Shared Matrix in Matlab

A MEX-C library for creating or reading matrix using shared memory (parpool supports) to reduce unnecessary copies of memory.

(Be care for all the computations, because it may crash Matlab if any improper accesses are made)

Environments

Supported environments

Windows: MSVC Compiler (using WIN API), WIN64 Matlab
Linux: GCC Compiler (using POSIX API), GLNXA64 Matlab

Tested environments

Windows: R2018b + VS 2019
Linux: R2017b + GCC 7.5.0, R2018b + GCC 7.5.0

Usage

Functionality

Matlab version	R2017b and earlier	R2018a and later
Normal array	✓	✓
Sparse array	✓	✓
Complex array	✗	✓
Complex sparse array	✗	✓

NOTE: Interleaved complex API is used for accessing complex array, it is introduced in R2018a. Hence R2017b or earlier Matlab are unavailable (because the internal API mxGet/SetPr/Pi requires unavoidable memory copying when handling complex data).

Compile

Running compile in Matlab shell.

Example

A full example showing how this works on parallel computing:

a = randn(32*4096, 4096);  % creating a large (4GB) matrix
host = shared_matrix_host(a);  % put it into shared memory, this variable is transfer to parpool process via matlab internal serialization method
sum_a = sum(a(:));  % just for simple verification
clear a  % the original variable can be removed now

sum_vector = zeros(4096, 1);
parfor i = 1:4096
    accessor = host.attach();  % attach this memory within parpool process, this variable differs from other parpool processes
    data_matrix = accessor.get_data();  % gets the real matrix
    % do parallel computation using big data matrix
    % NOTICE: ONLY SUPPORTS READ
    sum_vector(i) = sum(data_matrix(:, i));
    % don't forget to clean it up
    accessor.detach();
end

% detach host memory
host.detach();
sum_b = sum(sum_vector);

% have a comparison
sum_a
sum_b

In general, this example only uses 4GB memory (8GB peak when copying). The traditional method (Matlab provided) requires 4*(n_workers+1) GB memory, where n_workers is the number of CPU cores:

a = randn(32*4096, 4096);  % creating a large (4GB) matrix
sum_a = sum(a(:));  % just for simple verification

sum_vector = zeros(4096, 1);
parfor i = 1:4096
    % accessing large matrix within parfor will cost massive memory usage, because every worker keeps a copy of this large matrix
    sum_vector(i) = sum(a(:, i));
end

sum_b = sum(sum_vector);

% have a comparison
sum_a
sum_b

A more simple way to use:

a = randn(16*4096, 4096);
b = randn(16*4096, 4096);
% ... more arguments
host = create_shmat(a, b); % create a MATLAB-struct holding all host memory
sum_a = sum(a(:));
sum_b = sum(b(:));
clear a

sum_vector = zeros(4096, 1);
parfor i = 1:4096
    [dev, data] = attach_shmat(host); % equivalent to call .attach() and .get_data(), all data are stored in "data" struct
    sum_vector(i) = sum(data.a(:, i)) + sum(data.b(:, i));
    detach_shmat(dev); % equivalent to call .detach()
end
detach_shmat(host);

% comparison
disp(sum_a + sum_b);
disp(sum(sum_vector));

Notice

For Linux users, make sure the usable size of /dev/shm is capable for the matrix.

Citation and license

This repository is licensed under GNU GPLv3, all rights reserved.

If this code is helpful for your research, please consider citing this repository.

@misc{zhou_shmat21,
  author = {Xuebin Zhou},
  title = {Shared Matrix in Matlab},
  url = {https://github.com/qhgz2013/shared_matrix},
  year = {2021}
}