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Randles, Amanda

Overview:

My research in biomedical simulation and high performance computing focuses on the development of new computational tools that we use to provide insight into the localization and development of human diseases ranging from atherosclerosis to cancer. 

Positions:

Assistant Professor of Biomedical Engineering

Biomedical Engineering
Pratt School of Engineering

Assistant Professor in the Department of Mechanical Engineering and Material Science

Mechanical Engineering and Materials Science
Pratt School of Engineering

Assistant Professor of Computer Science

Computer Science
Trinity College of Arts & Sciences

Member of the Duke Cancer Institute

Duke Cancer Institute
School of Medicine

Education:

Ph.D. 2013

Ph.D. — Harvard University

News:

Grants:

Toward coupled multiphysics models of hemodynamics on leadership systems

Administered By
Biomedical Engineering
AwardedBy
National Institutes of Health
Role
Principal Investigator
Start Date
September 22, 2014
End Date
August 31, 2019

Training in Medical Imaging

Administered By
Biomedical Engineering
AwardedBy
National Institutes of Health
Role
Mentor
Start Date
July 15, 2003
End Date
August 31, 2019

Using GPU-Accelerated Computational Fluid Dynamics to Study In-stent Restenosis

Administered By
Biomedical Engineering
AwardedBy
Oak Ridge Associated Universities
Role
Principal Investigator
Start Date
June 01, 2016
End Date
May 31, 2017

Awards:

Ralph E. Powe Junior Faculty Enhancement Award. Oak Ridge Associated Universities.

Type
National
Awarded By
Oak Ridge Associated Universities
Date
May 01, 2016

Best Paper, IEEE International Conference on Computational Science (ICCS) 2015. IEEE.

Type
Other
Awarded By
IEEE
Date
May 01, 2015

Gordon Bell Finalist. ACM.

Type
International
Awarded By
ACM
Date
January 01, 2015

Early Independence Award. NIH.

Type
National
Awarded By
NIH
Date
September 01, 2014

Lawrence Fellowship. Lawrence Livermore National Laboratory.

Type
National
Awarded By
Lawrence Livermore National Laboratory
Date
January 01, 2013

U.S. Delegate . Heidelberg Laureate Forum.

Type
International
Awarded By
Heidelberg Laureate Forum
Date
January 01, 2013

Anita Borg Memorial Scholarship. Google.

Type
National
Awarded By
Google
Date
January 01, 2012

George Michael Memorial High Performance Computing Fellowship. ACM/IEEE.

Type
National
Awarded By
ACM/IEEE
Date
January 01, 2012

U.S. Delegate . Lindau Nobel Laureates and Students Meeting Dedicated to Physics.

Type
International
Awarded By
Lindau Nobel Laureates and Students Meeting Dedicated to Physics
Date
January 01, 2012

Computational Science Graduate Fellowship. Department of Energy.

Type
National
Awarded By
Department of Energy
Date
January 01, 2010

George Michael Memorial High Performance Computing Fellowship. ACM/IEEE.

Type
National
Awarded By
ACM/IEEE
Date
January 01, 2010

Gordon Bell Finalist. ACM.

Type
International
Awarded By
ACM
Date
January 01, 2010

Graduate Research Fellowship. National Science Foundation.

Type
National
Awarded By
National Science Foundation
Date
January 01, 2009

Publications:

Massively parallel models of the human circulatory system

© 2015 ACM.The potential impact of blood flow simulations on the diagnosis and treatment of patients suffering from vascular disease is tremendous. Empowering models of the full arterial tree can provide insight into diseases such as arterial hypertension and enables the study of the influence of local factors on global hemodynamics. We present a new, highly scalable implementation of the lattice Boltzmann method which addresses key challenges such as multiscale coupling, limited memory capacity and bandwidth, and robust load balancing in complex geometries. We demonstrate the strong scaling of a three-dimensional, high-resolution simulation of hemodynamics in the systemic arterial tree on 1,572,864 cores of Blue Gene/Q. Faster calculation of flow in full arterial networks enables unprecedented risk stratification on a perpatient basis. In pursuit of this goal, we have introduced computational advances that significantly reduce time-to-solution for biofluidic simulations.

