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Monte Carlo eXtreme (MCX) - CUDA Edition
- Author: Qianqian Fang <fangq at nmr.mgh.harvard.edu>
- License: GNU General Public License version 3 (GPLv3)
- Version: 0.9.7-2 (Dark Matter - alpha update 2)
- Date: 09/23/2014
Table of Content:
- 1. Introduction
- 2. Requirement and Installation
- 3. Running Simulations
- 4. Using JSON-formatted input files
- 5. Using JSON-formatted shape description files
- 6. Using MCXLAB in MATLAB and Octave
- 7. Using MCX Studio GUI
- 8. Interpreting the Output
- 9. Best practices guide
- 10. Reference
1. Introduction
Monte Carlo eXtreme (MCX) is a fast photon transport simulation software for 3D heterogeneous turbid media. By taking advantage of the massively parallel threads and extremely low memory latency in a modern graphics processing unit (GPU), this program is able to perform Monte Carlo (MC) simulations at a blazing speed, typically hundreds to a thousand times faster than a fully optimized CPU-based MC implementation.
The algorithm of this software is detailed in the Reference [1]. A short summary of the main features includes:
- . 3D heterogeneous media represented by voxelated array
- . a variety of source forms, including wide-field and pattern illuminations
- . boundary reflection support
- . time-resolved photon transport simulations
- . saving photon partial path lengths at the detectors
- . optimized random number generators
- . build-in flux/fluence normalization to output Green's functions
- . user adjustable voxel resolution
- . improved accuracy with atomic operations
- . cross-platform graphics user interface
- . native Matlab/Octave support for high usability
- . flexible JSON interface for future extensions
This software can be used on Windows, Linux and Mac OS. MCX is written in CUDA and can be used with NVIDIA hardware with the native NVIDIA drivers, or used with the open-source GPU Ocelot libraries for CPUs and AMD GPUs. An OpenCL implementation of MCX, i.e. MCX-CL, was announced on July, 2012. It supports NVIDIA/AMD/Intel hardware out-of-box. If your hardware does not support CUDA, please download MCXCL from the below URL:
https://github.com/fangq/mcxcl
2. Requirement and Installation
Please read this section carefully. The majority of failures using MCX were found related to incorrect installation of CUDA library and NVIDIA driver.
For MCX-CUDA, the requirements for using this software include
- . a CUDA capable NVIDIA graphics card
- . pre-installed CUDA toolkit [1]
- . pre-installed NVIDIA graphics driver
You must use a CUDA capable NVIDIA graphics card in order to use MCX. A list of CUDA capable cards can be found at [2]. The oldest graphics card that MCX supports is GeForce 8XXX series (circa 2006). Using the latest NVIDIA card is expected to produce the best speed (GTX 4xx is twice faster than 2xx, which is twice faster than 8800/9800). To use the "fermi" version of MCX, you must have a fermi (GTX 4xx) or newer (5xx/6xx/7xx series) graphics card. The fermi version of MCX supports atomic operations and photon detection within a single binary.
For simulations with large volumes, sufficient graphics memory is also required to perform the simulation. The minimum amount of graphics memory required for a MC simulation is Nx*Ny*Nz*Ng bytes for the input tissue data plus Nx*Ny*Nz*Ng*4 bytes for the output flux/fluence data - where Nx,Ny,Nz are the dimensions of the tissue volume, Ng is the number of concurrent time gates, 4 is the size of a single-precision floating-point number. MCX does not require double-precision support in your hardware.
In addition to the hardware support, it is critical to install your CUDA library and NVIDIA driver correctly before running MCX. You should always install the matching CUDA version to the binary package you have downloaded. For example, if you have downloaded the "cuda4" version of MCX, you should install CUDA 4.x on your computer; if your MCX package has "cuda5.5" in the file name, you should install CUDA 5.5. Always download and install the matching NVIDIA driver linked from the CUDA download page. Once installed, follow the below URL to verify the installation is correct:
To install MCX, you need to download the binary executable compiled for your computer architecture (32 or 64bit) and platform, extract the package and run the executable under the <mcx root>/bin directory. For Linux and MacOS users, you need to add the following lines to your shell initialization scripts. First, use "echo $SHELL" command to identify your shell type. For csh/tcsh, add the following lines to your ~/.cshrc file:
if ("uname -p" =~ "*_64" ) then
setenv LD_LIBRARY_PATH "/usr/local/cuda/lib64:$LD_LIBRARY_PATH"
else
setenv LD_LIBRARY_PATH "/usr/local/cuda/lib:$LD_LIBRARY_PATH"
endif
setenv PATH "/usr/local/cuda/bin:$PATH"
and for bash/sh users, add
if [[ "uname -p" =~ .*_64 ]]; then
export LD_LIBRARY_PATH="/usr/local/cuda/lib64:$LD_LIBRARY_PATH"
else
export LD_LIBRARY_PATH="/usr/local/cuda/lib:$LD_LIBRARY_PATH"
fi
export PATH="/usr/local/cuda/bin:$PATH"
to your ~/.bash_profile (for Ubuntu users, this file is ~/.bashrc).
