---------------------------------------------------------------------
                   Monte Carlo eXtreme  (MCX-CL)
                         OpenCL Edition
---------------------------------------------------------------------

Author: Qianqian Fang <q.fang at neu.edu>
License: GNU General Public License version 3 (GPLv3)
Version: 0.9 (Eternity - beta)
Website: http://mcx.space

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Table of Content:

O.    What's New
I.    Introduction
II.   Requirement and Installation
III.  Running Simulations
IV.   Using JSON-formatted input files
V.    Using JSON-formatted shape description files
VI.   Using MCXLAB in MATLAB and Octave
VII.  Using MCX Studio GUI
VIII. Interpreting the Outputs
IX.   Best practices guide
X.    Acknowledgement
XI.   Reference


---------------------------------------------------------------------

O.    What's New

In MCX-CL v2019.3 (0.9), we added a list of major new additions, including

* Add 4 built-in complex domain examples - Colin27 brain atlas, USC_19-5 brain atlas, Digimouse, and mcxyz skin-vessel benchmark
* Support isotropic launch for all focuable sources - gaussian, pattern, pattern3d, fourier, disk, fourierx, fourierx2d, and slit - by setting cfg.srcdir(4) to nan
* Add python-based mch file reader (by Shih-Cheng Tu), nightly build compilation script, colored command line output, and more
* Add gpubenchmark script to mcxlabcl, please browse submitted benchmark results at http://mcx.space/computebench/
* Half-precision ray-tracing support (paper in-preparation)
* The GPU-ANLM denoiser newly added for mcx, mcxfilter.m, detailed in our [Yao2018] paper, also applies to mcxlabcl/mcxcl output

In addition, we also fixed a number of critical bugs, such as

* fix bugs for incorrect results for isotropic source, cone source
* fix mcxlabcl gpuinfo output crash using multiple GPUs
* fix mcxlab crash when srcpattern/srcdir/srcpos/detpos are not in double precision
* fix mcxlab crash due to racing in multi-threads
* force g to 1 in region where mus is 0

Compared to MCX v2019.3 (1.4.8), MCX-CL has not yet supported the below features as of this release

* Output momentum transfer for DCS simulations
* Output photon trajectory data
* Specifying one of the four boundary conditions for each of the 6 facets of the domain
* Output partial scattering event counts like MMC
* Photon replay
* Photon sharing - simultaneous simulations of multiple patterns
* 2D simulations
* Support photon numbers over 2^31-1 in one simulation

[Yuan2018] Yaoshen Yuan, Leiming Yu, Zafer Doğan, Qianqian Fang*, 
"Graphics processing units-accelerated adaptive nonlocal means filter for denoising 
three-dimensional Monte Carlo photon transport simulations," 
J. of Biomedical Optics, 23(12), 121618 (2018), URL: https://doi.org/10.1117/1.JBO.23.12.121618

---------------------------------------------------------------------

I.  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.

MCX-CL is the OpenCL implementation of the MCX algorithm. Unlike MCX
which only be executed on NVIDIA GPUs, MCX-CL is written in OpenCL,
the Open Computing Language, and can be executed on most modern CPUs
and GPUs available today, including Intel and AMD CPUs and GPUs. MCX-CL
is highly portable, highly scalable and is feature rich like MCX.

The details of MCX-CL can be found in the below paper

[Yu2018] Leiming Yu, Fanny Nina-Paravecino, David Kaeli, and Qianqian Fang, \
"Scalable and massively parallel Monte Carlo photon transport simulations \
for heterogeneous computing platforms," J. Biomed. Optics, 23(1), 010504 (2018) .

A short summary of the main features includes:

*. 3D heterogeneous media represented by voxelated array
*. support over a dozen source forms, including wide-field and pattern illuminations
*. boundary reflection support
*. time-resolved photon transport simulations
*. saving photon partial path lengths and trajectories
*. 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 graphical user interface
*. native Matlab/Octave support for high usability
*. flexible JSON interface for future extensions
*. multi-GPU support

MCX-CL can be used on Windows, Linux and Mac OS. Multiple user 
interfaces are provided, including

- command line mode: mcxcl can be executed in the command line, best suited \
  for batch data processing
- graphical user interface with MCXStudio: MCXStudio is a unified GUI program \
  for MCX, MCX-CL and MMC. One can intuitively set all parameters, including \
  GPU settings, MC settings and domain design, in the cross-platform interface
- calling inside MATLAB/Octave: mcxlabcl is a mex function, one can call it \
  inside MATLAB or GNU Octave to get all functionalities as the command line \
  version.
  