Authors
Randles, A; Draeger, EW; Oppelstrup, T; Krauss, L; Gunnels, JA
MLA Citation
Randles, A, Draeger, EW, Oppelstrup, T, Krauss, L, and Gunnels, JA. "Massively parallel models of the human circulatory system." November 15, 2015.
Source
scopus
Published In
International Conference for High Performance Computing, Networking, Storage and Analysis, SC
Volume
15-20-November-2015
Publish Date
2015
DOI
10.1145/2807591.2807676

Massively parallel simulations of hemodynamics in the primary large arteries of the human vasculature

Authors
Randles, A; Draeger, EW; Bailey, PE
MLA Citation
Randles, A, Draeger, EW, and Bailey, PE. "Massively parallel simulations of hemodynamics in the primary large arteries of the human vasculature." Journal of Computational Science 9 (July 2015): 70-75.
Source
crossref
Published In
Journal of Computational Science
Volume
9
Publish Date
2015
Start Page
70
End Page
75
DOI
10.1016/j.jocs.2015.04.003

Lenard-Balescu Calculations and Classical Molecular Dynamics Simulations of Electrical and Thermal Conductivities of Hydrogen Plasmas

Authors
Whitley, HD; Scullard, CR; Benedict, LX; Castor, JI; Randles, A; Glosli, JN; Richards, DF; Desjarlais, MP; Graziani, FR
MLA Citation
Whitley, HD, Scullard, CR, Benedict, LX, Castor, JI, Randles, A, Glosli, JN, Richards, DF, Desjarlais, MP, and Graziani, FR. "Lenard-Balescu Calculations and Classical Molecular Dynamics Simulations of Electrical and Thermal Conductivities of Hydrogen Plasmas." Contributions to Plasma Physics 55.2-3 (February 2015): 192-202.
Source
crossref
Published In
Contributions to Plasma Physics
Volume
55
Issue
2-3
Publish Date
2015
Start Page
192
End Page
202
DOI
10.1002/ctpp.201400066

Scaling Support Vector Machines on modern HPC platforms

Authors
You, Y; Fu, H; Song, SL; Randles, A; Kerbyson, D; Marquez, A; Yang, G; Hoisie, A
MLA Citation
You, Y, Fu, H, Song, SL, Randles, A, Kerbyson, D, Marquez, A, Yang, G, and Hoisie, A. "Scaling Support Vector Machines on modern HPC platforms." Journal of Parallel and Distributed Computing 76 (February 2015): 16-31.
Source
crossref
Published In
Journal of Parallel and Distributed Computing
Volume
76
Publish Date
2015
Start Page
16
End Page
31
DOI
10.1016/j.jpdc.2014.09.005

Parallel in time approximation of the lattice Boltzmann method for laminar flows

Authors
Randles, A; Kaxiras, E
MLA Citation
Randles, A, and Kaxiras, E. "Parallel in time approximation of the lattice Boltzmann method for laminar flows." Journal of Computational Physics 270 (August 2014): 577-586.
Source
crossref
Published In
Journal of Computational Physics
Volume
270
Publish Date
2014
Start Page
577
End Page
586
DOI
10.1016/j.jcp.2014.04.006

Inference of Tumor Evolution during Chemotherapy by Computational Modeling and In Situ Analysis of Genetic and Phenotypic Cellular Diversity

Authors
Almendro, V; Cheng, Y-K; Randles, A; Itzkovitz, S; Marusyk, A; Ametller, E; Gonzalez-Farre, X; Muñoz, M; Russnes, HG; Helland, Å; Rye, IH; Borresen-Dale, A-L; Maruyama, R; van Oudenaarden, A; Dowsett, M; Jones, RL; Reis-Filho, J; Gascon, P; Gönen, M; Michor, F; Polyak, K
MLA Citation
Almendro, V, Cheng, Y-K, Randles, A, Itzkovitz, S, Marusyk, A, Ametller, E, Gonzalez-Farre, X, Muñoz, M, Russnes, HG, Helland, Å, Rye, IH, Borresen-Dale, A-L, Maruyama, R, van Oudenaarden, A, Dowsett, M, Jones, RL, Reis-Filho, J, Gascon, P, Gönen, M, Michor, F, and Polyak, K. "Inference of Tumor Evolution during Chemotherapy by Computational Modeling and In Situ Analysis of Genetic and Phenotypic Cellular Diversity." Cell Reports 6.3 (February 2014): 514-527.
Source
crossref
Published In
Cell Reports
Volume
6
Issue
3
Publish Date
2014
Start Page
514
End Page
527
DOI
10.1016/j.celrep.2013.12.041

