Instead of concatening results to this, Is there other way to do the following, I mean the loop will persist but vector=[vector,sum(othervector)]; can be gotten in any other way?
vector=[];
while a - b ~= 0
othervector = sum(something') %returns a vector like [ 1 ; 3 ]
vector=[vector,sum(othervector)];
...
end
vector=vector./100
Well, this really depends on what you are trying to do. Starting from this code, you might need to think about the actions you are doing and if you can change that behavior. Since the snippet of code you present shows little dependencies (i.e. how are a, b, something and vector related), I think we can only present vague solutions.
I suspect you want to get rid of the code to circumvent the effect of constantly moving the array around by concatenating your new results into it.
First of all, just make sure that the slowest portion of your application is caused by this. Take a look at the Matlab profiler. If that portion of your code is not a major time hog, don't bother spending a lot of time on improving it (and just say to mlint to ignore that line of code).
If you can analyse your code enough to ensure that you have a constant number of iterations, you can preallocate your variables and prevent any performance penalty (i.e. write a for loop in the worst case, or better yet really vectorized code). Or if you can `factor out' some variables, this might help also (move any loop invariants outside of the loop). So that might look something like this:
vector = zeros(1,100);
while a - b ~= 0
othervector = sum(something);
vector(iIteration) = sum(othervector);
iIteration = iIteration + 1;
end
If the nature of your code doesn't allow this (e.g. you are iterating to attain convergence; in that case, beware of checking equality of doubles: always include a tolerance), there are some tricks you can perform to improve performance, but most of them are just rules of thumb or trying to make the best of a bad situation. In this last case, you might add some maintenance code to get slightly better performance (but what you gain in time consumption, you lose in memory usage).
Let's say, you expect the code to run 100*n iterations most of the time, you might try to do something like this:
iIteration = 0;
expectedIterations = 100;
vector = [];
while a - b ~= 0
if mod(iIteration,expectedIterations) == 0
vector = [vector zeros(1,expectedIterations)];
end
iIteration = iIteration + 1;
vector(iIteration) = sum(sum(something));
...
end
vector = vector(1:iIteration); % throw away uninitialized
vector = vector/100;
It might not look pretty, but instead of resizing the array every iteration, the array only gets resized every 100th iteration. I haven't run this piece of code, but I've used very similar code in a former project.
If you want to optimize for speed, you should preallocate the vector and have a counter for the index as #Egon answered already.
If you just want to have a different way of writing vector=[vector,sum(othervector)];, you could use vector(end + 1) = sum(othervector); instead.
Related
Is there anyway to improve the below codes? It takes much time in higher values. I'd be appreciated if a solution for improving be advised.
term3=0;
ngrain=70;
etas = rand(512*512,70);
glist = round(1,70);
en = zeros(1,70);
for i=1:70
en(i)=0.493;
end
for igrain=1:ngrain
for jgr=1:ngrain+nrex
if(glist(jgr)== 1)
den(igrain,jgr)=en(jgr)-en(igrain);
term3=term3-8/pi*(etas(:,igrain).*etas(:,jgr)).^0.5*den(igrain,jgr);
end
end
end
please be more precise in the future. Here are a couple of hints to improve your coding:
Building a vector out of constants can be achieved in several ways (unfortunately, you picked the least efficient one):
a for-loop (with or without pre-allocation of memory, you did allocate, so that is good!)
use 1 * constant => ones(1,70)*0.493
repeat the 1x1 matrix: repmat() => repmat(1,70,0.493) (this is the most efficient approach, you may check this using tic() and toc() )
Anyway, this wasn't your bottleneck.
You can do logical comparisons directly instead of looping and using if:
lg = glist == 1;
idx = find(lg);
for i = 1:length(idx)
jgr = idx(i)
end
Note that your example does not work because nrex is not defined. It would also be great if you format your code nicer (consistent spacing, indention, a naming that is a bit more intuitive)
So, I am aware that there are a number of other posts about eliminating for loops but I still haven't been able to figure this out.
I am looking to rewrite my code so that it has fewer for loops and runs a little faster. The code describes an optics problem calculating the intensity of different colors after the light has propagated through a medium. I have already gotten credit for this assignment but I would like to learn of better ways than just throwing in for loops all over the place. I tried rewriting the innermost loop using recursion which worked and looked nice but was a little slower.
Any other comments/improvements are also welcome.