If a user is familiar with MATLAB/Octave, it is highly recommended to 
use MCXCL in MATLAB/Octave to ease data visualization. If one prefers a 
GUI, please use MCXStudio to start.  For users who are familiar with MCX/MCXCL 
and need it for regular data processing, using the command line mode is 
recommended.

---------------------------------------------------------------------------
II. Requirement and Installation

With the up-to-date driver installed for your computers, MCXCL can run on
almost all computers. The requirements for using this software include

*. a CPU, or
*. a CUDA capable NVIDIA graphics card, or
*. an AMD graphics card
and
*. pre-installed graphics driver - typically includes the OpenCL library (libOpenCL.* or OpenCL.dll)

For speed differences between different CPUs/GPUs made by different vendors, please
see your above paper [1] and our websites

http://mcx.space/computebench/
http://mcx.space/mcxcl

Generally speaking, AMD and NVIDIA high-end dedicated GPU performs the best, about 20-60x 
faster than a multi-core CPU; Intel's integrated GPU is about 3-4 times faster than
a multi-core CPU.

To install MCXCL, you simply download the binary executable corresponding to your 
computer architecture and platform, extract the package 
and run the executable under the <mcxcl root>/bin directory. For Linux
and MacOS users, please double check and make sure libOpenCL.so is installed under 
the /usr/lib directory. If it is installed under a different directory, please
define environment variable LD_LIBRARY_PATH to include the path.

If libOpenCL.so or OpenCL.dll does not exist on your system or, please
make sure you have installed CUDA SDK (if you are using an nVidia card)
or AMD APP SDK (if you are using an AMD card). 

The below installation steps can be browsed online at 

http://mcx.space/wiki/index.cgi/wiki/index.cgi?Workshop/MCX18Preparation/MethodA


=== # Step 1. Verify your CPU/GPU support ==

MCX-CL supports a wide-range of processors, including Intel/AMD CPUs 
and GPUs from NVIDIA/AMD/Intel. If your computer has been working previously,
in most cases, MCX-CL can simply run out-of-box. However, if you have trouble,
please follow the below detailed steps to verify and setup your OS to run
MCX-CL.

==== # Verify GPU/CPU support ===

To verify if you have installed the OpenCL or CUDA support, you may

* if you have a windows machine, download and install the \
 [https://www.voidtools.com/ Everything Search] tool (a small and 
 fast file name search utility), and type '''"opencl.dll"''' in the search bar
** '''Expected result''': you must see <tt>OpenCL.dll</tt> (or \
  <tt>nvopencl.dll</tt> if you have an NVIDIA GPU) installed in the \
  <tt>Windows\System32</tt> directory.
* if you have an Mac, open a terminal, and type <tt>ls /System/Library/Frameworks/OpenCL.framework/Versions/A/OpenCL</tt>
** '''Expected result''': you should not see an error.
* if you have a Linux laptop, open a terminal, and type <tt>locate libOpenCL.so</tt>, 
** '''Expected result''': you should see one or multiple libOpenCL files

If the <tt>OpenCL.dll</tt> file is not found on your system, please 
read the below sections. Otherwise, please go to Step 2: Install MATLAB.

==== # Computers without discrete GPUs ===

In many cases, your computer runs on an Intel CPU with integrated graphics. In 
this case, please make sure you have installed the latest Intel graphics drivers. 
If you are certain that you have installed the graphics drivers, or your 
graphics works smoothly, please skip this step.

If you want to double check, for Windows machine, you can download the 
"Intel Driver&Support Assistant" to check if you have installed the 
graphics drivers

https://downloadcenter.intel.com/download/24345/Intel-Driver-Support-Assistant

for a Mac, you need to use your App store to update the driver, see the 
below link for details

https://www.intel.com/content/www/us/en/support/articles/000022440/graphics-drivers.html

for a Linux (for example Ubuntu) laptop, the intel CPU OpenCL run-time 
can be downloaded from

https://software.intel.com/en-us/articles/opencl-drivers#latest_CPU_runtime

if you want to use both Intel CPU and GPU on Linux, you need to install 
the OpenCL™ 2.0 GPU/CPU driver package for Linux* (this involves compiling a new kernel)

https://software.intel.com/en-us/articles/opencl-drivers#latest_linux_driver


==== # Computers with discrete GPUs ===

If you have a computer with a discrete GPU, you need to make sure your 
discrete GPU is configured with the appropriate GPU 
driver installed. Again, if you have been using your laptop regularly and 
the graphics has been smooth, likely your graphics driver has already been 
installed.

