GPU required specifications

[Eidantz]

Hey, I try to run this code:

clear cfg;
cfg.unitinmm=0.1; %set the grid to 100um
cfg.nphoton=1e7;

prop=[ 0.019 7.8   0.89 1.37     % medium 1: skin & skull
   0.004 0.009 0.89 1.37     % medium 2: CSF
   0.02  9.0   0.89 1.37     % medium 3: gray matter
   0.08 40.9   0.84 1.37    % medium 4: white matter
   ];

cfg.prop=[0 0 1 1];
% define layer structure
cfg.vol=ones(500,500,1500);
j=15;
for i=1:100 % define 100 layers with random brain optical properties
cfg.vol(:,:,j:j+1)=i;
j=j+15;
randomm=randi([1,4]);
cfg.prop=[cfg.prop;prop(randomm,:)];
end
cfg.vol=uint8(cfg.vol);

% define the source position
cfg.srcpos=[200,250,0]+1;
cfg.srcdir=[0 0 1];

% time-domain simulation parameters
cfg.tstart=0;
cfg.tend=5e-9;
cfg.tstep=5e-10;

% GPU thread configuration
cfg.autopilot=1;
cfg.gpuid=1;

%% running simulation with boundary reflection enabled
cfg.isreflect=1; % enable reflection at exterior boundary
cfg.isrefint=1;  % enable reflection at interior boundary too
cfg.issavedet=1; % enable recording partial pathlength of detected photons
cfg.detpos=[101 251 1 2];
for i=1:1
tic;
[f,det,vol,seeds]=mcxlab(cfg);
toc;
end
fprintf(sprintf('simulation number: %d is done..............................',i));

And I got this warning and error.

WARNING: GPU memory can not hold all time gates, disabling normalization to allow multiple runs
MCXLAB ERROR -2 in unit mcx_core.cu:1756: out of memory
Error from thread (0): out of memory
C++ Error: MCXLAB Terminated due to an exception!
Elapsed time is 2.533999 seconds.

I know my GPU is not that strong (GEFORCE 930MX) and I am willing to upgrade my GPU but what are the required specifications of the GPU to run simulations like this properly

[Eidantz]

I saw the list: http://mcx.space/gpubench/
Sorry for bothering you
Eidan.

[fangq]

The estimation of the total memory needed by an MCX simulation is straightforward, from your script, you requested a volume of 500x500x1500 voxels, and a total of 10 time-gates, each voxel uses a 4-byte single-precision number to store fluence. if you convert this to bytes, you need 50050015004/1024/102410 = 14 GB global memory. In addition, MCX need twice of that size internally to improve accuracy, so, your simulation requires 14*2 = 28GB GPU memory.

if you look at all the GPUs on the market, you can almost never find a GPU with such amount of memory, except the super expensive v100 GPU. So, your simulation is just not realistic.

Even you can find a GPU with such a large memory, to produce a fluence map that every voxel has decent SNR will require a huge amount of photons. The runtime will also be unrealistic. Think about this, if you run a simulation on a grid with 100x100x100 voxels, if you refine the grid to 200x200x200, you will need 64 times more run-times to produce a simulation with comparable SNR - 8 fold comes from the increase number of voxels, and another 8-fold comes from launching 8x more photons to produce the same SNR.

My suggestion is always: start with a small domain, run a small number of photons, and see if your solution is acceptable, then gradually increase the photon number or domain size. Do not go the other way around.