Thanks!
n_o=1.50;
n_eo=1.60;
d=20e-6;
N_skiv=100;
lambda=[650e-9 510e-9 475e-9];
E_in=[1;1]./sqrt(2);
alfa=pi/2/N_skiv;
delta=d/N_skiv;
points=100;
int=linspace(0,pi/2,points);
I_ut=zeros(3,points);
n_eo_theta=#(theta)n_eo*n_o/sqrt(n_o^2*cos(theta)^2+n_eo^2*sin(theta)^2);
hold on
for i=1:3
for j=1:points
J_last=J_pol2(0);
theta=int(j);
for n=0:N_skiv
alfa_n=alfa*n;
J_last=J_ret_uppg2(alfa_n, delta , n_eo_theta(theta) , n_o , lambda(i) ) * J_last;
end
E_ut=J_pol2(pi/2)*J_last*E_in;
I_ut(i,j)=norm(E_ut)^2;
end
end
theta_grad=linspace(0,90,points);
plot(theta_grad,I_ut(1,:),'r')
plot(theta_grad,I_ut(2,:),'g')
plot(theta_grad,I_ut(3,:),'b')
And the functions:
function matris=J_proj(alfa)
matris(1,1)=cos(alfa);
matris(1,2)=sin(alfa);
matris(2,1)=-sin(alfa);
matris(2,2)=cos(alfa);
end
function matris=J_pol2(alfa)
J_p0=[1 0;0 0];
matris=J_proj(-alfa)*J_p0*J_proj(alfa);
end
function matris=J_ret_uppg2(alfa_n,delta,n_eo_theta,n_o,lambda)
k0=2*pi/lambda;
J_r0_u2(1,1)=exp(1i*k0*delta*n_eo_theta);
J_r0_u2(2,2)=exp(1i*k0*n_o*delta);
matris=J_proj(-alfa_n)*J_r0_u2*J_proj(alfa_n);
end
Typically you cannot get rid of a for-loop if you are doing a calculation that depends on a previous answer, which seems to be the case with the J_last-variable.
However I saw at least one possible improvement with the n_eo_theta inline-function, instead of doing that calculation 100 times, you could instead simply change this line:
n_eo_theta=#(theta)n_eo*n_o/sqrt(n_o^2*cos(theta)^2+n_eo^2*sin(theta)^2);
into:
theta_0 = 1:100;
n_eo_theta=n_eo*n_o./sqrt(n_o^2*cos(theta_0).^2+n_eo^2*sin(theta_0).^2);
This would run as is, although you should also want to remove the variable "theta" in the for-loop. I.e. simply change
n_eo_theta(theta)
into
n_eo_theta(j)
The way of using the "." prefix in the calculations is the furthermost tool for getting rid of for-loops (i.e. using element-wise calculations). For instance; see element-wise multiplication.
You can use matrices!!!!
For example, you have the statement:
theta=int(j)
which is inside a nested loop. You can replace it by:
theta = [int(1:points);int(1:points);int(1:points)];
or:
theta = int(repmat((1:points), 3, 1));
Then, you have
alfa_n=alfa * n;
you can replace it by:
alfa_n = alfa .* (0:N_skiv);
And have all the calculation done in a row like fashion. That means, instead looping, you will have the values of a loop in a row. Thus, you perform the calculations at the rows using the MATLAB's functionalities and not looping.
I am running a very large meta-simulation where I go through two hyperparameters (lets say x and y) and for each set of hyperparameters (x_i & y_j) I run a modest sized subsimulation. Thus:
for x=1:I
for y=1:j
subsimulation(x,y)
end
end
For each subsimulation however, about 50% of the data is common to every other subsimulation, or subsimulation(x_1,y_1).commondata=subsimulation(x_2,y_2).commondata.
This is very relevant since so far the total simulation results file size is ~10Gb! Obviously, I want to save the common subsimulation data 1 time to save space. However, the obvious solution, being to save it in one place would screw up my plotting function, since it directly calls subsimulation(x,y).commondata.
I was wondering whether I could do something like
subsimulation(x,y).commondata=% pointer to 1 location in memory %
If that cant work, what about this less elegant solution:
subsimulation(x,y).commondata='variable name' %string
and then adding
if(~isstruct(subsimulation(x,y).commondata)),
subsimulation(x,y).commondata=eval(subsimulation(x,y).commondata)
end
What solution do you guys think is best?
Thanks
DankMasterDan
You could do this fairly easily by defining a handle class. See also the documentation.
