Writing Fast Matlab Code #4: JIT Acceleration
Matlab 6.5 (R13) and later feature the Just-In-Time (JIT) Accelerator for improving the speed of M-functions, particularly with loops. By knowing a few things about the accelerator, you can improve its performance.
The JIT Accelerator is enabled by default. To disable it, type “feature accel off” in the console, and “feature accel on” to enable it again.
As of Matlab R2008b, only a subset of the Matlab language is supported for acceleration. Upon encountering an unsupported feature, acceleration processing falls back to non-accelerated evaluation. Acceleration is most effective when significant contiguous portions of code are supported.
- Data types: Code must use supported data types for acceleration: double (both real and complex), logical, char, int8–32, uint8–32. Some struct, cell, classdef, and function handle usage is supported. Sparse arrays are not accelerated.
- Array shapes: Array shapes of any size with 3 or fewer dimensions are supported. Changing the shape or data type of an array interrupts acceleration. A few limited situations with 4D arrays are accelerated.
- Function calls: Calls to built-in functions and M-functions are accelerated. Calling MEX func- tions and Java interrupts acceleration. (See also page 14 on inlining simple functions.)
- Conditionals and loops: The conditional statements if, elseif, and simple switch statements are supported if the conditional expression evaluates to a scalar. Loops of the form for k=a:b, for k=a:b:c, and while loops are accelerated if all code within the loop is supported.
In-place computation
Introduced in Matlab 7.3 (R2006b), the element-wise operators (+, .*, etc.) and some other functions can be computed in-place. That is, a computation like
x = 5*sqrt(x.ˆ2 + 1);
is handled internally without needing temporary storage for accumulating the result. An M-function can also be computed in-place if its output argument matches one of the input arguments.
x = myfun(x); function x = myfun(x) x = 5*sqrt(x.ˆ2 + 1); return;
To enable in-place computation, the in-place operation must be within an M-function (and for an in- place function, the function itself must be called within an M-function). Currently, there is no support for in-place computation with MEX-functions.
Multithreaded Computation
Matlab 7.4 (R2007a) introduced multithreaded computation for multicore and multiprocessor com- puters. Multithreaded computation accelerates some per-element functions when applied to large arrays (for example, .^, sin, exp) and certain linear algebra functions in the BLAS library. To enable it, select File → Preferences → General → Multithreading and select “Enable multithreaded computation.” Fur- ther control over parallel computation is possible with the Parallel Computing Toolbox using parfor and spmd.
JIT-Accelerated Example
For example, the following loop-heavy code is supported for acceleration:
function B = bilateral(A,sd,sr,R) % The bilateral image denoising filter B = zeros(size(A)); for i = 1:size(A,1) for j = 1:size(A,2) zsum = 0; for m = -R:R if i+m >= 1 && i+m <= size(A,1) for n = -R:R if j+n >= 1 && j+n <= size(A,2) z = exp(-(A(i+m,j+n)-A(i,j))^2/(2*sd^2))... *exp(-(m^2 + n^2)/(2*sr^2)); zsum = zsum + z; B(i,j) = B(i,j) + z*A(i+m,j+n); end end end end end end B(i,j) = B(i,j)/zsum;
For a 128 × 128 input image and R = 3, the run time is 53.3 seconds without acceleration and 0.68 seconds with acceleration.
function B = bilateral(A,sd,sr,R)
% The bilateral image denoising filter
B = zeros(size(A));
for i = 1:size(A,1)
for j = 1:size(A,2)
zsum = 0;
for m = -R:R
if i+m >= 1 && i+m <= size(A,1)
for n = -R:R
if j+n >= 1 && j+n <= size(A,2)
z = exp(-(A(i+m,j+n)-A(i,j))^2/(2*sd^2))…
*exp(-(m^2 + n^2)/(2*sr^2));
zsum = zsum + z;
B(i,j) = B(i,j) + z*A(i+m,j+n);
end
end
end
end
end
end
B(i,j) = B(i,j)/zsum;
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