If your GPU driver was not installed, and would like to install, or upgrade 
from an older version, for an NVIDIA GPU, you may browse this link to 
install the matching driver

http://www.nvidia.com/Download/index.aspx

if your GPU is an AMD GPU, please use the below link

https://support.amd.com/en-us/download

It is also possible to simultaneously access Intel CPU along with your 
discrete GPU. In this case, you need to download the latest Intel OpenCL 
Runtime for CPU only if you haven't installed it already. 

https://software.intel.com/en-us/articles/opencl-drivers#latest_CPU_runtime

Note: if you have an NVIDIA GPU, there is no need to install CUDA in 
order for you to run MCX/MCXLAB.


=== # Step 2. Install MATLAB or GNU Octave =

One must install either a MATLAB or GNU Octave if one needs to use mcxlabcl.
If you use a Mac or Linux laptop, you need to create a link (if this link does
not exist) so that your system can find MATLAB. To do this you start a terminal, 
and type

 sudo ln -s /path/to/matlab /usr/local/bin

please replace <tt>/path/to/matlab</tt> to the actual <tt>matlab</tt> command 
full path (for Mac, this is typically <tt>/Application/MATLAB_R20???.app/bin/matlab</tt>, 
for Linux, it is typically <tt>/usr/local/MATLAB/R20???/bin/matlab</tt>, ??? 
is the year and release, such as 18a, 17b etc). You need to type your 
password to create this link.

To verify your computer has MATLAB installed, please start a terminal on a 
Mac or Linux, or type "cmd" and enter in Windows start menu, in the terminal, 
type "<tt>matlab</tt>" and enter, you should see MATLAB starts.

=== # Step 3. Download MCXCL ==

One can download two separate MCXCL packages (standalone mcxcl binary, and mcxlabcl)
or download the integrated MCXStudio package (which contains mcx, mcxcl, mmc, mcxlab, 
mcxlabcl and mmclab) where both packages, and many more, are included. The latest
stable released can be found on the MCX's website. However, if you want to use the
latest (but sometimes containing half-implemented features) software, you can access
the nightly-built packages from

http://mcx.space/nightly/

If one has downloaded the mcxcl binary package, after extraction, you may open
a terminal (on Windows, type cmd the Start menu), cd mcxcl folder and then cd
the bin subfolder. Please type "mcxcl" and enter, if the binary is compatible with
your OS, you should see the printed help info. The next step is to run 

 mcxcl -L

this will query your system and find any hardware that can run mcxcl. If your
hardware (CPU and GPU) have proper driver installed, the above command will typically
return 1 or more available computing hardware. Then you can move to the next step.

If you do not see any processor printed, that means your CPU or GPU does not have
OpenCL support (because it is too old or no driver installed). You will need to 
go to their vendor's website and download the latest driver. For Intel CPUs older
than Ivy Bridge (4xxx), OpenCL and MCXCl are not well supported. Please consider
installing dedicated GPU or use a different computer.

In the case one has installed the MCXStudio package, you may follow the below 
procedure to test for hardware compatibility.


Please click on the folder matching your operating system (for example, if you run 
a 64bit Windows, you need to navigate into <tt>win64</tt> folder), and download 
the file named  <tt>"MCXStudio-MCX18-nightlybuild.zip"</tt>.

Open this file, and unzip it to a working folder (for Windows, for example, the 
<tt>Documents</tt> or <tt>Downloads</tt> folder). The package needs about 100 MB disk space.

Once unzipped, you should be able to see a folder named '''"MCXStudio"''', 
with a few executables and 3 subfolders underneath. See the folder structure below:

<pre>MCXStudio/
├── MATLAB/
│   ├── mcxlab/
│   ├── mcxlabcl/
│   └── mmclab/
├── MCXSuite/
│   ├── mcx/
│   ├── mcxcl/
│   └── mmc/
├── mcxstudio
├── mcx
├── mmc
├── mcxcl
└── Output/
</pre>

Please make sure that your downloaded <tt>MCXStudio</tt> must match your operating system.

==== # Notes for Mac Users ===
'''For Mac users:''' Please unzip the package under your '''[https://www.cnet.com/how-to/how-to-find-your-macs-home-folder-and-add-it-to-finder/ home directory]''' directly (Shift+Command+H).

==== # Notes for Windows Users ===
When you start MCXStudio, you may see a dialog to ask you to modify the TdrDelay key 
in the registry so that mcx can run more than 5 seconds. If you select Yes, some 
of you may get an error saying you do not have permission. 

