[LA-SiGMA] LI-LA-SiGMA seminar Tommowor, May 24

[Dentcho Genov]

Dear All,

 

This is a kind reminder that our next LI-LA-SiGMA seminar will be tomorrow,
May 24 at 2.00pm (AG room: 234 Netken Hall, LA tech). Our speaker is Dr.
Zhang Le, Department of Mathematical Sciences, Michigan Tech University,
Houghton. 

 

Title: Using multi-scale and multi-resolution model with Graphics processing
Unit (GPU) to simulate real-time actual cancer progression.

 

Abstract:  Multi-scale agent based model (ABM) has been wildly employed to
simulate Glioma tumor cells' interaction and tumor progression. In this type
of ABM, computational scientists always use a quantitative molecular pathway
of the intracellular level to determine cell's phonotype switch of
extracellular level as well as simulate the diffusion of the
chemoattractants which are the input factors of the molecular pathway of the
tissue level. And then, we can investigate which molecules play important
roles to determine cells' phonotype switch and drive the whole Glioma tumor
expansion. However, most of the recent ABM models are too theoretic to
predict real-time actual cancer progression due to the following reasons.
First, it is hard to employ conventional ABM to simulate the large system
restricted to the limited compute resource and memory of the computer.
Second, we should employ relative fine grids and small time interval for ABM
to simulate real-time cancer progression, but it takes long time for the
conventional numerical diffusion solver to approximate the exact solution
with relative fine grids. For the first problem, we use novel
multi-resolution algorithm to relieve the heavy compute requests. The
multi-resolution algorithm divides the cancer cells into homogeneous group
and heterogeneous group. And then, it concentrates limited compute resource
onto the cells in the heterogeneous group. The results demonstrate that
multi-resolution ABM can have high predictive power with light compute
requests. For the second problem, our study employs cutting-edge graphics
processing unit (GPU) technology to speed up the conventional sequential
numerical solver for diffusion with relative fine grids. Our research output
shows that GPU based parallel compute algorithm can accelerate diffusion
processing time around 50 folders than sequential algorithm. Finally, we
plan to develop a parallel ordinary differential equation numerical solver
to speed up the molecular pathway computation for each cancer cells in the
heterogeneous group. In general, GPU based multi-scale and multi-resolution
model has great potentiality for us to model real-time actual cancer
progression in the 2D and 3D space which could provide more valuable
information for the clinical personnel to forecast Glioma tumor expansion.

 

The access grid locations are as follow: 

 

Tulane: AG room: 403 Stanley Thomas Hall

University of Louisiana at Lafayette: AG room: Abdalla Hall

Southern University:  AG room: 218 Moore Hall

UNO: AG room:  SC 2002

LSU: AG room:  Johnston 338

LATECH: AG room: 234 Netken Hall

 

Please, distribute this email to your students and colleagues.

 

Kind Regards,

Dentcho

 

----

Dentcho A. Genov, PhD

The LONI Institute Fellow

Assistant Professor of Physics & Electrical Engineering

Louisiana Tech University, Engineering Annex, Room 220

599 W Arizona Ave, Ruston LA 71272

 

Phone: (318) 257-4190, Fax: (318) 257-2777

Webpage: http://www.phys.latech.edu/~dgenov/

 

 

 

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