and local sensitivity analysis, and make recommendations for the evaluation of stability in a practical and analytically focused setting. Identifying how these relationships and recommendations scale to higher order computational tools, distinct data sets, and their implication on biological feasibility remain exciting avenues for future work.

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Section: Operating System Variationmentioning

confidence: 99%

Comparing perturbation models for evaluating stability of neuroimaging pipelines

Kiar

Castro

Rioux

et al. 2020

The International Journal of High Performance Computing Applica

View full text Add to dashboard Cite

show abstract

“…Incremental increases in complexity may be unavoidable for scientific codebases, we encourage computational scientists to familiarize themselves with modern tools that can increase their awareness of code-generation effects that may impede reproducibility. For example, the FLiT tool [35,36] allows users to assess the effects of various combinations of compiler options on their codes numerical properties, while tools like Spack [25] ease the burden of maintaining and organizing multiple versions of complex scientific software built against multiple toolchains.…”

Section: Recommendation 5-1: Broadening Notions Of Uncertainty Quantimentioning

confidence: 99%

Building a Vision for Reproducibility in the Cyberinfrastructure Ecosystem: Leveraging Community Efforts

Chapp

Stodden

Taufer

2020

JSFI

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The scientific computing community has long taken a leadership role in understanding and assessing the relationship of reproducibility to cyberinfrastructure, ensuring that computational results-such as those from simulations-are "reproducible", that is, the same results are obtained when one re-uses the same input data, methods, software and analysis conditions. Starting almost a decade ago, the community has regularly published and advocated for advances in this area. In this article we trace this thinking and relate it to current national efforts, including the 2019 National Academies of Science, Engineering, and Medicine report on "Reproducibility and Replication in Science". To this end, this work considers high performance computing workflows that emphasize workflows combining traditional simulations (e.g. Molecular Dynamics simulations) with in situ analytics. We leverage an analysis of such workflows to (a) contextualize the 2019 National Academies of Science, Engineering, and Medicine report's recommendations in the HPC setting and (b) envision a path forward in the tradition of community driven approaches to reproducibility and the acceleration of science and discovery. The work also articulates avenues for future research at the intersection of transparency, reproducibility, and computational infrastructure that supports scientific discovery.

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“…22 Unfortunately, AmpB has significant nephrotoxicity coupled with poor solubility, relegating it to a drug of last-resort for systemic fungal infections. 23,24 Originally, AmpB was believed to kill fungi via assembly with sterols in the lipid membrane to create channels leading to ion and solute leakage and ultimately to cell death. 25 More recent work from the Burke laboratory suggests another potential mechanism where AmpB acts as a sterol sponge to extract ergosterol from fungal lipid membranes.…”

Section: Polyene Macrolidesmentioning

confidence: 99%

Fixing the Unfixable: The Art of Optimizing Natural Products for Human Medicine

Yñigez-Gutierrez

Bachmann

2019

J. Med. Chem.

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Molecules isolated from natural sources including bacteria, fungi, and plants are a long-standing source of therapeutics that continue to add to our medicinal arsenal today. Despite their potency and prominence in the clinic, complex natural products often exhibit a number of liabilities that hinder their development as therapeutics, which may be partially responsible for the current trend away from natural product discovery, research, and development. However, advances in synthetic biology and organic synthesis have inspired a new generation of natural product chemists to tackle powerful undeveloped scaffolds. In this Perspective, we will present case studies demonstrating the historical and current focus on making targeted, but significant, changes to natural product scaffolds via biosynthetic gene cluster manipulation, total synthesis, semisynthesis, or a combination of these methods, with a focus on increasing activity, decreasing toxicity, or improving chemical and pharmacological properties.

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FLiT: Cross-platform floating-point result-consistency tester and workload

Cited by 17 publications

References 8 publications

Comparing perturbation models for evaluating stability of neuroimaging pipelines

Comparing perturbation models for evaluating stability of neuroimaging pipelines

Building a Vision for Reproducibility in the Cyberinfrastructure Ecosystem: Leveraging Community Efforts

Fixing the Unfixable: The Art of Optimizing Natural Products for Human Medicine

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