To solve this problem, you need to quit MCXStudio, and then right-click on the 
<tt>mcxstudio.exe</tt>, and select "Run as Administrator". Then, you should be 
able to apply the registry change successfully. 

Alternatively, one should open file browser, navigate into mcxcl/setup/win64 folder,
and right-click on the "apply_timeout_registry_fix.bat" file and select 
"Run as Administrator".

'''You must reboot your computer for this change to be effective!'''

=== # Step 4. Start MCXStudio and query GPU information ==

Now, navigate to the MCXStudio folder (i.e. the top folder of the extracted 
software structure). On Windows, right-click on the executable named <tt>"mcxstudio.exe"</tt> 
and select "Run as Administrator" for the first time only; on the Linux, 
double click on the <tt>mcxstudio</tt> executable; on the Mac, open a 
terminal and type

 cd ~/MCXStudio
 open mcxstudio.app

First, click on the "New" button to the top-left (green plus icon), 
select the 3rd option "<tt>NVIDIA/AMD/Intel CPUs/GPUs (MCX-CL)</tt>", 
and type a session name '''"test"''' in the field below. Then click 
OK. You should see a blue/yellow "test" icon added to the left panel. 

Now, click on the '''"GPU"''' button on the toolbar (6th button from 
the left side), an Output window will popup, and wait for a few seconds, 
you should see an output like

<pre>"-- Run Command --"
"mcxcl" -L
"-- Printing GPU Information --"
Platform [0] Name NVIDIA CUDA
============ GPU device ID 0 [1 of 2]: Graphics Device  ============
 Device 1 of 2:		Graphics Device
 Compute units   :	80 core(s)
 Global memory   :	12644188160 B
 Local memory    :	49152 B
 Constant memory :	65536 B
 Clock speed     :	1455 MHz
 Compute Capacity:	7.0
 Stream Processor:	10240
 Vendor name    :	NVIDIA
 Auto-thread    :	655360       
...
Platform [1] Name Intel(R) OpenCL
============ CPU device ID 2 [1 of 1]: Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz  ============
 Device 3 of 1:		Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz
 Compute units   :	8 core(s)
 Global memory   :	33404575744 B
 Local memory    :	32768 B
 Constant memory :	131072 B
 Clock speed     :	4200 MHz
 Vendor name    :	Intel
 Auto-thread    :	512
 Auto-block     :	64

"-- Task completed --"
</pre>

Your output may look different from above. If you do not see any output, or 
it returns no GPU found, that means your OpenCL support was not installed 
properly. Please go back to Steps 1-2 and reinstall the drivers. 

If you have Intel CPU with Integrated GPU, you should be able to see a section with 
<b>"Platform [?] Name Intel(R) OpenCL"</b> in the above output. You may see only 
the CPU is listed, or both the CPU and the integrated GPU.


=== # Step 5. Run a trial simulation ==

If your above GPU query was successful, you should now see in the middle panel 
of the MCXStudio window, under the Section entitled "GPU Settings", in a check-box 
list under "Run MCX on", you should now see the available devices on your laptop. 

To avoid running lengthy simulations, please change the <u>"Total photon number (-n)"</u> 
field under the <u>"Basic Settings"</u> from <tt>1e6</tt> to <tt>1e5</tt>.

Now, you can then run a trial simulation, by first clicking on the "Validate" 
button (blue check-mark icon), and then click on "Run" (the button to the 
right of validate). This will launch an MCXCL simulation. The output window 
will show again, and you can see the messages printed from the simulation, 
similar to the output below

<pre>"-- Command: --"
mcxcl --session "preptest"  --input "/drives/taote1/users/fangq/Download/MCXStudio/Output/mcxclsessions/preptest/preptest.json" --root "/drives/taote1/users/fangq/Download/MCXStudio/Output/mcxclsessions/preptest" --outputformat mc2 --gpu 10 --autopilot 1 --photon 10000000 --normalize 1 --save2pt 1 --reflect 1 --savedet 1 --unitinmm 1.00 --saveseed 0 --seed "1648335518" --compileropt "-D USE_ATOMIC" --array 0 --dumpmask 0 --repeat 1  --maxdetphoton 10000000
"-- Executing Simulation --"
==============================================================================
=                       Monte Carlo eXtreme (MCX) -- OpenCL                  =
=          Copyright (c) 2010-2018 Qianqian Fang <q.fang at neu.edu>         =
=                             http://mcx.space/                              =
=                                                                            =
= Computational Optics&Translational Imaging (COTI) Lab - http://fanglab.org =
=            Department of Bioengineering, Northeastern University           =
==============================================================================
=    The MCX Project is funded by the NIH/NIGMS under grant R01-GM114365     =
==============================================================================
$Rev::6e839e $ Last $Date::2017-07-20 12:46:23 -04$ by $Author::Qianqian Fang$
==============================================================================
- variant name: [Detective MCXCL] compiled with OpenCL version [1]
- compiled with: [RNG] Logistic-Lattice [Seed Length] 5
initializing streams ...	init complete : 0 ms

