Build out MATLAB test framework and core integration coverage
This commit is contained in:
Silas Oettinghaus
349
Functions/Minimal_examples/gpu_cpu_comparison.m
Normal file
349
Functions/Minimal_examples/gpu_cpu_comparison.m
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%% GPU vs CPU Comparison Test for DP_Fiber
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% This script runs the fiber simulation with and without GPU acceleration
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% and compares the numerical results.
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% clear; clc;
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%% Setup (same as gpu_processing_dpfiber.m but simplified)
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s.wavelengthplan = calcWavelengthPlan(4, 400e9, 1310);
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link_length = 10;
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s.pmd = 0.1;
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s.gamma = 0.0023;
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s.M = 4;
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fsym = 112e9;
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fdac = 2*fsym;
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fadc = 120000000000;
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s.random_key = 1;
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% Laser / Modulator
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vbias_rel = 0.5;
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u_pi = 4.6;
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vbias = -vbias_rel*u_pi;
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laser_linewidth = 0e6;
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% DB Stuff
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duob_mode = db_mode.no_db;
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rcalpha = 0.05;
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Pform = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rc","pulselength",16,"alpha",rcalpha);
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s.chirpalpha = 0;
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s.p_launch = 3;
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s.p = "co";
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N = numel(s.wavelengthplan);
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switch s.p
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case "co"
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pol_rot = 100.*ones(1,N);
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d_local = 0;
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case "pair"
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pol_rot = repmat([100,100,0,0],1,N/4);
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d_local = 0;
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case "alt"
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pol_rot = repmat([100,0,100,0],1,N/4);
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d_local = 0;
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case "seg"
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pol_rot = 100.*ones(1,N);
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d_local = 3;
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otherwise
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error('Unknown fwm_mitigation_technique: %s', string(s.p));
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end
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f_plan = physconst('lightspeed')./(s.wavelengthplan.*1e-9);
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margin = 5e12;
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f_span = (max(f_plan)+margin)-(min(f_plan)-margin);
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f_nyq = f_span/2;
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kover = 4;
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upsample_required = f_nyq./(fdac*kover/2);
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upsample_pow = 2^nextpow2(upsample_required);
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s.f_opt = fdac*kover*upsample_pow;
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s.f_opt_nyq = s.f_opt/2;
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%% TX per channel
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for l = 1:N
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[Digi_sig,Symbols{l},Tx_bits{l}] = PAMsource( ...
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"fsym",fsym,"M",s.M,"order",15,"useprbs",0, ...
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"fs_out",fdac, ...
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"applyclipping",0,"clipfactor",1.5, ...
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"applypulseform",1,"pulseformer",Pform, ...
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"randkey",s.random_key+l, ...
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"mrds_code",0,"mrds_blocklength",512,"duobinary_mode",duob_mode ...
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).process();
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Lp_awg = Filter('filtdegree',3,"f_cutoff",56e9,"fs",fdac*kover, ...
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"filterType",filtertypes.gaussian,"active",true);
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El_sig = AWG("fdac",fdac,"f_cutoff",fsym,"lpf_active",1,"kover",kover, ...
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"bit_resolution",6,"upsampling_method","samplehold","precomp_sinc_rolloff",0, ...
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"H_lpf",Lp_awg,"dac_max",0.6,"dac_min",-0.6).process(Digi_sig);
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clear Digi_sig
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El_sig = El_sig.normalize("mode","oneone");
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scaling = 0.6*(u_pi/2-abs(vbias-u_pi/2));
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El_sig = El_sig .* scaling;
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Eml_out = EML("mode",eml_mode.im_cosinus,"power",3,"fsimu",El_sig.fs, ...
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"lambda",s.wavelengthplan(l),"bias",vbias,"u_pi",u_pi, ...
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"linewidth",laser_linewidth,"randomkey",s.random_key+l,"alpha",s.chirpalpha).process(El_sig);
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clear El_sig
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signal_cell{l} = Polarization_Controller("mode","rot_power","desired_power",pol_rot(l)).process(Eml_out);
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clear Eml_out Lp_awg
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end
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disp('Signal generated for all channels.');
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%% WDM mux + launch
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Opt_sig_wdm = Optical_Multiplex("fs_in",fdac*kover,"fs_out",upsample_pow*fdac*kover, ...
