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Employing Graphic Programming Unit Technology to Accelerate Numerical Diffusion Solver in 2D Multi-scale, Multi-resolution Agent-based Brain Cancer Model

Authors:
Accelerate numerical diffusion solver of 2D multi-
scale and multi-resolution agent-based brain
cancer model by employing graphics processing
unit technology
[BIOCOMP]
Beini Jiang1
1Department of Mathematical
Sciences
Michigan Tech University
Houghton, MI, USA
beinij@mtu.edu
Allan Struthers1
1Department of Mathematical
Sciences
Michigan Tech University
Houghton, MI, USA
Le Zhang1*
1Department of Mathematical
Sciences
Michigan Tech University
Houghton, MI, USA
zhangle@mtu.edu
Michael E Berens2
2Cancer and Cell Biology Division
Translational Genomics Research
Institute, TGen
Phoenix, AZ, USA
Wen Zhang1
1Department of Mathematical
Sciences
Michigan Tech University
Houghton, MI, USA
Xiaobo Zhou3
3Center for Bioinformatics and
Department of Pathology
The Methodist Hospital
Research Institute & Weill Cornell
Medical College
Houston, Texas, USA
AbstractDiffusion model is increasingly employed to simulate
diffusion of biological compounds including nutrient, oxygen and
chemoattractants in the agent-based model (ABM). However, it
takes long compute time to employ conventional numerical
methods such as alternating direction implicit (ADI) method to
approximate the exact solution of the diffusion processed by
sequential computing algorithm. To overcome this limitation, our
study employs cutting-edge graphics processing unit (GPU)
technology to speed up the conventional sequential numerical
solver for diffusion and incorporates our proposed parallel
computing algorithms into our well developed 2D multi-scale and
multi-resolution agent-based brain cancer model to break
through the bottleneck of the ABM that it is hard to simulate the
large system restricted to the limited compute resource and
memory. Our simulation outputs demonstrate that ABM model
can be used to simulate real-time actual cancer progression with
relative fine grids by using GPU based parallel computing
algorithm.
Keywords: graphics processing unit; agent-based model;
alternating direction implicit method; domain decomposition;
parallel computing
I. INTRODUCTION
Agent-based model (ABM) has become a popular method to
describe the complex dynamic, adaptive and self-organizing
cancer system. For example, Mansury and Deisboeck [1, 2]
employed the ABM to simulate the expansion of brain tumor
in micro-macro environments. And Zhang et al. [3-6]
developed multi-scale ABMs to model the growth of glioma
and investigate incoherent relations of the tumor expansion
among macroscopic environment, microscopic environment
and molecular environments. A diffusion module is employed
to simulate the diffusion of the chemoattractants on the
macroscopic scale environment.
Though conventional finite difference numerical methods
such as ADI, Gauss–Seidel and Jocobi methods [7-9] for
diffusion module already have been used to simulate diffusion
of biological compounds such as nutrients, oxygen and
chemoattractants [3, 10-15] for years, they all depend on the
grid size so much that a relative fine grids can better mimic
the diffusion process but significantly increase the compute
time. Therefore, previous studies such as the work done by
Athale et al. [10, 11] and Wang et al.[16] have to employ
relatively coarse grids to reduce the compute time and the
work done by Dai et al.[17] and Zhang et al.[18-20] employed
special numerical scheme such as preconditioned Richardson
method [21, 22] to sacrifice the compute accuracy in some
dimensions of coordinates to reduce the compute resource
request due to the specific aim of these biomedical projects.
Nonetheless, our well developed 2D multi-scale and multi-
resolution ABM model needs such a fast diffusion module that
not only can accurately model the diffusion process but also
costs less compute resource. For this reason, using parallel
computing algorithm to speed up the conventional numerical
solver [23, 24] is the best promising solution. Quite a few
previous parallel computing algorithms employed Message
Passing Interface (MPI) [25], a parallel computing scheme
based on multiple instruction multiple data infrastructure, to
parallel the sequential numerical diffusion solver. However,
MPI is not only too expensive to be routinely used for light
computing project, but also its compute speed is limited by the
communication rate [26]. Since 2007, NVDIA keeps releasing
its graphics processing unit (GPU) and the novel Compute
Unified Device Architecture (CUDA) based on single
instruction multiple data infrastructure (SIMD). Until now,
GPU of NVDIA has been evolved into a highly parallel,
multithreaded, many core processor, with dramatic compute
ability and high memory bandwidth [27] , especially for the
recent Fermi GPU [28, 29]. Compared to MPI, GPU
computing is more affordable, portable and suitable for the
ABM simulation.
In general, the aim of this study is to incorporate the parallel
diffusion numerical solver based on latest released Fermi
GPU technology into our previous well developed multi-scale
and multi-resolution ABM model [5] to resolve its compute
capability shortage problem. The methods section introduces
the conventional numerical scheme, alternating direction
implicit (ADI) method [7, 30] and the GPU implementation
[31]. And then, we show that our parallel algorithms
significantly increase the performance when applied to the 2D
multi-scale and multi-resolution ABM [5].
II. METHODS
This section gives a brief introduction to ADI scheme [7]
with the standard domain decomposition strategy [32, 33]
followed by the description of GPU implementations.
A. Numerical diffusion solver:ADI Scheme
The diffusion of the chemical cues is described by (1.a),
where the D is the diffusivity for glucose (DG=6.7×10-7cm2s1)
and TGFα (DT=5.18×10-7 cm2 s1), respectively.