Building kernel with option: -cl-mad-enable -DMCX_USE_NATIVE -DMCX_SIMPLIFY_BRANCH -DMCX_VECTOR_INDEX -DMCX_SRC_PENCIL  -D USE_ATOMIC -DUSE_ATOMIC -D MCX_SAVE_DETECTORS -D MCX_DO_REFLECTION
build program complete : 23 ms
- [device 0(1): Graphics Device] threadph=15 oddphotons=169600 np=10000000.0 nthread=655360 nblock=64 repetition=1
set kernel arguments complete : 23 ms
lauching mcx_main_loop for time window [0.0ns 5.0ns] ...
simulation run# 1 ... 

kernel complete:  	796 ms
retrieving flux ... 	
detected 0 photons, total: 0	transfer complete:        818 ms
normalizing raw data ...	normalization factor alpha=20.000000
saving data to file ... 216000 1	saving data complete : 821 ms

simulated 10000000 photons (10000000) with 1 devices (repeat x1)
MCX simulation speed: 12953.37 photon/ms
total simulated energy: 10000000.00	absorbed: 27.22654%
(loss due to initial specular reflection is excluded in the total)
</pre>

If this simulation is completed successfully, you should be able to see the 
"Simulation speed" and total simulated energy reported at the end. Please 
verify your "absorbed" percentage value printed at the end (in bold above), 
and make sure it is '''~27%'''. We found that some Intel OpenCL library 
versions produced incorrect results. 

If your laptop shows an error for the Intel GPU, please choose another 
device from the "GPU Settings" section, and run the simulation again. 

If your GPU/CPU gives the below error (found on HD4400 GPU and 4th gen Intel CPUs)

  error: OpenCL extension 'cl_khr_fp64' is unsupported
  MCXCL ERROR(11):Error: Failed to build program executable! in unit mcx_host.cpp:510

You may add 

   -J "-DUSE_LL5_RAND"

in the <tt>MCXStudio GUI</tt>\<tt>Advanced Settings</tt>\<tt>Additional Parameters</tt> 
field. This should allow it to run, but please verify the absorption fraction 
is ~27%. For 4th generation Intel CPU, we found that install the Intel CPU 
OpenCL run-time can produce correct simulations. Please download it from here

https://software.intel.com/en-us/articles/opencl-drivers#latest_CPU_runtime

=== # Step 6. Test MATLAB for visualization ==

From v2019.3, MCXStudio provides builtin 3D volume visualization, this step is no longer needed.


=== # Step 7. Setting up MATLAB search path ==

The next step is to set up the search paths for MCXLAB/MMCLAB. You need to 
start MATLAB, and in the Command window, please type 

 pathtool

this will popup a window. Click on the "Add with Subfolders ..." button 
(the 2nd from the top), then browse the MCXStudio folder, then select 
OK. Now you should see all needed MCX/MMC paths are added to MATLAB. 
Before you quick this window, click on the "Save" button.

To verify if your MCXLAB/MMCLAB/MCXLABCL has been installed properly, please type

 which mcxlab
 which mmclab
 which mcxlabcl

you should see their full paths printed. 


To see if you can run MCXLAB-CL in your environment, please type 

 USE_MCXCL=1   ''%define this line in the base workspace, all subsequent mcxlab calls will use mcxcl''
 info=mcxlab('gpuinfo')
 clear USE_MCXCL

this should print a list of CPU/GPU devices using which you can run the MC simulations.

upload:matlab_gpu_verify.png

If you do not see any output, that means your CPU/GPU OpenCL driver was not installed 
properly, you need to go back to Steps 1-2.

If you have an NVIDIA GPU, and have installed the proper GPU driver, you may run

 info=mcxlab('gpuinfo')  % notice the command is mcxlab instead of mcxlabcl

this should print a list of NVIDIA GPU from the MATLAB window.

