The source of many "out of memory" problems often involves analyzing or processing an existing large set of data such as in a file or a database. This requires bringing all or part of the data set into the MATLAB® software process. The following techniques deal with minimizing the required memory during this stage.
Only import into MATLAB as much of a large data set as you need for the problem you are trying to solve. This is not usually a problem when importing from sources such as a database, where you can explicitly search for elements matching a query. But this is a common problem with loading large flat text or binary files. Rather than loading the entire file, use the appropriate MATLAB function to load parts of files.
|File Type||Partial Loading|
Load part of a variable by indexing into an object that
you create with the
You can use low-level binary file I/O functions, such
|Image, HDF, Audio, and Video|
Many of the MATLAB functions that support loading from these types of files allow you to select portions of the data to read. For details, see the function reference pages listed in Supported File Formats for Import and Export.
Consider block processing, that is, processing a large data set one section at a time in a loop. Reducing the size of the largest array in a data set reduces the size of any copies or temporaries needed. You can use this technique in either of two ways:
For a subset of applications that you can break into separate chunks and process independently.
For applications that only rely on the state of a previous block, such as filtering.
Avoid creating large temporary variables, and also make it a practice to clear
those temporary variables you do use when they are no longer needed. For
example, when you create a large array of zeros, instead of saving to a
A, and then converting
A to a single:
A = zeros(1e6,1); As = single(A);
use just the one command to do both operations:
A = zeros(1e6,1,'single');
When working with large data sets, be aware that MATLAB makes a temporary copy of an input variable if the called function modifies its value. This temporarily doubles the memory required to store the array, which causes MATLAB to generate an error if sufficient memory is not available.
One way to use less memory in this situation is to use nested functions. A
nested function shares the workspace of all outer functions, giving the nested
function access to data outside of its usual scope. In the example shown here,
setrowval has direct access to the workspace
of the outer function
myfun, making it unnecessary to pass a
copy of the variable in the function call. When
modifies the value of
A, it modifies it in the workspace of
the calling function. There is no need to use additional memory to hold a
separate array for the function being called, and there also is no need to
return the modified value of
function myfun A = magic(500); function setrowval(row, value) A(row,:) = value; end setrowval(400, 0); disp('The new value of A(399:401,1:10) is') A(399:401,1:10) end
MATLAB provides you with different sizes of data classes, such as
uint8, so you do not need to
use large classes to store your smaller segments of data. For example, it takes 7 KB
less memory to store 1,000 small unsigned integer values using the
uint8 class than it does with
The numeric class you should use in MATLAB depends on your intended actions. The default class
double gives the best precision, but requires 8 bytes per
element of memory to store. If you intend to perform complicated math such as
linear algebra, you must use a floating-point class such as a
single class requires only 4 bytes. There are some
limitations on what you can do with the
single class, but
most MATLAB Math operations are supported.
If you just need to carry out simple arithmetic and you represent the original data as integers, you can use the integer classes in MATLAB. The following is a list of numeric classes, memory requirements (in bytes), and the supported operations.
|Class (Data Type)||Bytes||Supported Operations|
|1||Arithmetic and some simple functions|
|2||Arithmetic and some simple functions|
|4||Arithmetic and some simple functions|
|8||Arithmetic and some simple functions|
MATLAB arrays (implemented internally as
require room to store meta information about the data in memory, such as type,
dimensions, and attributes. This takes about 80 bytes per array. This overhead
only becomes an issue when you have a large number (e.g., hundreds or thousands)
mxArrays (e.g., scalars). The
whos command lists the memory
used by variables, but does not include this overhead.
Because simple numeric arrays (comprising one
the least overhead, you should use them wherever possible. When data is too
complex to store in a simple array (or matrix), you can use other data
Cell arrays are comprised of separate
mxArrays for each
element. As a result, cell arrays with many small elements have a large
Structures require a similar amount of overhead per field (see Array Headers). Structures with many fields and small contents have a large overhead and should be avoided. A large array of structures with numeric scalar fields requires much more memory than a structure with fields containing large numeric arrays.
Also note that while MATLAB stores numeric arrays in contiguous memory, this is not the case for structures and cell arrays.