If the path "/usr/local/cuda/lib*" does not exist on your system or the CUDA library is not installed under this directory, you should substitute the actual path under which libcudart.* presents.
3. Running Simulations
To run a simulation, the minimum input is a configuration (text) file, and a volume file (a binary file with each byte representing a medium index). Typing the name of the executable without any parameters, will print the help information and a list of supported parameters, such as the following:
###############################################################################
# Monte Carlo eXtreme (MCX) -- CUDA #
# Copyright (c) 2009-2013 Qianqian Fang <fangq at nmr.mgh.harvard.edu> #
# #
# Martinos Center for Biomedical Imaging, Massachusetts General Hospital #
###############################################################################
$MCX $Rev:: 308 $ Last Commit $Date:: 2013-09-19 00:25:32#$ by $Author:: fangq$
###############################################################################
usage: mcx <param1> <param2> ...
where possible parameters include (the first item in [] is the default value)
-i (--interactive) interactive mode
-s sessionid (--session) a string to label all output file names
-f config (--input) read config from a file
-n [0|int] (--photon) total photon number (exponential form accepted)
-t [2048|int] (--thread) total thread number
-T [64|int] (--blocksize) thread number per block
-A [0|int] (--autopilot) auto thread config:1 dedicated GPU;2 non-dedic.
-G [0|int] (--gpu) specify which GPU to use, list GPU by -L; 0 auto
-r [1|int] (--repeat) number of repetitions
-a [0|1] (--array) 1 for C array (row-major); 0 for Matlab array
-z [0|1] (--srcfrom0) 1 volume coord. origin [0 0 0]; 0 use [1 1 1]
-g [1|int] (--gategroup) number of time gates per run
-b [1|0] (--reflect) 1 to reflect photons at ext. boundary;0 to exit
-B [0|1] (--reflectin) 1 to reflect photons at int. boundary; 0 do not
-e [0.|float] (--minenergy) minimum energy level to terminate a photon
-R [0.|float] (--skipradius) 0: vanilla MCX, no atomic operations
>0: radius in which use shared-memory atomics
-1: use crop0/crop1 to determine atomic zone
-2: use atomics for the entire domain
-u [1.|float] (--unitinmm) defines the length unit for the grid edge
-U [1|0] (--normalize) 1 to normalize flux to unitary; 0 save raw
-d [1|0] (--savedet) 1 to save photon info at detectors; 0 not save
-M [0|1] (--dumpmask) 1 to dump detector volume masks; 0 do not save
-H [1000000] (--maxdetphoton)max number of detected photons
-S [1|0] (--save2pt) 1 to save the flux field; 0 do not save
-E [0|int] (--seed) set random-number-generator seed, -1 to generate
-k [1|0] (--voidtime) when src is outside, 1 enables timer inside void
-h (--help) print this message
-l (--log) print messages to a log file instead
-L (--listgpu) print GPU information only
-I (--printgpu) print GPU information and run program
-P '{...}' (--shapes) a JSON string for additional shapes in the grid
-N [10^7|int] (--reseed) number of scattering events before reseeding RNG
-v (--version) print MCX revision number
example: (autopilot mode)
mcx -A -n 1e7 -f input.inp -G 1
or (manual mode)
mcx -t 2048 -T 64 -n 1e7 -f input.inp -s test -r 2 -g 10 -d 1 -b 1 -G 1
or (use inline domain definition)
mcx -f input.json -P '{"Shapes":[{"ZLayers":[[1,10,1],[11,30,2],[31,60,3]]}]}'
the 2nd command above will launch 2048 GPU threads (-t) with every 64 threads a block (-T); a total of 1e7 photons will be simulated by the first GPU (-G 1) with two equally divided runs (-r); the media/source configuration will be read from input.inp (-f) and the output will be labeled with the session id "test" (-s); the simulation will run 10 concurrent time gates (-g). Photons passing through the defined detector positions will be saved for later rescaling (-d); refractive index mismatch is considered at media boundaries (-b).