==
+
=+. (1.a)
The Crank–Nicolson method approximates (1.a) by (1.b)


∆ =
∆+
∆
+
. (1.b)
where 
is the numerical approximation of (,,) and
=,=,=. and denote the central
difference operators [7].
Introducing an intermediate level 
 
, the ADI method
modifies (1.b) into two separate difference equations with
implicit scheme, given by (2):

 

∆/ =
∆
 
+
∆
. (2.a)


 
∆/ =
∆
 
+
∆
. (2.b)
Writing =∆
∆ and =∆
∆ reduces (2) into the
Peaceman-Rachford ADI scheme [7], shown as (3)
,
 
+(1+)

,

=
,
+
1 − 
+
,
. (3.a)
,
 +1+

,
 =
,
 
+
(1−)
 
+
,
 
. (3.b)
The right part of both equations of (3) is explicit formula and
easily parallelized, while the left part is a symmetric and
tridiagonal system of equations = to be solved with the
Thomas algorithm [7, 34].
Equation (3) could be written into a general form as (4.a)
with =0 and=0 .
++=,=1,2,…,. (4.a)
The corresponding matrix form of this tridiagonal system is
represented by (4.b)
0⋯
⋯0
0
⋮⋱
⋱⋱
⋱0
0⋯⋱⋱
⋯0⋱