---------------------------------------------------------------------------
III.Running Simulations

To run a simulation, the minimum input is a configuration (text) file,
and a volume (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:

<pre>==============================================================================
=                       Monte Carlo eXtreme (MCX) -- OpenCL                  =
=          Copyright (c) 2010-2019 Qianqian Fang <q.fang at neu.edu>         =
=                             http://mcx.space/                              =
=                                                                            =
= Computational Optics&Translational Imaging (COTI) Lab - http://fanglab.org =
=            Department of Bioengineering, Northeastern University           =
==============================================================================
=    The MCX Project is funded by the NIH/NIGMS under grant R01-GM114365     =
==============================================================================
$Rev::4fdc45$2019.3 $Date::2018-03-29 00:35:53 -04$ by $Author::Qianqian Fang$
==============================================================================

usage: mcxcl <param1> <param2> ...
where possible parameters include (the first value in [*|*] is the default)

== Required option ==
 -f config     (--input)       read an input file in .json or .inp format

== MC options ==
 -n [0|int]    (--photon)      total photon number (exponential form accepted)
 -r [1|int]    (--repeat)      divide photons into r groups (1 per GPU call)
 -b [1|0]      (--reflect)     1 to reflect photons at ext. boundary;0 to exit
 -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
 -E [0|int]    (--seed)        set random-number-generator seed, -1 to generate
 -z [0|1]      (--srcfrom0)    1 volume origin is [0 0 0]; 0: origin at [1 1 1]
 -k [1|0]      (--voidtime)    when src is outside, 1 enables timer inside void
 -P '{...}'    (--shapes)      a JSON string for additional shapes in the grid
 -e [0.|float] (--minenergy)   minimum energy level to terminate a photon
 -g [1|int]    (--gategroup)   number of time gates per run
 -a [0|1]      (--array)       1 for C array (row-major); 0 for Matlab array

== GPU options ==
 -L            (--listgpu)     print GPU information only
 -t [16384|int](--thread)      total thread number
 -T [64|int]   (--blocksize)   thread number per block
 -A [1|int]    (--autopilot)   auto thread config:1 enable;0 disable
 -G [0|int]    (--gpu)         specify which GPU to use, list GPU by -L; 0 auto
      or
 -G '1101'     (--gpu)         using multiple devices (1 enable, 0 disable)
 -W '50,30,20' (--workload)    workload for active devices; normalized by sum
 -I            (--printgpu)    print GPU information and run program
 -o [3|int]    (--optlevel)    optimization level 0-no opt;1,2,3 more optimized
 -J '-D MCX'   (--compileropt) specify additional JIT compiler options
 -k my_simu.cl (--kernel)      user specified OpenCL kernel source file

== Output options ==
 -s sessionid  (--session)     a string to label all output file names
 -d [1|0]      (--savedet)     1 to save photon info at detectors; 0 not save
 -x [0|1]      (--saveexit)    1 to save photon exit positions and directions
                               setting -x to 1 also implies setting '-d' to 1
 -X [0|1]      (--saveref)     1 to save diffuse reflectance at the air-voxels
                               right outside of the domain; if non-zero voxels
			       appear at the boundary, pad 0s before using -X
 -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
 -F [mc2|...] (--outputformat) fluence data output format:
                               mc2 - MCX mc2 format (binary 32bit float)
                               nii - Nifti format
                               hdr - Analyze 7.5 hdr/img format
                               tx3 - GL texture data for rendering (GL_RGBA32F)
 -O [X|XFEJP]  (--outputtype)  X - output flux, F - fluence, E - energy deposit
                               J - Jacobian (replay mode),   P - scattering
                               event counts at each voxel (replay mode only)

== User IO options ==
 -h            (--help)        print this message
 -v            (--version)     print MCX revision number
 -l            (--log)         print messages to a log file instead
 -i 	       (--interactive) interactive mode

== Debug options ==
 -D [0|int]    (--debug)       print debug information (you can use an integer
  or                           or a string by combining the following flags)
 -D [''|RMP]                   4 P  print progress bar
      combine multiple items by using a string, or add selected numbers together

== Additional options ==
 --atomic       [1|0]          1: use atomic operations; 0: do not use atomics
 --root         [''|string]    full path to the folder storing the input files
 --internalsrc  [0|1]          set to 1 to skip entry search to speedup launch
 --maxvoidstep  [1000|int]     maximum distance (in voxel unit) of a photon that
                               can travel before entering the domain, if 
                               launched outside (i.e. a widefield source)