Historically, MCX supports an extended version of the input file format used by tMCimg. The difference is that MCX allows comments in the input file. A typical MCX input file looks like this:
1000000 # total photon, use -n to overwrite in the command line
29012392 # RNG seed, negative to generate
30.0 30.0 0.0 1 # source position (in grid unit), the last num (optional) sets srcfrom0 (-z)
0 0 1 # initial directional vector
0.e+00 1.e-09 1.e-10 # time-gates(s): start, end, step
semi60x60x60.bin # volume ('unsigned char' format)
1 60 1 60 # x voxel size in mm (isotropic only), dim, start/end indices
1 60 1 60 # y voxel size, must be same as x, dim, start/end indices
1 60 1 60 # y voxel size, must be same as x, dim, start/end indices
1 # num of media
1.010101 0.01 0.005 1.37 # scat. mus (1/mm), g, mua (1/mm), n
4 1.0 # detector number and default radius (in grid unit)
30.0 20.0 0.0 2.0 # detector 1 position (real numbers in grid unit) and individual radius (optional)
30.0 40.0 0.0 # ..., if individual radius is ignored, MCX will use the default radius
20.0 30.0 0.0 #
40.0 30.0 0.0 #
pencil # source type (optional)
0 0 0 0 # parameters (4 floats) for the selected source
0 0 0 0 # additional source parameters
Note that the scattering coefficient mus=musp/(1-g).
The volume file (semi60x60x60.bin in the above example), can be read in two ways by MCX: row-major[3] or column-major depending on the value of the user parameter "-a". If the volume file was saved using matlab or fortran, the byte order is column-major, and you should use "-a 0" or leave it out of the command line. If it was saved using the fwrite() in C, the order is row-major, and you can either use "-a 1".
The time gate parameter is specified by three numbers: start time, end time and time step size (in seconds). In the above example, the configuration specifies a total time window of [0 1] ns, with a 0.1 ns resolution. That means the total number of time gates is 10.
MCX provides an advanced option, -g, to run simulations when the GPU memory is limited. It specifies how many time gates to simulate concurrently. Users may want to limit that number to less than the total number specified in the input file - and by default it runs one gate at a time in a single simulation. But if there's enough memory based on the memory requirement in Section II, you can simulate all 10 time gates (from the above example) concurrently by using "-g 10" in which case you have to make sure the video card has at least 60*60*60*10*5=10MB of free memory. If you do not include the -g, MCX will assume you want to simulate just 1 time gate at a time.. If you specify a time-gate number greater than the total number in the input file, (e.g, "-g 20") MCX will stop when the 10 time-gates are completed. If you use the autopilot mode (-A), then the time-gates are automatically estimated for you.
4. Using JSON-formatted input files
Starting from version 0.7.9, MCX accepts a JSON-formatted input file in addition to the conventional tMCimg-like input format. JSON (JavaScript Object Notation) is a portable, human-readable and "fat-free" text format to represent complex and hierarchical data. Using the JSON format makes a input file self-explanatory, extensible and easy-to-interface with other applications (like MATLAB).