MIC-SVM: Designing a highly efficient support vector machine for advanced modern multi-core and many-core architectures

Support Vector Machine (SVM) has been widely used in data-mining and Big Data applications as modern commercial databases start to attach an increasing importance to the analytic capabilities. In recent years, SVM was adapted to the field of High Performance Computing for power/performance prediction, auto-tuning, and runtime scheduling. However, even at the risk of losing prediction accuracy due to insufficient runtime information, researchers can only afford to apply offline model training to avoid significant runtime training overhead. Advanced multi- and many-core architectures offer massive parallelism with complex memory hierarchies which can make runtime training possible, but form a barrier to efficient parallel SVM design. To address the challenges above, we designed and implemented MIC-SVM, a highly efficient parallel SVM for x86 based multi-core and many-core architectures, such as the Intel Ivy Bridge CPUs and Intel Xeon Phi co-processor (MIC). We propose various novel analysis methods and optimization techniques to fully utilize the multilevel parallelism provided by these architectures and serve as general optimization methods for other machine learning tools. MIC-SVM achieves 4.4-84x and 18-47x speedups against the popular LIBSVM, on MIC and Ivy Bridge CPUs respectively, for several real-world data-mining datasets. Even compared with GPUSVM, run on a top of the line NVIDIA k20x GPU, the performance of our MIC-SVM is competitive. We also conduct a cross-platform performance comparison analysis, focusing on Ivy Bridge CPUs, MIC and GPUs, and provide insights on how to select the most suitable advanced architectures for specific algorithms and input data patterns. © 2014 IEEE.

Authors
You, Y; Song, SL; Fu, H; Marquez, A; Dehnavi, MM; Barker, K; Cameron, KW; Randles, AP; Yang, G
MLA Citation
You, Y, Song, SL, Fu, H, Marquez, A, Dehnavi, MM, Barker, K, Cameron, KW, Randles, AP, and Yang, G. "MIC-SVM: Designing a highly efficient support vector machine for advanced modern multi-core and many-core architectures." 2014.
Source
scival
Publish Date
2014
Start Page
809
End Page
818
DOI
10.1109/IPDPS.2014.88

Analysis of pressure gradient across aortic stenosis with massively parallel computational simulations

Coarctation of the aorta (CoA) is one of the most common congenital heart defects in the United States, and despite treatment, patients have a decrease in life expectancy. Computational fluid dynamics simulations can provide the physician with a non-invasive method to measure the pressure gradient. With HARVEY, a massively parallel hemodynamics application, patient specific simulations can be conducted of large regions of the vasculature. The pressure across the stenosis is impacted by flow from nearby vessels. The purpose of this study was to study the impact of including these distal vessels in the simulation on the resulting pressure measurements. Computational fluid dynamic simulations were conducted in three subsets of one patient's vasculature. We demonstrate up to a 29% difference in calculated pressure gradient based on the number of vessels included in the simulation. These initial results are positive but need to be substantiated with further patient studies.

Authors
Randles, A; Draeger, E; Michor, F
MLA Citation
Randles, A, Draeger, E, and Michor, F. "Analysis of pressure gradient across aortic stenosis with massively parallel computational simulations." Computing in Cardiology 41.January (2014): 217-220.
Source
scival
Published In
Computing in cardiology
Volume
41
Issue
January
Publish Date
2014
Start Page
217
End Page
220