== Example ==
example: (autopilot mode)
       mcxcl -A 1 -n 1e7 -f input.json -G 1
or (manual mode)
       mcxcl -t 16384 -T 64 -n 1e7 -f input.json -s test -r 2 -d 1 -b 1 -G 1
or (use multiple devices - 1st,2nd and 4th GPUs - together with equal load)
       mcxcl -A -n 1e7 -f input.json -G 1101 -W 10,10,10
or (use inline domain definition)
       mcxcl -f input.json -P '{"Shapes":[{"ZLayers":[[1,10,1],[11,30,2],[31,60,3]]}]}'
</pre>

 the above command will launch 1024 GPU threads (-t) with every 64 threads
 a block (-T); for each thread, it will simulate 1e7 photons (-n) and
 repeat only once (-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 utilize the 1st and 3rd Compute Units among the 4 total 
 devices present in the system (-G 1010); the list of CU can be found by mcxcl -L; 
 the workload partition between the two selected devices is 50:50 (-W); the simulation
 requires the relative/full path to the kernel source file mcx_core.cl (-k).

Currently, MCX supports a modified version of the input file format used 
for tMCimg. (The difference is that MCX allows comments)
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 0               # initial directional vector, 4th number is the focal-length, 0 for collimated beam, nan for isotropic
0.e+00 1.e-09 1.e-10 # time-gates(s): start, end, step
semi60x60x60.bin     # volume ('unsigned char' binary 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 MCXCL: 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. 

MCXCL 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.

---------------------------------------------------------------------------
IV. 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 description file, can be a binary or JSON file",
        "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::LengthUnit": "define the voxel length in mm, similar to --unitinmm",
        "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,fourierx,fourierx2d,
		   zgaussian,line,slit,pencilarray,pattern3d",
        "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,
	"LengthUnit": 1,
        "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": value 
to represent any parameter directly in the root level. For example

 {
    "Domain.VolumeFile": "semi60x60x60.json",
    "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:

  mcxcl -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.

---------------------------------------------------------------------------
V. 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:

 mcxcl -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.


---------------------------------------------------------------------------
VI. Using mcxlabcl in MATLAB and Octave

mcxlabcl is the native MEX version of MCX-CL for Matlab and GNU Octave. It includes
the entire MCX-CL code in a MEX function which can be called directly inside
Matlab or Octave. The input and output files in MCX-CL are replaced by convenient
in-memory struct variables in mcxlabcl, thus, making it much easier to use
and interact. Matlab/Octave also provides convenient plotting and data
analysis functions. With mcxlabcl, 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.

Specifically, please add the path to mcxlabcl.m and mcxcl.mex* to your
MATLAB or octave following Step 7 in Section II Installation.

To use mcxlabcl, the first step is to see if your system has any supported
processors. To do this, you can use one of the following 3 ways

 info=mcxlabcl('gpuinfo')

or run

 info=mcxlab('gpuinfo','opencl')

or

 USE_MCXCL=1
 info=mcxlab('gpuinfo')

Overall, mcxlabcl and mcxlab is highly compatible with nearly identical features
and interfaces. If yo have a working mcxlab script, the simpliest way to use it
with mcxlabcl on non-NVIDIA devices is to insert the below command

 eval('base','USE_MCXCL=1;');

at the begining of the script, and insert 

 eval('base','USE_MCXCL=0;');

at the end of the script.

If you have supported processors, please then run the demo mcxlabcl scripts
inside mcxlabcl/examples. mcxlabcl and mcxlab has a high compatibility in interfaces
and features. If you have a previously written MCXLAB script, you are likely able
to run it without modification when calling mcxlabcl. All you need to do
is to define

 USE_OPENCL=1
 
in matlab's base workspace (command line window). Alternatively, you may replace
mcxlab() calls by mcxlab(...,'opencl'), or by mcxlabcl().

Please make sure you select the fastest processor on your system by using the cfg.gpuid
field. 

---------------------------------------------------------------------------
VII. Using MCXStudio GUI

MCXStudio is a graphics user interface (GUI) for MCX/MCXCL and MMC. 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 MCXStudio, 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/mcxcl
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.


---------------------------------------------------------------------------
VIII. Interpreting the Output

MCX/MCX-CL output consists of two parts, the flux volume 
file and messages printed on the screen.

8.1 Output files

An mc2 file contains the fluence-rate distribution from the simulation in 
the given medium. By default, this fluence-rate 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'''  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

  mcxcl -f input.inp -s output ....

the fluence-rate 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 '''fluence-rate (Phi(r,t))''' is
measured by number of particles passing through an infinitesimal 
spherical surface per '''unit time''' at '''a given location''' regardless of directions.
The unit of the MCX output is "W/mm<sup>2 = J/(mm<sup>2</sup>s)", if it is interpreted as the 
"energy fluence-rate" [6], or "1/(mm<sup>2</sup>s)", if the output is interpreted as the 
"particle fluence-rate" [6].