A sample JSON input file can be found under the examples/quicktest folder. The same file, qtest.json, is also shown below:
{
"Help": {
"[en]": {
"Domain::VolumeFile": "file full path to the volume file, file mst be in the uchar binary format",
"Domain::Dim": "dimension of the data array stored in the volume file",
"Domain::OriginType": "similar to --srcfrom0, 1 if the origin is [0 0 0], 0 if it is [1.0,1.0,1.0]",
"Domain::Step": "do not change this, should be always be 1",
"Domain::CacheBoxP0": "for cachebox mcx with -R negative_num, this specifies a 3D index for
a corner of the cache region, in grid unit",
"Domain::CacheBoxP1": "the other corner, the starting value of the indices is 1",
"Domain::Media": "the first medium is always assigned to voxels with a value of 0 or outside of
the volume, the second row is for medium type 1, and so on. mua and mus must
be in 1/mm unit",
"Session::Photons": "if -n is not specified in the command line, this defines the total photon number",
"Session::ID": "if -s is not specified in the command line, this defines the output file name stub",
"Forward::T0": "the start time of the simulation, in seconds",
"Forward::T1": "the end time of the simulation, in seconds",
"Forward::Dt": "the width of each time window, in seconds",
"Optode::Source::Pos": "the grid position of the source, can be non-integers, in grid unit",
"Optode::Detector::Pos": "the grid position of a detector, can be non-integers, in grid unit",
"Optode::Source::Dir": "the unitary directional vector of the photon at launch",
"Optode::Source::Type": "source types, must be one of the following:
pencil,isotropic,cone,gaussian,planar,pattern,fourier,arcsine,disk",
"Optode::Source::Param1": "source parameters, 4 floating-point numbers",
"Optode::Source::Param2": "additional source parameters, 4 floating-point numbers"
}
},
"Domain": {
"VolumeFile": "semi60x60x60.bin",
"Dim": [60,60,60],
"OriginType": 1,
"Step": [1.0,1.0,1.0],
"CacheBoxP0": [24,24,1],
"CacheBoxP1": [34,34,10],
"Media": [
{"mua": 0.00, "mus": 0.0, "g": 1.00, "n": 1.0},
{"mua": 0.005,"mus": 1.0, "g": 0.01, "n": 1.0}
]
},
"Session": {
"Photons": 1000000,
"RNGSeed": 29012392,
"ID": "qtest"
},
"Forward": {
"T0": 0.0e+00,
"T1": 5.0e-09,
"Dt": 5.0e-09
},
"Optode": {
"Source": {
"Pos": [29.0, 29.0, 0.0],
"Dir": [0.0, 0.0, 1.0],
"Type": "pencil",
"Param1": [0.0, 0.0, 0.0, 0.0],
"Param2": [0.0, 0.0, 0.0, 0.0]
},
"Detector": [
{
"Pos": [29.0, 19.0, 0.0],
"R": 1.0
},
{
"Pos": [29.0, 39.0, 0.0],
"R": 1.0
},
{
"Pos": [19.0, 29.0, 0.0],
"R": 1.0
},
{
"Pos": [39.0, 29.0, 0.0],
"R": 1.0
}
]
}
}
A JSON input file requiers several root objects, namely "Domain", "Session", "Forward" and "Optode". Other root sections, like "Help", will be ignored. Each object is a data structure providing information indicated by its name. Each object can contain various sub-fields. The orders of the fields in the same level are flexible. For each field, you can always find the equivalent fields in the *.inp input files. For example, The "VolumeFile" field under the "Domain" object is the same as Line#6 in qtest.inp; the "RNGSeed" under "Session" is the same as Line#2; the "Optode.Source.Pos" is the same as the triplet in Line#3; the "Forward.T0" is the same as the first number in Line#5, etc.
An MCX JSON input file must be a valid JSON text file. You can validate your input file by running a JSON validator, for example http://jsonlint.com/ You should always use "" to quote a "name" and separate parallel items by ",".
MCX accepts an alternative form of JSON input, but using it is not recommended. In the alternative format, you can use
"rootobj_name.field_name": valueto represent any parameter directly in the root level. For example
{
"Domain.VolumeFile": "semi60x60x60.bin",
"Session.Photons": 10000000,
...
}
You can even mix the alternative format with the standard format. If any input parameter has values in both formats in a single input file, the standard-formatted value has higher priority.
To invoke the JSON-formatted input file in your simulations, you can use the "-f" command line option with MCX, just like using an .inp file. For example:
mcx -A -n 20 -f onecube.json -s onecubejson
The input file must have a ".json" suffix in order for MCX to recognize. If the input information is set in both command line, and input file, the command line value has higher priority (this is the same for .inp input files). For example, when using "-n 20", the value set in "Session"/"Photons" is overwritten to 20; when using "-s onecubejson", the "Session"/"ID" value is modified. If your JSON input file is invalid, MCX will quit and point out where the format is incorrect.
5. Using JSON-formatted shape description files
Starting from v0.7.9, MCX can also use a shape description file in the place of the volume file. Using a shape-description file can save you from making a binary .bin volume. A shape file uses more descriptive syntax and can be easily understood and shared with others.