Scaling Support Vector Machines on modern HPC platforms

© 2014 Elsevier Inc.Support Vector Machines (SVM) have been widely used in data-mining and Big Data applications as modern commercial databases start to attach an increasing importance to the analytic capabilities. In recent years, SVM was adapted to the field of High Performance Computing for power/performance prediction, auto-tuning, and runtime scheduling. However, even at the risk of losing prediction accuracy due to insufficient runtime information, researchers can only afford to apply offline model training to avoid significant runtime training overhead. Advanced multi- and many-core architectures offer massive parallelism with complex memory hierarchies which can make runtime training possible, but form a barrier to efficient parallel SVM design.To address the challenges above, we designed and implemented MIC-SVM, a highly efficient parallel SVM for x86 based multi-core and many-core architectures, such as the Intel Ivy Bridge CPUs and Intel Xeon Phi co-processor (MIC). We propose various novel analysis methods and optimization techniques to fully utilize the multilevel parallelism provided by these architectures and serve as general optimization methods for other machine learning tools.MIC-SVM achieves 4.4-84× and 18-47× speedups against the popular LIBSVM, on MIC and Ivy Bridge CPUs respectively, for several real-world data-mining datasets. Even compared with GPUSVM, running on the NVIDIA k20x GPU, the performance of our MIC-SVM is competitive. We also conduct a cross-platform performance comparison analysis, focusing on Ivy Bridge CPUs, MIC and GPUs, and provide insights on how to select the most suitable advanced architectures for specific algorithms and input data patterns.

Authors
You, Y; Fu, H; Song, SL; Randles, A; Kerbyson, D; Marquez, A; Yang, G; Hoisie, A
MLA Citation
You, Y, Fu, H, Song, SL, Randles, A, Kerbyson, D, Marquez, A, Yang, G, and Hoisie, A. "Scaling Support Vector Machines on modern HPC platforms." Journal of Parallel and Distributed Computing (2014).
Source
scival
Published In
Journal of Parallel and Distributed Computing
Publish Date
2014
DOI
10.1016/j.jpdc.2014.09.005

Multiphysics simulations: Challenges and opportunities

Authors
Keyes, DE; McInnes, LC; Woodward, C; Gropp, W; Myra, E; Pernice, M; Bell, J; Brown, J; Clo, A; Connors, J; Constantinescu, E; Estep, D; Evans, K; Farhat, C; Hakim, A; Hammond, G; Hansen, G; Hill, J; Isaac, T; Jiao, X; Jordan, K; Kaushik, D; Kaxiras, E; Koniges, A; Lee, K; Lott, A; Lu, Q; Magerlein, J; Maxwell, R; McCourt, M; Mehl, M; Pawlowski, R; Randles, AP; Reynolds, D; Riviere, B; Rude, U; Scheibe, T; Shadid, J; Sheehan, B; Shephard, M; Siegel, A; Smith, B; Tang, X; Wilson, C; Wohlmuth, B
MLA Citation
Keyes, DE, McInnes, LC, Woodward, C, Gropp, W, Myra, E, Pernice, M, Bell, J, Brown, J, Clo, A, Connors, J, Constantinescu, E, Estep, D, Evans, K, Farhat, C, Hakim, A, Hammond, G, Hansen, G, Hill, J, Isaac, T, Jiao, X, Jordan, K, Kaushik, D, Kaxiras, E, Koniges, A, Lee, K, Lott, A, Lu, Q, Magerlein, J, Maxwell, R, McCourt, M, Mehl, M, Pawlowski, R, Randles, AP, Reynolds, D, Riviere, B, Rude, U, Scheibe, T, Shadid, J, Sheehan, B, Shephard, M, Siegel, A, Smith, B, Tang, X, Wilson, C, and Wohlmuth, B. "Multiphysics simulations: Challenges and opportunities." International Journal of High Performance Computing Applications 27.1 (February 1, 2013): 4-83.
Source
crossref
Published In
International Journal of High Performance Computing Applications
Volume
27
Issue
1
Publish Date
2013
Start Page
4
End Page
83
DOI
10.1177/1094342012468181

Performance analysis of the lattice Boltzmann model beyond Navier-Stokes

The lattice Boltzmann method is increasingly important in facilitating large-scale fluid dynamics simulations. To date, these simulations have been built on discretized velocity models of up to 27 neighbors. Recent work has shown that higher order approximations of the continuum Boltzmann equation enable not only recovery of the Navier-Stokes hydro-dynamics, but also simulations for a wider range of Knudsen numbers, which is especially important in micro- and nano-scale flows. These higher-order models have significant impact on both the communication and computational complexity of the application. We present a performance study of the higher-order models as compared to the traditional ones, on both the IBM Blue Gene/P and Blue Gene/Q architectures. We study the tradeoffs of many optimizations methods such as the use of deep halo level ghost cells that, alongside hybrid programming models, reduce the impact of extended models and enable efficient modeling of extreme regimes of computational fluid dynamics. © 2013 IEEE.