=
. (4.b)
B. Thomas Algorithm
The Thomas algorithm is employed to solve (4.a). It has
two major steps. First is computing coefficients (5.a) and
(5.b) known as forward sweep. Second is using backward
substitution to get solutions as (5.c).
=
; =1
−; =2,3,…,−1.
=
; =1
−
−; =2,3,…,.
(5.a)
(5.b)
=
=−; =−1,−2,,1. (5.c)
The details of the deduction of (5) are described in Morton’s
book [7].
C. Domain Decomposition
For the boundary value problem on a large domain, the
domain decomposition method decomposes the problem into
smaller independent boundary value problems on smaller
subdomains and then employ iterative method to resolve
differences between the solutions on adjacent subdomains [32,
33]. We develop such a GPU based parallel computing
algorithm with classical alternating Schwarz method [33, 35]
that can benefit from the advantages of GPU technology. Fig.
1(b) [31] exhibits the decomposition of a 10 by 10 array with
an 8 by 8 inner array (green) and four vectors of boundary
points (red) (Fig .1(a) [31]) into 4 overlapping 6 by 6 sub-
arrays, each of which consists of a 4 by 4 inner array (green)
and four artificial internal boundaries (red). Each sub-array is
iteratively solved to make the artificial boundaries converge [7,
32, 33, 35, 36]. Here, we use the data transfer between sub-
matrix 1 and sub-matrix 2 as an example. The values of the
four inner elements on the rightmost side in sub-matrix 1 are
sent to sub-matrix 2 as the new left artificial boundary as well
as the values of the four inner elements on the leftmost side in
sub-matrix 2 are sent to sub-matrix 1 as the new right artificial
boundary until both artificial boundaries converge.
(a)
(b)
Figure 1 [31] (a) A 10 by 10 solution matrix with red to indicate boundary
elements and blue to indicate inner elements and (b) Decomposition of a 10 by
10 array into 4 overlapping 6 by 6 sub-arrays with red to indicate boundary
elements, green to indicate inner elements and the arrows to show how to
update the boundary data.
D. Parallel Computing Algorithms to Speed up the diffusion
solver
The first step of ADI is to set up the explicit scheme, shown
as the right part of (3). Since each element could be computed
independently, the explicit scheme is easy to be parallelized
with single-instruction, multiple-thread (SIMT) infrastructure
of CUDA. The second step is to solve the implicit scheme of
ADI by Thomas algorithm. As we discussed in our previous
research [31], Thomas algorithm is the bottleneck to speed up
the conventional numerical diffusion solver.
CUDA programming has two major steps. The first is
preparing such data that can be paralleled in the host side
(CPU). The second is processing these data in the device side
(GPU) by kernel. CUDA organizes the threads into a two-level
hierarchy (Fig. 2-1 of NVIDIA CUDA Programming Guide
[27]). As shown by Fig. 2-2 of NVIDIA CUDA Programming
Guide [27], a thread executing on the device has access to the
device’s (GPU) DRAM and on-chip memory through 6
different memory spaces such as registers, local memory,
shared memory, global memory, constant memory, and texture
memory [27, 37-40]. As a very important memory of GPU,
global memory is in charge of exchanging the data between
the host (CPU) and the device (GPU). Moreover, it plays such
a role that passes the messages between the threads from
different blocks, since current GPU infrastructure prohibits the
communication of threads from different blocks [27, 29, 41].
However, as an off-chip memory, the latency of global
memory is very high. As on-chip memory, shared memory,
registers, and constant-memory caches are much faster with
much lower latency. Nonetheless, shared memory is very
limited and it is only allocated to each block. For example, the
capacity of the latest version of GPU (Fermi) is only 64KB
[28, 42]. Moreover, another on-chip memory, constant
memory, is disallowed to be written to during the computation
[27, 43] though it is cashed.
CUDA uses a new architecture called SIMT to mange
threads running different programs. The multiprocessor SIMT
unit creates, manages, schedules, and executes threads in
groups of 32 parallel threads we call warps [27]. We have
developed three parallel computing algorithms to accelerate
the numerical diffusion solver based on the new features of
GPU technology [31]. The first is parallel computing
algorithm with global memory (PGM), which employs only
global memory to carry out parallel computing. The second is
parallel computing algorithm with shared memory, global
memory and CPU synchronization [27, 29, 41, 44] (PSGMC)
and the third is parallel computing algorithm using shared
memory, global memory and GPU synchronization [29, 41, 45]
(PSGMG). PSGMC and PSGMG employ “tiles” strategy to
partition the data and take advantages of both global memory
and shared memory with the classical alternating Schwarz
domain decomposition method [7, 32, 33, 35, 36]. The details
of these three implementation methods are presented in our
recent publication [31]. Here, we incorporate our fastest
parallel diffusion solver into 2D multi-scale and multi-
resolution ABM [5] to speed up the computation of ABM.
III. RESULTS
Our source code is implemented by C [46, 47] and NVCC
[48] programming language and running on the recent Fermi
GPU card (GeForce GTX 480) [42, 49, 50] with CUDA
standard.
In the beginning, let us briefly show how to use parallel
computing algorithms [31] based on GPU technology to
accelerate the numerical diffusion solver as following.
First, we employ PGM to compute the diffusion on the
lattice with different number of grid points and compare the
computing time with the sequential computing. Fig. 2 shows
PGM computing time is not always faster than sequential
algorithm for the lattice with small point number but
dramatically faster than sequential algorithm for the lattice
with large point number [31].
Figure 2 [31]. Computing time of PGM and sequential computing by
logarithmic scale. The x axis represents the inner matrix size (number of inner
grid points) and y axis represents the computing time (logarithmic scale with
base 10) in millisecond. The blue bar represents the computing time of
sequential computing and the red bar represents the computing time of PGM.
Second, we compare the compute time between PSGMC
and PGM, when simulating the diffusion on a 4098 by 4098
lattice. Fig. 3 shows PSGMC improves the performance by 58%
compared with PGM [31].
Figure 3 [31]. Computing time of PSGMC and PGM by logarithmic scale.