Samples on how to use the shape files are included under the example/shapetest folder.
The sample shape file, shapes.json, is shown below:
{
"MCX_Shape_Command_Help":{
"Shapes::Common Rules": "Shapes is an array object. The Tag field sets the voxel value for each
region; if Tag is missing, use 0. Tag must be smaller than the maximum media number in the
input file.Most parameters are in floating-point (FP). If a parameter is a coordinate, it
assumes the origin is defined at the lowest corner of the first voxel, unless user overwrite
with an Origin object. The default origin of all shapes is initialized by user's --srcfrom0
setting: if srcfrom0=1, the lowest corner of the 1st voxel is [0,0,0]; otherwise, it is [1,1,1]",
"Shapes::Name": "Just for documentation purposes, not parsed in MCX",
"Shapes::Origin": "A floating-point (FP) triplet, set coordinate origin for the subsequent objects",
"Shapes::Grid": "Recreate the background grid with the given dimension (Size) and fill-value (Tag)",
"Shapes::Sphere": "A 3D sphere, centered at C0 with radius R, both have FP values",
"Shapes::Box": "A 3D box, with lower corner O and edge length Size, both have FP values",
"Shapes::SubGrid": "A sub-section of the grid, integer O- and Size-triplet, inclusive of both ends",
"Shapes::XLayers/YLayers/ZLayers": "Layered structures, defined by an array of integer triples:
[start,end,tag]. Ends are inclusive in MATLAB array indices. XLayers are perpendicular to x-axis, and so on",
"Shapes::XSlabs/YSlabs/ZSlabs": "Slab structures, consisted of a list of FP pairs [start,end]
both ends are inclusive in MATLAB array indices, all XSlabs are perpendicular to x-axis, and so on",
"Shapes::Cylinder": "A finite cylinder, defined by the two ends, C0 and C1, along the axis and a radius R",
"Shapes::UpperSpace": "A semi-space defined by inequality A*x+B*y+C*z>D, Coef is required, but not Equ"
},
"Shapes": [
{"Name": "Test"},
{"Origin": [0,0,0]},
{"Grid": {"Tag":1, "Size":[40,60,50]}},
{"Sphere": {"Tag":2, "O":[30,30,30],"R":20}},
{"Box": {"Tag":0, "O":[10,10,10],"Size":[10,10,10]}},
{"Subgrid": {"Tag":1, "O":[13,13,13],"Size":[5,5,5]}},
{"UpperSpace":{"Tag":3,"Coef":[1,-1,0,0],"Equ":"A*x+B*y+C*z>D"}},
{"XSlabs": {"Tag":4, "Bound":[[5,15],[35,40]]}},
{"Cylinder": {"Tag":2, "C0": [0.0,0.0,0.0], "C1": [15.0,8.0,10.0], "R": 4.0}},
{"ZLayers": [[1,10,1],[11,30,2],[31,50,3]]}
]
}
A shape file must contain a "Shapes" object in the root level. Other root-level fields are ignored. The "Shapes" object is a JSON array, with each element representing a 3D object or setting. The object-class commands include "Grid", "Sphere", "Box" etc. Each of these object include a number of sub-fields to specify the parameters of the object. For example, the "Sphere" object has 3 subfields, "O", "R" and "Tag". Field "O" has a value of 1x3 array, representing the center of the sphere; "R" is a scalar for the radius; "Tag" is the voxel values. The most useful command is "[XYZ]Layers". It contains a series of integer triplets, specifying the starting index, ending index and voxel value of a layered structure. If multiple objects are included, the subsequent objects always overwrite the overlapping regions covered by the previous objects.
There are a few ways for you to use shape description records in your MCX simulations. You can save it to a JSON shape file, and put the file name in Line#6 of yoru .inp file, or set as the value for Domain.VolumeFile field in a .json input file. In these cases, a shape file must have a suffix of .json.
You can also merge the Shapes section with a .json input file by simply appending the Shapes section to the root-level object. You can find an example, jsonshape_allinone.json, under examples/shapetest. In this case, you no longer need to define the "VolumeFile" field in the input.
Another way to use Shapes is to specify it using the -P (or --shapes) command line flag. For example:
mcx -f input.json -P '{"Shapes":[{"ZLayers":[[1,10,1],[11,30,2],[31,60,3]]}]}'
This will first initialize a volume based on the settings in the input .json file, and then rasterize new objects to the domain and overwrite regions that are overlapping.