Authors
Randles, AP; Kale, V; Hammond, J; Gropp, W; Kaxiras, E
MLA Citation
Randles, AP, Kale, V, Hammond, J, Gropp, W, and Kaxiras, E. "Performance analysis of the lattice Boltzmann model beyond Navier-Stokes." 2013.
Source
scival
Published In
Proceedings - IEEE 27th International Parallel and Distributed Processing Symposium, IPDPS 2013
Publish Date
2013
Start Page
1063
End Page
1074
DOI
10.1109/IPDPS.2013.109

Massively parallel model of extended memory use in evolutionary game dynamics

To study the emergence of cooperative behavior, we have developed a scalable parallel framework for evolutionary game dynamics. This is a critical computational tool enabling large-scale agent simulation research. An important aspect is the amount of history, or memory steps, that each agent can keep. When six memory steps are taken into account, the strategy space spans 2 4096 potential strategies, requiring large populations of agents. We introduce a multi-level decomposition method that allows us to exploit both multi-node and thread-level parallel scaling while minimizing communication overhead. We present the results of a production run modeling up to six memory steps for populations consisting of up to 1018 agents, making this study one of the largest yet undertaken. The high rate of mutation within the population results in a non-trivial parallel implementation. The strong and weak scaling studies provide insight into parallel scalability and programmability trade-offs for large-scale simulations, while exhibiting near perfect weak and strong scaling on 16,384 tasks on Blue Gene/Q. We further show 99% weak scaling up to 294,912 processors 82% strong scaling efficiency up to 262,144 processors of Blue Gene/P. Our framework marks an important step in the study of game dynamics with potential applications in fields ranging from biology to economics and sociology. © 2013 IEEE.

Authors
Randles, AP; Rand, DG; Lee, C; Morrisett, G; Sircar, J; Nowak, MA; Pfister, H
MLA Citation
Randles, AP, Rand, DG, Lee, C, Morrisett, G, Sircar, J, Nowak, MA, and Pfister, H. "Massively parallel model of extended memory use in evolutionary game dynamics." 2013.
Source
scival
Published In
Proceedings - IEEE 27th International Parallel and Distributed Processing Symposium, IPDPS 2013
Publish Date
2013
Start Page
1217
End Page
1228
DOI
10.1109/IPDPS.2013.102

A Lattice Boltzmann Simulation of Hemodynamics in a Patient-Specific Aortic Coarctation Model

Authors
Randles, A; Baecher, M; Pfister, H; Kaxiras, E
MLA Citation
Randles, A, Baecher, M, Pfister, H, and Kaxiras, E. "A Lattice Boltzmann Simulation of Hemodynamics in a Patient-Specific Aortic Coarctation Model." Ed. O Camara, T Manso, M Pop, K Rhode, M Sermesant, and A Young. Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges: Third International Workshop, STACOM 2012, Held in Conjunction with MICCAI 2012, Nice, France, October 5, 2012, Revised Selected Papers (2013): 17-25.
Source
manual
Published In
Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges: Third International Workshop, STACOM 2012, Held in Conjunction with MICCAI 2012, Nice, France, October 5, 2012, Revised Selected Papers
Publish Date
2013
Start Page
17
End Page
25
DOI
10.1007/978-3-642-36961-2_3

Massively parallel model of evolutionary game dynamics

To study the emergence of cooperative behavior, we have developed a scalable parallel framework. An important aspect is the amount of history that each agent can keep. When six memory steps are taken into account, the strategy space spans 24096 potential strategies, requiring large populations of agents. We introduce a multi-level decomposition method that allows us to exploit both multi-node and thread-level parallel scaling while minimizing the communication overhead. We present the following contributions: (1) A production run modeling up to six memory steps for populations consisting of up to 1018 agents, making this study one of the largest yet undertaken. (2) Results exhibiting near perfect weak scaling and 82% strong scaling efficiency up to 262,144 processors of the IBM Blue Gene/P supercomputer and 16,384 processors of the Blue Gene/Q. Our framework marks an important step in the study of game dynamics with potential applications in fields ranging from biology to economics and sociology. © 2012 IEEE.