The y axis represents the computing time (logarithmic scale with base 10) in
millisecond. The blue bar represents the computing time of PGM and the red
bar represents the optimal computing time of PSGMC. The number on each
bar indicates the multiple of acceleration to the sequential computing.
Third, we compare the performance of PSGMC and
PSGMG. Fig. 4 exhibits PSGMG improves the performance
by 11% compared with PSGMC, when processing the
diffusion on a 4098 by 4098 lattice [31].
Figure 4 [31]. Computing time of PSGMG and PSGMC by logarithmic
scale. The y axis represents the computing time (logarithmic scale with base
10) in millisecond. The blue bar represents the computing time of PSGMC
and the red bar represents the computing time of PSGMG. The number on
each bar indicates the multiple of acceleration to the sequential computing.
Next, we incorporate the fastest parallel computing method
(PSGMG) into the well developed multi-scale and multi-
resolution ABM model [5]. The multi-resolution model is
designed based on two different resolution lattices, namely
low-resolution lattice and high-resolution lattice. The low-
resolution lattice is set up with a grid size of about 62.5 ,
on each grid point of which, a 6 by 6 high-resolution lattice
with a grid size of approximately 10  is superimposed,
described by Fig. 5 [5]. To demonstrate the advantages of the
parallel computing algorithm, we scale up the lattice size of
the previous multi-scale and multi-resolution ABM model [5].
Current low-resolution lattice is changed from 100 by 100 to
683 by 683 and high-resolution lattice is upgraded from 600
by 600 to 4098 by 4098.
Figure 5 [5] Configuration of multi-resolution lattice.
The diffusion of the chemical cues is observed on the high-
resolution lattice, with a grid size of approximately 10,
namely both ∆ and ∆ in the ADI scheme (2) are equal to
10. ∆ is set to 1s to make max
,
1 regarding to
the maximum principle [7], thus the ADI scheme needs to be
computed 3600 times for each time step, which is equivalent
to 1h.
And then, Fig. 6 exhibits that parallel computing can
significantly increase the performance of the compute time
37.5 folders than sequential computing for multi-scale and
multi-resolution ABM model [5].
1
10
100
1000
10000
100000
Computing Time (log)
Size of Inner Matrix
Sequential Computing
PGM
29.5 46.6
1
10
100
1000
10000
Computing TIme (log)
PGM PSGMC
46.6 51.7
1
10
100
1000
Computing TIme (log)
PSGMC PSGMG
Figure 6. Computing time of parallel and sequential computing by
logarithmic scale. The y axis represents the computing time (logarithmic scale
with base 10) in millisecond. The blue bar represents the computing time of
sequential computing and the red bar represents the optimal computing time
of parallel computing. The number on the red bar indicates the multiple of
acceleration to the sequential computing.
IV.
CONCLUSIONS
This study demonstrates that it is possible to simulate the
real-time actual tumor progression in a 2D lattice with relative
fine grids by using GPU based parallel computing algorithms.
Our extension research will develop a GPU based parallel
ODE solver to speed up the molecular pathway module of our
well developed multi-scale and multi-resolution agent-based
model [5].
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Modeling diffusion processes, such as drug deliver, bio-heat transfer, and the concentration change of cytokine for computational biology research, requires intensive computing resources as one must employ sequential numerical algorithms to obtain accurate numerical solutions, especially for real-time in vivo 3D simulation. Thus, it is necessary to develop a new numerical algorithm compatible with state-of-the-art computing hardware. The purpose of this article is to integrate the graphics processing unit (GPU) technology with the locally-one-dimension (LOD) numerical method for solving partial differential equations, and to develop a novel 3D numerical parallel diffusion algorithm (GNPD) in cylindrical coordinates based on GPU technology, which can be used in the neuromuscular junction research. To demonstrate the effectiveness and efficiency of the obtained GNPD algorithm, we employed it to approximate the real diffusion of the neurotransmitter through a disk shaped volume. This disk shaped volume is the synaptic gap, connecting the neuron and the muscle cell in the neuromuscular junction. Furthermore, we compared the speed and accuracy of the GNPD with the conventional sequential diffusion algorithm. Results show that the GNPD can not only significantly accelerate the speed of the diffusion solver via GPU-based parallelism, but also greatly increase the accuracy by employing the stream function of latest FermiGPU cards. Therefore, the GNPD has a great potential to be employed in the design, testing, and implementation of health information systems in the near future.
Chapter
As noted in the last two chapters, many ODEs and systems of ODEs cannot be solved analytically. When we want solutions to such equations, we need to turn to numerical methods, which we now take up. We begin with a simple DE that can also be solved analytically by separation of variables1; i.e., $$ \frac{{dy}} {{dx}} = xy,y(1) = 1, $$ This will allow comparison of the approximate numerical solution with the exact analytic solution, which is y = exp[(x2− 1)/2].
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This second edition of a highly successful graduate text presents a complete introduction to partial differential equations and numerical analysis. Revised to include new sections on finite volume methods, modified equation analysis, and multigrid and conjugate gradient methods, the second edition brings the reader up-to-date with the latest theoretical and industrial developments. First Edition Hb (1995): 0-521-41855-0 First Edition Pb (1995): 0-521-42922-6
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It is of interest to research hyperthermia combined with radiation and cytotoxic drugs to enhance the killing of tumors. The crucial problem is to keep surrounding normal tissue below a temperature that will produce harm when heating the tumor tissue. In this study, we develop a numerical model for optimizing laser power in the irradiation of a 3-D triple-layered cylindrical skin structure. The method determines the required laser intensity in order to obtain prespecified temperatures at given locations on the skin surface after a prespecified laser exposure time.
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