sprintf vs. compose performance for large arrays
My understanding is that compose is based on sprintf, yet I’m noticing a huge discrepancy in performance. Consider the following example.
M = 32;
N = 32;
A = randn(M,N);
[m,n] = ndgrid(1:M,1:N);
Nrep = 100;
%% compose and no for loops
t1 = zeros(Nrep,1);
for kr=1:Nrep
tic;
s1 = compose("%d,%d,%.3g",m(:),n(:),A(:));
s1 = reshape(s1,M,N);
t1(kr) = toc;
end
%% sprintf and two nested for loops
t2 = zeros(Nrep,1);
for kr=1:Nrep
s2 = repelem("",M,N);
tic;
for j=1:N
for i=1:M
s2(i,j) = sprintf("%d,%d,%.3g",i,j,A(i,j));
end
end
t2(kr) = toc;
end
%% compose and two nested for loops
t3 = zeros(Nrep,1);
for kr=1:Nrep
tic;
s3 = repelem("",M,N);
for j=1:N
for i=1:M
s3(i,j) = compose("%d,%d,%.3g",i,j,A(i,j));
end
end
t3(kr) = toc;
end
fprintf(" min | mean | maxn");
fprintf("compose, no loops: %.6f | %.6f | %.6fn",min(t1),mean(t1),max(t1));
fprintf("sprintf, 2 loops: %.6f | %.6f | %.6fn",min(t2),mean(t2),max(t2));
fprintf("compose, 2 loops: %.6f | %.6f | %.6fn",min(t3),mean(t3),max(t3));
This code produces the following output on my machine.
min | mean | max
compose, no loops: 1.008151 | 1.035783 | 1.076671
sprintf, 2 loops: 0.004265 | 0.004384 | 0.008990
compose, 2 loops: 1.018900 | 1.050923 | 1.464713
It seems that sprintf is 60x – 70x faster in this example. Any idea why that is?
Here is the output of ver on my machine:
—————————————————————————————–
MATLAB Version: 25.1.0.2943329 (R2025a)
MATLAB License Number:
Operating System: macOS Version: 15.5 Build: 24F74
Java Version: Java 11.0.27+6-LTS with Amazon.com Inc. OpenJDK 64-Bit Server VM mixed mode
—————————————————————————————–My understanding is that compose is based on sprintf, yet I’m noticing a huge discrepancy in performance. Consider the following example.
M = 32;
N = 32;
A = randn(M,N);
[m,n] = ndgrid(1:M,1:N);
Nrep = 100;
%% compose and no for loops
t1 = zeros(Nrep,1);
for kr=1:Nrep
tic;
s1 = compose("%d,%d,%.3g",m(:),n(:),A(:));
s1 = reshape(s1,M,N);
t1(kr) = toc;
end
%% sprintf and two nested for loops
t2 = zeros(Nrep,1);
for kr=1:Nrep
s2 = repelem("",M,N);
tic;
for j=1:N
for i=1:M
s2(i,j) = sprintf("%d,%d,%.3g",i,j,A(i,j));
end
end
t2(kr) = toc;
end
%% compose and two nested for loops
t3 = zeros(Nrep,1);
for kr=1:Nrep
tic;
s3 = repelem("",M,N);
for j=1:N
for i=1:M
s3(i,j) = compose("%d,%d,%.3g",i,j,A(i,j));
end
end
t3(kr) = toc;
end
fprintf(" min | mean | maxn");
fprintf("compose, no loops: %.6f | %.6f | %.6fn",min(t1),mean(t1),max(t1));
fprintf("sprintf, 2 loops: %.6f | %.6f | %.6fn",min(t2),mean(t2),max(t2));
fprintf("compose, 2 loops: %.6f | %.6f | %.6fn",min(t3),mean(t3),max(t3));
This code produces the following output on my machine.
min | mean | max
compose, no loops: 1.008151 | 1.035783 | 1.076671
sprintf, 2 loops: 0.004265 | 0.004384 | 0.008990
compose, 2 loops: 1.018900 | 1.050923 | 1.464713
It seems that sprintf is 60x – 70x faster in this example. Any idea why that is?
Here is the output of ver on my machine:
—————————————————————————————–
MATLAB Version: 25.1.0.2943329 (R2025a)
MATLAB License Number:
Operating System: macOS Version: 15.5 Build: 24F74
Java Version: Java 11.0.27+6-LTS with Amazon.com Inc. OpenJDK 64-Bit Server VM mixed mode
—————————————————————————————– My understanding is that compose is based on sprintf, yet I’m noticing a huge discrepancy in performance. Consider the following example.
M = 32;
N = 32;
A = randn(M,N);
[m,n] = ndgrid(1:M,1:N);
Nrep = 100;
%% compose and no for loops
t1 = zeros(Nrep,1);
for kr=1:Nrep
tic;
s1 = compose("%d,%d,%.3g",m(:),n(:),A(:));
s1 = reshape(s1,M,N);
t1(kr) = toc;
end
%% sprintf and two nested for loops
t2 = zeros(Nrep,1);
for kr=1:Nrep
s2 = repelem("",M,N);
tic;
for j=1:N
for i=1:M
s2(i,j) = sprintf("%d,%d,%.3g",i,j,A(i,j));
end
end
t2(kr) = toc;
end
%% compose and two nested for loops
t3 = zeros(Nrep,1);
for kr=1:Nrep
tic;
s3 = repelem("",M,N);
for j=1:N
for i=1:M
s3(i,j) = compose("%d,%d,%.3g",i,j,A(i,j));
end
end
t3(kr) = toc;
end
fprintf(" min | mean | maxn");
fprintf("compose, no loops: %.6f | %.6f | %.6fn",min(t1),mean(t1),max(t1));
fprintf("sprintf, 2 loops: %.6f | %.6f | %.6fn",min(t2),mean(t2),max(t2));
fprintf("compose, 2 loops: %.6f | %.6f | %.6fn",min(t3),mean(t3),max(t3));
This code produces the following output on my machine.
min | mean | max
compose, no loops: 1.008151 | 1.035783 | 1.076671
sprintf, 2 loops: 0.004265 | 0.004384 | 0.008990
compose, 2 loops: 1.018900 | 1.050923 | 1.464713
It seems that sprintf is 60x – 70x faster in this example. Any idea why that is?
Here is the output of ver on my machine:
—————————————————————————————–
MATLAB Version: 25.1.0.2943329 (R2025a)
MATLAB License Number:
Operating System: macOS Version: 15.5 Build: 24F74
Java Version: Java 11.0.27+6-LTS with Amazon.com Inc. OpenJDK 64-Bit Server VM mixed mode
—————————————————————————————– string, strings, sprintf, compose, text MATLAB Answers — New Questions
