For fun (and science) I tried out the new package
https://github.com/IntelLabs/ParallelAccelerator.jl for this problem.
Here is the code:
function Jakes_Flat( fd, Ts, Ns, t0 = 0, E0 = 1, phi_N = 0 )
# Inputs:
#
# Outputs:
N0 = 8; # As suggested by Jakes
N = 4*N0+2; # An accurate approximation
wd = 2*pi*fd; # Maximum Doppler frequency
ts = collect(t0 + (0:Ns-1)*Ts)
tf = ts[end] + Ts;
Ns = collect(1:N0)
coswt = [ cosvec(ts, wd)'; cosmat(ts, Ns, wd, N) ]
h = E0/sqrt(2*N0+1)*exp(im*[ phi_N pi/(N0+1)*(1:N0)']) * coswt
return h, tf;
end
@acc function cosvec(ts, wd)
Float64[sqrt(2)*cos(wd*t) for t in ts]
end
@acc function cosmat(ts, Ns, wd, N)
Float64[2*cos(wd*cos(2*pi/N*n)*t) for n in Ns, t in ts]
end
Benchmarking this I get:
julia> @time Jakes_Flat( 926, 1e-6, 50000, 0, 1, 0 )
0.004779 seconds (115 allocations: 4.965 MB)
and without calling the accelerated functions (by putting @noacc in front
of the function calls, I get):
julia> @time Jakes_Flat_noacc( 926, 1e-6, 50000, 0, 1, 0 )
0.019072 seconds (75 allocations: 8.396 MB)
The matlab code on my computer runs at:
>> tic; Jakes_Flat( 926, 1E-6, 50000, 0, 1, 0 ); toc
Elapsed time is 0.009936 seconds.
So.. great victory for ParallelAccelerator.jl?
On Sunday, October 18, 2015 at 1:17:50 PM UTC+2, Vishnu Raj wrote:
>
> Although Julia homepage shows using Julia over Matlab gains more in
> performance, my experience is quite opposite.
> I was trying to simulate channel evolution using Jakes Model for wireless
> communication system.
>
> Matlab code is:
> function [ h, tf ] = Jakes_Flat( fd, Ts, Ns, t0, E0, phi_N )
> %JAKES_FLAT
> % Inputs:
> % fd, Ts, Ns : Doppler frequency, sampling time, number of samples
> % t0, E0 : initial time, channel power
> % phi_N : initial phase of the maximum Doppler frequeny
> % sinusoid
> %
> % Outputs:
> % h, tf : complex fading vector, current time
>
> if nargin < 6, phi_N = 0; end
> if nargin < 5, E0 = 1; end
> if nargin < 4, t0 = 0; end
>
> N0 = 8; % As suggested by Jakes
> N = 4*N0 + 2; % an accurate approximation
> wd = 2*pi*fd; % Maximum Doppler frequency[rad]
> t = t0 + [0:Ns-1]*Ts; % Time vector
> tf = t(end) + Ts; % Final time
> coswt = [ sqrt(2)*cos(wd*t); 2*cos(wd*cos(2*pi/N*[1:N0]')*t) ];
> h = E0/sqrt(2*N0+1)*exp(j*[phi_N pi/(N0+1)*[1:N0]])*coswt;
> end
> Enter code here...
>
> My call results in :
> >> tic; Jakes_Flat( 926, 1E-6, 50000, 0, 1, 0 ); toc
> Elapsed time is 0.008357 seconds.
>
>
> My corresponding Julia code is
> function Jakes_Flat( fd, Ts, Ns, t0 = 0, E0 = 1, phi_N = 0 )
> # Inputs:
> #
> # Outputs:
> N0 = 8; # As suggested by Jakes
> N = 4*N0+2; # An accurate approximation
> wd = 2*pi*fd; # Maximum Doppler frequency
> t = t0 + [0:Ns-1]*Ts;
> tf = t[end] + Ts;
> coswt = [ sqrt(2)*cos(wd*t'); 2*cos(wd*cos(2*pi/N*[1:N0])*t') ]
> h = E0/sqrt(2*N0+1)*exp(im*[ phi_N pi/(N0+1)*[1:N0]']) * coswt
> return h, tf;
> end
> # Saved this as "jakes_model.jl"
>
>
> My first call results in
> julia> include( "jakes_model.jl" )
> Jakes_Flat (generic function with 4 methods)
>
> julia> @time Jakes_Flat( 926, 1e-6, 50000, 0, 1, 0 )
> elapsed time: 0.65922234 seconds (61018916 bytes allocated)
>
> julia> @time Jakes_Flat( 926, 1e-6, 50000, 0, 1, 0 )
> elapsed time: 0.042468906 seconds (17204712 bytes allocated, 63.06% gc
> time)
>
> For first execution, Julia is taking huge amount of time. On second call,
> even though Julia take considerably less(0.042468906 sec) than first(
> 0.65922234 sec), it's still much higher to Matlab(0.008357 sec).
> I'm using Matlab R2014b and Julia v0.3.10 on Mac OSX10.10.
>
> - vish
>