When reading data from a binary file with
fread, it is a common error to
specify only the class of the data in the file, and not the class of the data
MATLAB uses once it is in the workspace. As a result, the default
double is used even if you are reading only 8-bit values.
fid = fopen('large_file_of_uint8s.bin', 'r'); a = fread(fid, 1e3, 'uint8'); % Requires 8k whos a Name Size Bytes Class Attributes a 1000x1 8000 double a = fread(fid, 1e3, 'uint8=>uint8'); % Requires 1k whos a Name Size Bytes Class Attributes a 1000x1 1000 uint8
If your data contains many zeros, consider using sparse arrays, which store only nonzero elements. The following example compares the space required for storage of an array of mainly zeros:
A = eye(1000); % Full matrix with ones on the diagonal As = sparse(A); % Sparse matrix with only nonzero elements whos Name Size Bytes Class Attributes A 1000x1000 8000000 double As 1000x1000 24008 double sparse
You can see that this array requires only approximately 4 KB to be stored as
sparse, but approximately 8 MB as a full matrix. In general, for a sparse double
nnz nonzero elements and
columns, the memory required is
nnz + 8 *
ncol + 8 bytes
(on a 64-bit machine)
nnz + 4 *
ncol + 4 bytes
(on a 32-bit machine)
Note that MATLAB does not support all mathematical operations on sparse arrays.
MATLAB always uses a contiguous segment of memory to store a numeric array. As you manipulate this data, however, the contiguous block can become fragmented. When memory is fragmented, there might be plenty of free space, but not enough contiguous memory to store a new large variable. Increasing fragmentation can use significantly more memory than is necessary.
In the course of a MATLAB session, memory can become fragmented due to dynamic memory
allocation and deallocation.
while loops that incrementally
increase, or grow, the size of a data structure each time
through the loop can add to this fragmentation as they have to repeatedly find
and allocate larger blocks of memory to store the data.
To make more efficient use of your memory, preallocate a block of memory large enough to hold the matrix at its final size before entering the loop. When you preallocate memory for an array, MATLAB reserves sufficient contiguous space for the entire full-size array at the beginning of the computation. Once you have this space, you can add elements to the array without having to continually allocate new space for it in memory.
For more information on preallocation, see Preallocation.
MATLAB uses a heap method of memory management. It requests memory from the operating system when there is not enough memory available in the heap to store the current variables. It reuses memory as long as the size of the memory segment required is available in the heap.
The following statements can require approximately 4.3 MB of RAM. This is because MATLAB might not be able to reuse the space previously occupied by two 1 MB arrays when allocating space for a 2.3 MB array:
a = rand(1e6,1); b = rand(1e6,1); clear c = rand(2.3e6,1);
The simplest way to prevent overallocation of memory is to allocate the largest vectors first. These statements require only about 2.0 MB of RAM:
c = rand(2.3e6,1); clear a = rand(1e6,1); b = rand(1e6,1);
On 32-bit Microsoft® Windows®, the workspace of MATLAB can fragment over time due to the fact that the Windows memory manager does not return blocks of certain types and sizes to the operating system. Clearing the MATLAB workspace does not fix this problem. You can minimize the problem by allocating the largest variables first. This cannot address, however, the eventual fragmentation of the workspace that occurs from continual use of MATLAB over many days and weeks, for example. The only solution to this is to save your work and restart MATLAB.
pack command, which saves all
variables to disk and loads them back, does not help with this situation.
One simple way to increase the amount of memory you have available is to clear large arrays that you no longer use.
If your program generates very large amounts of data, consider writing the
data to disk periodically. After saving that portion of the data, use the
clear function to remove the
variable from memory and continue with the data generation.
When you are working with a very large data set repeatedly or interactively, clear the old variable first to make space for the new variable. Otherwise, MATLAB requires temporary storage of equal size before overriding the variable. For example,
a = rand(100e6,1) % 800 MB array b = rand(100e6,1) % New 800 MB array Error using rand Out of memory. Type HELP MEMORY for your options. clear a a = rand(100e6,1) % New 800 MB array