Authors
Randles, AP
MLA Citation
Randles, AP. "Massively parallel model of evolutionary game dynamics." Proceedings - 2012 SC Companion: High Performance Computing, Networking Storage and Analysis, SCC 2012 (2012): 1531--.
Source
scival
Published In
Proceedings - 2012 SC Companion: High Performance Computing, Networking Storage and Analysis, SCC 2012
Publish Date
2012
Start Page
1531-
DOI
10.1109/SC.Companion.2012.307

Evaluation of Artery Visualizations for Heart Disease Diagnosis

Authors
Borkin, M; Gajos, K; Peters, A; Mitsouras, D; Melchionna, S; Rybicki, F; Feldman, C; Pfister, H
MLA Citation
Borkin, M, Gajos, K, Peters, A, Mitsouras, D, Melchionna, S, Rybicki, F, Feldman, C, and Pfister, H. "Evaluation of Artery Visualizations for Heart Disease Diagnosis." IEEE Transactions on Visualization and Computer Graphics 17.12 (December 2011): 2479-2488.
Source
crossref
Published In
IEEE Transactions on Visualization and Computer Graphics
Volume
17
Issue
12
Publish Date
2011
Start Page
2479
End Page
2488
DOI
10.1109/TVCG.2011.192

Efficient Resource Allocation for Broadcasting Multi-Slot Messages With Random Access with Capture

Authors
Randles, A; Zeger, L
MLA Citation
Randles, A, and Zeger, L. "Efficient Resource Allocation for Broadcasting Multi-Slot Messages With Random Access with Capture." IEEE Military Communications Conference. November 7, 2011 - November 10, 2011. Baltimore, MD. IEEE, October 2011.
Source
manual
Publish Date
2011

Drug discovery using very large numbers of patents. General strategy with extensive use of match and edit operations

Authors
Robson, B; Li, J; Dettinger, R; Peters, A; Boyer, SK
MLA Citation
Robson, B, Li, J, Dettinger, R, Peters, A, and Boyer, SK. "Drug discovery using very large numbers of patents. General strategy with extensive use of match and edit operations." Journal of Computer-Aided Molecular Design 25.5 (May 2011): 427-441.
Source
crossref
Published In
Journal of Computer-Aided Molecular Design
Volume
25
Issue
5
Publish Date
2011
Start Page
427
End Page
441
DOI
10.1007/s10822-011-9429-x

Multiscale simulation of cardiovascular flows on the IBM Bluegene/P: full heart-circulation system at red-blood cell resolution

Authors
Randles, A; Melchionna, S; Kaxiras, E; Latt, J; Sircar, J; Bernaschi, M; Bisson, M; Succi, S
MLA Citation
Randles, A, Melchionna, S, Kaxiras, E, Latt, J, Sircar, J, Bernaschi, M, Bisson, M, and Succi, S. "Multiscale simulation of cardiovascular flows on the IBM Bluegene/P: full heart-circulation system at red-blood cell resolution." ACM/IEEE International Conference for High Performance Computing, Networking, Storage, and Analysis. November 13, 2010 - November 19, 2010. New Orleans, LA. ACM IEEE, November 2010.
Source
manual
Publish Date
2010

An Efficient Parallel Implementation of the Hidden Markov Methods for Genomic Sequence-Search on a Massively Parallel System

Authors
Jiang, K; Thorsen, O; Peters, A; Smith, B; Sosa, CP
MLA Citation
Jiang, K, Thorsen, O, Peters, A, Smith, B, and Sosa, CP. "An Efficient Parallel Implementation of the Hidden Markov Methods for Genomic Sequence-Search on a Massively Parallel System." IEEE Transactions on Parallel and Distributed Systems 19.1 (January 2008): 15-23.
Source
crossref
Published In
IEEE Transactions on Parallel and Distributed Systems
Volume
19
Issue
1
Publish Date
2008
Start Page
15
End Page
23
DOI
10.1109/TPDS.2007.70712

EUDOC on the IBM Blue Gene/L system: Accelerating the transfer of drug discoveries from laboratory to patient

Authors
Pang, Y-P; Mullins, TJ; Swartz, BA; McAllister, JS; Smith, BE; Archer, CJ; Musselman, RG; Peters, AE; Wallenfelt, BP; Pinnow, KW
MLA Citation
Pang, Y-P, Mullins, TJ, Swartz, BA, McAllister, JS, Smith, BE, Archer, CJ, Musselman, RG, Peters, AE, Wallenfelt, BP, and Pinnow, KW. "EUDOC on the IBM Blue Gene/L system: Accelerating the transfer of drug discoveries from laboratory to patient." IBM Journal of Research and Development 52.1.2 (January 2008): 69-81.
Source
crossref
Published In
IBM Journal of Research and Development
Volume
52
Issue
1.2
Publish Date
2008
Start Page
69
End Page
81
DOI
10.1147/rd.521.0069