The Green's function of the fluence-rate means that it 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 fluence-rate
snapshots, corresponding to the time-windows at [0 0.1] ns, 
[0.1 0.2]ns ... and [4.9,5.0] ns. To convert the fluence rate
to the fluence 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-dependent fluence-rate to continuous-wave (CW) 
fluence (fluence in short), you need to integrate the
fluence-rate along the time dimension. Assuming the fluence-rate after 
5 ns is negligible, then the CW fluence is simply sum(flux_i*0.1 ns, i=1,50). 
You can read <tt>mcx/examples/validation/plotsimudata.m</tt>
and <tt>mcx/examples/sphbox/plotresults.m</tt> for examples 
to compare an MCX output with the analytical fluence-rate/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

   fluence=loadmc2('output.mc2',[60 60 60 10],'float');
   cw_mcx=sum(fluence,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

MCX can also output "current density" (J(r,t), unit W/m^2, same as Phi(r,t)) -
referring to the expected number of photons or Joule of energy flowing
through a unit area pointing towards a particular direction per unit time.
The current density can be calculated at the boundary of the domain by two means:

1. using the detected photon partial path output (i.e. the second output of mcxlab.m),
one can compute the total energy E received by a detector, then one can
divide E by the area/aperture of the detector to obtain the J(r) at a detector
(E should be calculated as a function of t by using the time-of-fly of detected
photons, the E(t)/A gives J(r,t); if you integrate all time gates, the total E/A
gives the current I(r), instead of the current density).

2. use -X 1 or --saveref/cfg.issaveref option in mcx to enable the
diffuse reflectance recordings on the boundary. the diffuse reflectance
is represented by the current density J(r) flowing outward from the domain.

The current density has, as mentioned, the same unit as fluence rate,
but the difference is that J(r,t) is a vector, and Phi(r,t) is a scalar. Both measuring
the energy flow across a small area (the are has direction in the case of J) per unit
time.

You can find more rigorous definitions of these quantities in Lihong Wang's
Biomedical Optics book, Chapter 5.

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.

When a user specifies "-D P" in the command line, or set cfg.debuglevel='P',
MCXCL or MCXLABCL prints a progress bar showing the percentage of completition.

---------------------------------------------------------------------------
IX. Best practices guide

To maximize MCX-CL'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/mcxcl). 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-CL'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.

---------------------------------------------------------------------------
X. Acknowledgement

=== cJSON library by Dave Gamble ===

  Files included: mcx/src/cJSON folder

  Copyright (c) 2009 Dave Gamble

  Permission is hereby granted, free of charge, to any person obtaining a copy
  of this software and associated documentation files (the "Software"), to deal
  in the Software without restriction, including without limitation the rights
  to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
  copies of the Software, and to permit persons to whom the Software is
  furnished to do so, subject to the following conditions:

  The above copyright notice and this permission notice shall be included in
  all copies or substantial portions of the Software.

  THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
  THE SOFTWARE.
  
=== myslicer toolbox by Anders Brun ===

  Files included: mcx/utils/{islicer.m, slice3i.m, image3i.m}
  
  Copyright (c) 2009 Anders Brun, anders@cb.uu.se

  Redistribution and use in source and binary forms, with or without 
  modification, are permitted provided that the following conditions are 
  met:

  * Redistributions of source code must retain the above copyright 
  notice, this list of conditions and the following disclaimer. 
  * Redistributions in binary form must reproduce the above copyright 
  notice, this list of conditions and the following disclaimer in 
  the documentation and/or other materials provided with the distribution 
  * Neither the name of the Centre for Image Analysis nor the names 
  of its contributors may be used to endorse or promote products derived 
  from this software without specific prior written permission.

  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 
  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE 
  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE 
  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE 
  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR 
  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF 
  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS 
  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN 
  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) 
  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE 
  POSSIBILITY OF SUCH DAMAGE.


---------------------------------------------------------------------------
XI. Reference


[1] Leiming Yu, Fanny Nina-Paravecino, David Kaeli, and Qianqian Fang,
"Scalable and massively parallel Monte Carlo photon transport simulations \
for heterogeneous computing platforms," J. Biomed. Optics, 23(1), 010504 (2018) .

[2] 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 used MCX in your research, the authors of this software would like
you to cite the above paper in your related publications.

Links: 

[1] http://www.nvidia.com/object/cuda_get.html
[2] http://www.nvidia.com/object/cuda_learn_products.html
[3] http://en.wikipedia.org/wiki/Row-major_order
