It's fantastic to see some good ParallelAccelerator results "in the wild"! Thanks for sharing.
Lindsey On Wednesday, October 21, 2015 at 1:23:53 PM UTC-7, Kristoffer Carlsson wrote: > > Btw it is really cool to see julia running at 400% CPU when running a list > comprehension. > > I did some more benchmarks with larger N to reduce the noise a bit and the > difference is actually not that great between Matlab and Julia. However, > tying with Matlabs parallellized vectorized maps is great imho. > > julia> @time h, f = Jakes_Flat( 926, 1e-6, 5000000, 0, 1, 0 ) > 0.585940 seconds (153 allocations: 495.918 MB, 12.47% gc time) > ( > > > >> tic; Jakes_Flat( 926, 1E-6, 5000000, 0, 1, 0 ); toc > Elapsed time is 0.609867 seconds. > > > > > On Wednesday, October 21, 2015 at 10:17:18 PM UTC+2, Kristoffer Carlsson > wrote: >> >> 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 >>> >>