A Spatio-Temporal Coupling Method to Reduce the Time-to-Solution of Cardiovascular Simulations

We present a new parallel-in-time method designed to reduce the overall time-to- solution of a patientspecific cardiovascular flow simulation. Using a modified parareal algorithm, our approach extends strong scalability beyond spatial parallelism with fully controllable accuracy and no decrease in stability. We discuss the coupling of spatial and temporal domain decompositions used in our implementation, and showcase the use of the method on a study of blood flow through the aorta. We observe an additional 40% reduction in overall wall clock time with no significant loss of accuracy, in agreement with a predictive performance model.

Authors
Randles, A; Kaxiras, E
MLA Citation
Randles, A, and Kaxiras, E. "A Spatio-Temporal Coupling Method to Reduce the Time-to-Solution of Cardiovascular Simulations." IEEE International Parallel & Distributed Processing Symposium. Miami, FL. IEEE, 2008.
Source
manual
Publish Date
2008

Parallel Genomic Sequence-Search on a Massively Parallel System

Authors
Randles, A
MLA Citation
Randles, A. "Parallel Genomic Sequence-Search on a Massively Parallel System." ACM International Conference on Computing Frontiers. May 7, 2007 - May 9, 2007. Ischia, Italy. ACM, May 2007.
Source
manual
Publish Date
2007

A Feasibility Study using Image-based Parallel Modeling for Treatment Planning

Authors
Randles, A; Driscoll, M; Draeger, EW; Michor, F
MLA Citation
Randles, A, Driscoll, M, Draeger, EW, and Michor, F. "A Feasibility Study using Image-based Parallel Modeling for Treatment Planning." Computing in Cardiology. September 7, 2014 - September 10, 2014. Cambridge, MA.
Source
manual

Does the degree of coarctation of the aorta influence wall shear stress focal heterogeneity?

The development of atherosclerosis in the aorta is associated with low and oscillatory wall shear stress for normal patients. Moreover, localized differences in wall shear stress heterogeneity have been correlated with the presence of complex plaques in the descending aorta. While it is known that coarctation of the aorta can influence indices of wall shear stress, it is unclear how the degree of narrowing influences resulting patterns. We hypothesized that the degree of coarctation would have a strong influence on focal heterogeneity of wall shear stress. To test this hypothesis, we modeled the fluid dynamics in a patient-specific aorta with varied degrees of coarctation. We first validated a massively parallel computational model against experimental results for the patient geometry and then evaluated local shear stress patterns for a range of degrees of coarctation. Wall shear stress patterns at two cross sectional slices prone to develop atherosclerotic plaques were evaluated. Levels at different focal regions were compared to the conventional measure of average circumferential shear stress to enable localized quantification of coarctation-induced shear stress alteration. We find that the coarctation degree causes highly heterogeneous changes in wall shear stress.

Authors
Gounley, J; Chaudhury, R; Vardhan, M; Driscoll, M; Pathangey, G; Winarta, K; Ryan, J; Frakes, D; Randles, A
MLA Citation
Gounley, J, Chaudhury, R, Vardhan, M, Driscoll, M, Pathangey, G, Winarta, K, Ryan, J, Frakes, D, and Randles, A. "Does the degree of coarctation of the aorta influence wall shear stress focal heterogeneity?." 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. August 16, 2016 - August 20, 2016. Orlando, FL. IEEE.
Website
http://hdl.handle.net/10161/12929
Source
manual
Issue
2015
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Research Areas:

  • Aortic Coarctation
  • Atherosclerosis
  • Biomechanics
  • Biophysics
  • Cancer
  • Cancer cells
  • Cardiovascular Diseases
  • Computational Biology
  • Computational fluid dynamics
  • Computer Simulation
  • Fluid mechanics
  • Hemodynamics
  • High performance computing
  • Lattice Boltzmann methods
  • Metastasis
  • Multiscale modeling
  • Parallel algorithms
  • Parallel computers