For both JSON-formatted input and shape files, you can use the JSONlab toolbox [4] to load and process in MATLAB.
6. Using MCXLAB in MATLAB and Octave
MCXLAB is the native MEX version of MCX for Matlab and GNU Octave. It includes the entire MCX code in a MEX function which can be called directly inside Matlab or Octave. The input and output files in MCX are replaced by convenient in-memory struct variables in MCXLAB, thus, making it much easier to use and interact. Matlab/Octave also provides convenient plotting and data analysis functions. With MCXLAB, your analysis can be streamlined and speed- up without involving disk files.
Please read the mcxlab/README.txt file for more details on how to install and use MCXLAB.
7. Using MCX Studio GUI
MCX Studio is a graphics user interface (GUI) for MCX. It gives users a straightforward way to set the command line options and simulation parameters. It also allows users to create different simulation tasks and organize them into a project and save for later use. MCX Studio can be run on many platforms such as Windows, GNU Linux and Mac OS.
To use MCX Studio, it is suggested to put the mcxstudio binary in the same directory as the mcx command; alternatively, you can also add the path to mcx command to your PATH environment variable.
Once launched, MCX Studio will automatically check if mcx binary is in the search path, if so, the "GPU" button in the toolbar will be enabled. It is suggested to click on this button once, and see if you can see a list of GPUs and their parameters printed in the output field at the bottom part of the window. If you are able to see this information, your system is ready to run MCX simulations. If you get error messages or not able to see any usable GPU, please check the following:
- are you running MCX Studio/MCX on a computer with a supported card?
- have you installed the CUDA/NVIDIA drivers correctly?
- did you put mcx in the same folder as mcxstudio or add its path to PATH?
If your system has been properly configured, you can now add new simulations by clicking the "New" button. MCX Studio will ask you to give a session ID string for this new simulation. Then you are allowed to adjust the parameters based on your needs. Once you finish the adjustment, you should click the "Verify" button to see if there are missing settings. If everything looks fine, the "Run" button will be activated. Click on it once will start your simulation. If you want to abort the current simulation, you can click the "Stop" button.
You can create multiple tasks with MCX Studio by hitting the "New" button again. The information for all session configurations can be saved as a project file (with .mcxp extension) by clicking the "Save" button. You can load a previously saved project file back to MCX Studio by clicking the "Load" button.
8. Interpreting the Output
MCX output consists of two parts, the flux volume file and messages printed on the screen.
8.1 Output files
An mc2 file contains the flux distribution from the simulation in the given medium. By default, this flux is a normalized solution (as opposed to the raw probability) therefore, one can compare this directly to the analytical solutions (i.e. Green's function). The order of storage in the mc2 files is the same as the input file: i.e., if the input is row-major, the output is row-major, and so on. The dimensions of the file are Nx, Ny, Nz, and Ng where Ng is the total number of time gates.
By default, MCX produces the Green's function of the fluence rate (or flux) for the given domain and source. Sometime it is also known as the time-domain "two-point" function. If you run MCX with the following command
mcx -f input.inp -s output ....
the flux data will be saved in a file named "output.dat" under the current folder. If you run MCX without "-s output", the output file will be named as "input.inp.dat".
To understand this further, you need to know that a flux is measured by number of particles passing through an infinitesimal spherical surface per unit time at a given location. The unit of MCX output flux is "1/(mm2s)", if the flux is interpreted as the "particle flux" [6], or "J/(mm2s)", if it is interpreted as the "energy flux" [6].
The Green's function of the flux simply means that the flux is produced by a unitary source. In simple terms, this represents the fraction of particles/energy that arrives a location per second under '''the radiation of 1 unit (packet or J) of particle or energy at time t=0'''. The Green's function is calculated by a process referred to as the "normalization" in the MCX code and is detailed in the MCX paper [6] (MCX and MMC outputs share the same meanings).
