A Medium-Scale Distributed System for Computer Science Research: Infrastructure for the Long Term

Bal, Henri E.; Epema, Dick; Laat, Cees de; Nieuwpoort, Rob V. van; Romein, John W.; Seinstra, F.J.; Snoek, Cees G. M.; Wijshoff, Harry A. G.

doi:10.1109/mc.2016.127

Cited by 125 publications

(95 citation statements)

References 11 publications

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“…For both scenarios, the mean neointimal area is below this restenosis threshold. Overall uncertainty is fairly high however, with a standard deviation of 0.07 mm 2 for Scenario 1, and 0.09 mm 2 for Scenario 2 (red dashed lines). There are two sources of this uncertainty: the three uncertain input parameters give rise to epistemic uncertainty, while the internal stochastic variables result in aleatory uncertainty.…”

Section: Uncertainty Propagationmentioning

confidence: 99%

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Uncertainty Quantification of a Multiscale Model for In-Stent Restenosis

Nikishova

Veen

Zun

et al. 2018

Cardiovasc Eng Tech

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Purpose-Coronary artery stenosis, or abnormal narrowing, is a widespread and potentially fatal cardiac disease. After treatment by balloon angioplasty and stenting, restenosis may occur inside the stent due to excessive neointima formation. Simulations of in-stent restenosis can provide new insight into this process. However, uncertainties due to variability in patient-specific parameters must be taken into account. Methods-We performed an uncertainty quantification (UQ) study on a complex two-dimensional in-stent restenosis model. We used a quasi-Monte Carlo method for UQ of the neointimal area, and the Sobol sensitivity analysis (SA) to estimate the proportions of aleatory and epistemic uncertainties and to determine the most important input parameters. Results-We observe approximately 30% uncertainty in the mean neointimal area as simulated by the model. Depending on whether a fast initial endothelium recovery occurs, the proportion of the model variance due to natural variability ranges from 15 to 35%. The endothelium regeneration time is identified as the most influential model parameter. Conclusion-The model output contains a moderate quantity of uncertainty, and the model precision can be increased by obtaining a more certain value on the endothelium regeneration time. We conclude that the quasi-Monte Carlo UQ and the Sobol SA are reliable methods for estimating uncertainties in the response of complicated multiscale cardiovascular models.

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Section: Uncertainty Propagationmentioning

confidence: 99%

“…2 Here, the mean value, variances and standard deviation. For the sensitivity indices and partial variances, instead of sample size N, we test the number M, which is equal to the number of estimations of the expressions inside the sums in Eqs.…”

mentioning

confidence: 99%

Uncertainty Quantification of a Multiscale Model for In-Stent Restenosis

Nikishova

Veen

Zun

et al. 2018

Cardiovasc Eng Tech

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“…In the distributed computing solution, the DAS 4 cluster [28] in TU Delft was used. The cluster has 32 nodes (only 23 are currently usable), and each node has 16 hyperthreaded cores at 2.4 GHz (512 hyper-threaded cores in total, only 368 can be currently used) and 24 GB RAM (768 GB in total, only 552 GB can be currently used).…”

Section: Tie-points Extractionmentioning

confidence: 99%

Improving FOSS photogrammetric workflows for processing large image datasets

Martinez-Rubi

Nex

Pierrot-Deseilligny

et al. 2017

Open geospatial data, softw. stand.

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Background: In the last decade Photogrammetry has shown to be a valid alternative to LiDAR techniques for the generation of dense point clouds in many applications. However, dealing with large image sets is computationally demanding. It requires high performance hardware and often long processing times that makes the photogrammetric point cloud generation not suitable for mapping purposes at regional and national scale. These limitations are partially overcome by commercial solutions, thanks to the use of expensive and dedicated hardware. Nonetheless, a Free and Open-Source Software (FOSS) photogrammetric solution able to cope with these limitations is still missing. Methods: In this paper, the bottlenecks of the basic components of photogrammetric workflows -tie-points extraction, bundle block adjustment (BBA) and dense image matching-are tackled implementing FOSS solutions. We present distributed computing algorithms for the tie-points extraction and for the dense image matching. Moreover, we present two algorithms for decreasing the memory needs of the BBA. The various algorithms are deployed on different hardware systems including a computer cluster. Results and conclusions:The usage of the algorithms presented allows to process large image sets reducing the computational time. This is demonstrated using two different datasets.

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“…We evaluate our learning-based portfolio scheduler through real world experiments (Chapter 6). Using the cloud-like experimental environment DAS-5 [7] and workloads derived from a real industrial workflow, we analyze ANANKE's user-level and system-level performance, and elasticity (metrics defined in Chapter 5). We also compare ANANKE with a baseline system and with a portfolio-scheduling-only approach.…”

Section: Main Contributionmentioning

confidence: 99%

“…Real-world infrastructure: We conduct real-world experiments with ANANKE on the DAS-5 multi-cluster system, configured as a cloud environment using the existing DAS-5 capabilities [7]. For our experiments, we use 1 cluster of the six available in DAS-5, and up to 50 homogeneous nodes of the 68 available nodes in this cluster.…”

Section: Environment Configurationmentioning

confidence: 99%