An example:
classdef SimulationCommonData < handle
properties
someData
end
methods
function this = SimulationCommonData(someData)
% Constructor
this.someData = someData;
end
end
end
Then use like this,
commonData = SimulationCommonData(something);
subsimulation(x, y).commondata = commonData;
subsimulation(x, y+1).commondata = commonData;
% These now point to the same reference (handle)
As per my comment, as long as you do not modify the common data, you can pass it as third input and still not copy the array in memory on each iteration (a very good read is Internal Matlab memory optimizations). This image will clarify:
As you can see, the first jump in memory is due to the creation of common and the second one to the allocation of the output c. If the data were copied on each iteration, you would have seen many more memory fluctuations. For instance, a third jump, then a decrease, then back up again and so on...
Follows the code (I added a pause in between each iteration to make it clearer that no big jumps occur during the loop):
function out = foo(a,b,common)
out = a+b+common;
end
for ii = 1:10; c = foo(ii,ii+1,common); pause(2); end
I have two lists of timestamps and I'm trying to create a map between them that uses the imu_ts as the true time and tries to find the nearest vicon_ts value to it. The output is a 3xd matrix where the first row is the imu_ts index, the third row is the unix time at that index, and the second row is the index of the closest vicon_ts value above the timestamp in the same column.
Here's my code so far and it works, but it's really slow. I'm not sure how to vectorize it.
function tmap = sync_times(imu_ts, vicon_ts)
tstart = max(vicon_ts(1), imu_ts(1));
tstop = min(vicon_ts(end), imu_ts(end));
%trim imu data to
tmap(1,:) = find(imu_ts >= tstart & imu_ts <= tstop);
tmap(3,:) = imu_ts(tmap(1,:));%Use imu_ts as ground truth
%Find nearest indecies in vicon data and map
vic_t = 1;
for i = 1:size(tmap,2)
%
while(vicon_ts(vic_t) < tmap(3,i))
vic_t = vic_t + 1;
end
tmap(2,i) = vic_t;
end
The timestamps are already sorted in ascending order, so this is essentially an O(n) operation but because it's looped it runs slowly. Any vectorized ways to do the same thing?
Edit
It appears to be running faster than I expected or first measured, so this is no longer a critical issue. But I would be interested to see if there are any good solutions to this problem.
Have a look at knnsearch in MATLAB. Use cityblock distance and also put an additional constraint that the data point in vicon_ts should be less than its neighbour in imu_ts. If it is not then take the next index. This is required because cityblock takes absolute distance. Another option (and preferred) is to write your custom distance function.
I believe that your current method is sound, and I would not try and vectorize any further. Vectorization can actually be harmful when you are trying to optimize some inner loops, especially when you know more about the context of your data (e.g. it is sorted) than the Mathworks engineers can know.
Things that I typically look for when I need to optimize some piece of code liek this are:
All arrays are pre-allocated (this is the biggest driver of performance)
Fast inner loops use simple code (Matlab does pretty effective JIT on basic commands, but must interpret others.)
Take advantage of any special data features that you have, e.g. use sort appropriate algorithms and early exit conditions from some loops.
You're already doing all this. I recommend no change.
A good start might be to get rid of the while, try something like:
for i = 1:size(tmap,2)
C = max(0,tmap(3,:)-vicon_ts(i));
tmap(2,i) = find(C==min(C));
end
I am working on a big Matlab testbench with thousands of lines of code, and I am trying to optimize the most time-consuming routines, determined via the profiler in Matlab.
I noticed that one of those most time-consuming operations is the following:
list = list((list(:,1) >= condxMin) & (list(:,1) <= condxMax) & (list(:,2) >= condyMin) & (list(:,2) <= condyMax),:);
Concretely, I have a big list of coordinates (50000 x 2 at least) and I want to restrict the values of this list so as to keep only the points that verify both of these conditions :
list(:,1) must be within [condxMin, condxMax] and list(:2) within [condyMin condyMax].
I was wondering if there was a more efficient way to do it, considering that this line of code is already vectorized.
Also, I am wondering if Matlab does a short-circuiting or not. If it doesn't, then I don't think there is a way to do it without breaking the vectorization and do it with a while loop, where I would write instead something like this:
j=1;
for i=1:size(list,1)
if(cond1 && cond2 && cond3 && cond4)
newlist(j,1:2) = list(i,1:2);
j=j+1;
end
end
Thank you in advance for your answer :)
Looks like the original vectorized version is the fastest way I can find, barring any really clever ideas. Matlab does do short circuiting, but not for matrices. The loop implementation you you showed would be very slow, since you're not pre-allocating (nor are you able to pre-allocate the full matrix).
I tried a couple of variations on this, including a for loop which used a short circuited && to determine whether the index was bad or not, but no such luck. On the plus side, the vectorized version you've got runs at 0.21s for a 5 million element coordinate list.