Please be aware that the output flux is calculated at each time-window defined in the input file. For example, if you type
0.e+00 5.e-09 1e-10 # time-gates(s): start, end, step
in the 5th row in the input file, MCX will produce 50 flux distributions, corresponding to the time-windows at [0 0.1] ns, [0.1 0.2]ns ... and [4.9,5.0] ns. To convert the flux distributions to the fluence distributions for each time-window, you just need to multiply each solution by the width of the window, 0.1 ns in this case. To convert the time-domain flux to the continuous-wave (CW) fluence, you need to integrate the flux in t=[0,inf]. Assuming the flux after 5 ns is negligible, then the CW fluence is simply sum(flux_i*0.1 ns, i=1,50). You can read mcx/examples/validation/plotsimudata.m and mcx/examples/sphbox/plotresults.m for examples to compare an MCX output with the analytical flux/fluence solutions.
One can load an mc2 output file into Matlab or Octave using the loadmc2 function in the <mcx root>/utils folder.
To get a continuous-wave solution, run a simulation with a sufficiently long time window, and sum the flux along the time dimension, for example
mcx=loadmc2('output.mc2',[60 60 60 10],'float');
cw_mcx=sum(mcx,4);
Note that for time-resolved simulations, the corresponding solution in the results approximates the flux at the center point of each time window. For example, if the simulation time window setting is [t0,t0+dt,t0+2dt,t0+3dt...,t1], the time points for the snapshots stored in the solution file is located at [t0+dt/2, t0+3*dt/2, t0+5*dt/2, ... ,t1-dt/2]
A more detailed interpretation of the output data can be found at http://mcx.sf.net/cgi-bin/index.cgi?MMC/Doc/FAQ#How_do_I_interpret_MMC_s_output_data
8.2 Console print messages
Timing information is printed on the screen (stdout). The clock starts (at time T0) right before the initialization data is copied from CPU to GPU. For each simulation, the elapsed time from T0 is printed (in ms). Also the accumulated elapsed time is printed for all memory transaction from GPU to CPU.
9. Best practices guide
To maximize MCX's performance on your hardware, you should follow the best practices guide listed below:
Use dedicated GPUs
A dedicated GPU is a GPU that is not connected to a monitor. If you use a non-dedicated GPU, any kernel (GPU function) can not run more than a few seconds. This greatly limits the efficiency of MCX. To set up a dedicated GPU, it is suggested to install two graphics cards on your computer, one is set up for displays, the other one is used for GPU computation only. If you have a dual-GPU card, you can also connect one GPU to a single monitor, and use the other GPU for computation (selected by -G in mcx). If you have to use a non-dedicated GPU, you can either use the pure command-line mode (for Linux, you need to stop X server), or use the "-r" flag to divide the total simulation into a set of simulations with less photons, so that each simulation only lasts a few seconds.
Launch as many threads as possible
It has been shown that MCX's speed is related to the thread number (-t). Generally, the more threads, the better speed, until all GPU resources are fully occupied. For higher-end GPUs, a thread number over 10,000 is recommended. Please use the autopilot mode, "-A", to let MCX determine the "optimal" thread number when you are not sure what to use.
Use atomic operations when extra accuracy is needed
As described in Ref. [Fang2009], MCX uses non-atomic operations by default. This causes reduced fluence/flux near a point/pencil beam source due to data-racing. However, as characterized in [Fang2009], this error is fairly small when a photon is a few voxels (~5) away from the source. If the region-of-interest is very close to the source, we suggest you to use the atomic/cached version of MCX. If you use the fermi version of MCX, you can use "-R -2" in the command to enable full atomic operations. The simulation speed for the full atomic code will be about 50% of the non-atomic version on a Fermi card. Alternatively, you can use the "-R radius" flag to enable atomic operations within the specified radius from the source. For wide-field sources, we believe the data-racing is minimal, thus, the non-atomic simulation should be sufficiently accurate.
10. Reference
[1] Qianqian Fang and David A. Boas, "Monte Carlo Simulation of Photon Migration in 3D Turbid Media Accelerated by Graphics Processing Units," Optics Express, vol. 17, issue 22, pp. 20178-20190 (2009).
If you use MCX in your research, the author of this software would like you to cite the above paper in your related publications.
Links:
[1] http://developer.nvidia.com/cuda-downloads [2] http://www.nvidia.com/object/cuda_gpus.html [3] http://en.wikipedia.org/wiki/Row-major_order [4] http://iso2mesh.sourceforge.net/cgi-bin/index.cgi?jsonlab [5] http://science.jrank.org/pages/60024/particle-fluence.html [6] http://www.opticsinfobase.org/oe/abstract.cfm?uri=oe-17-22-20178