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"lambda_center",1310,"random_key",0,"filtype",1,"B",120e9).process(signal_cell);
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Opt_sig_wdm = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power", ...
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"amplification_db",s.p_launch+10*log10(N)).process(Opt_sig_wdm);
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% Save input for both runs
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Opt_sig_input = Opt_sig_wdm;
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segment_length = 1;
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nSegments = link_length/segment_length;
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if abs(nSegments - round(nSegments)) > 1e-12
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error('fiber_length_km=%g must be an integer multiple of segment_length=%g km.', link_length, segment_length);
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end
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nSegments = round(nSegments);
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zdw = 1310;
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randomize_D = true;
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if nSegments > 0
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Dvec = getDispersionVector(nSegments, d_local, zdw, randomize_D, s.random_key);
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else
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Dvec = [];
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end
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%% Run WITHOUT GPU
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fprintf('\n========== Running WITHOUT GPU (CPU) ==========\n');
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Opt_sig_cpu = Opt_sig_input;
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tic;
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for seg = 1:nSegments
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fprintf('CPU Segment %d/%d \n',seg, nSegments);
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Opt_sig_cpu = DP_Fiber("L",segment_length,"D",Dvec(seg),"Dpmd",s.pmd,"Ds",0.07, ...
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"beat_len",10,"corr_len",100,"dz",1,"manakov",0, ...
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"gamma",s.gamma,"lambda",zdw,"n_waveplates",10,"SS_dphimax",0.01, ...
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"SS_dzmax",50,"SS_dzmin",10,"X_alpha",0.3,"X_beta",0,"rng",1,"useGPU",false).process(Opt_sig_cpu);
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end
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time_cpu = toc;
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fprintf('CPU Time: %.3f seconds\n', time_cpu);
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%% Run WITH GPU (Double Precision)
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fprintf('\n========== Running WITH GPU (Double Precision) ==========\n');
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Opt_sig_gpu_double = Opt_sig_input;
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tic;
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for seg = 1:nSegments
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fprintf('GPU-Double Segment %d/%d \n',seg, nSegments);
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Opt_sig_gpu_double = DP_Fiber("L",segment_length,"D",Dvec(seg),"Dpmd",s.pmd,"Ds",0.07, ...
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"beat_len",10,"corr_len",100,"dz",1,"manakov",0, ...
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"gamma",s.gamma,"lambda",zdw,"n_waveplates",10,"SS_dphimax",0.01, ...
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"SS_dzmax",50,"SS_dzmin",10,"X_alpha",0.3,"X_beta",0,"rng",1,"useGPU",true,"useSingle",false).process(Opt_sig_gpu_double);
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end
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time_gpu_double = toc;
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fprintf('GPU Double Time: %.3f seconds\n', time_gpu_double);
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%% Run WITH GPU (Single Precision)
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fprintf('\n========== Running WITH GPU (Single Precision) ==========\n');
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Opt_sig_gpu_single = Opt_sig_input;
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tic;
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for seg = 1:nSegments
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fprintf('GPU-Single Segment %d/%d \n',seg, nSegments);
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Opt_sig_gpu_single = DP_Fiber("L",segment_length,"D",Dvec(seg),"Dpmd",s.pmd,"Ds",0.07, ...
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"beat_len",10,"corr_len",100,"dz",1,"manakov",0, ...
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"gamma",s.gamma,"lambda",zdw,"n_waveplates",10,"SS_dphimax",0.01, ...
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"SS_dzmax",50,"SS_dzmin",10,"X_alpha",0.3,"X_beta",0,"rng",1,"useGPU",true,"useSingle",true).process(Opt_sig_gpu_single);
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end
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time_gpu_single = toc;
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fprintf('GPU Single Time: %.3f seconds\n', time_gpu_single);
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%% Compare Results
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fprintf('\n========== Numerical Comparison ==========\n');
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sig_cpu = Opt_sig_cpu.signal;
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sig_gpu_double = Opt_sig_gpu_double.signal;
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sig_gpu_single = Opt_sig_gpu_single.signal;
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% Check dimensions
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fprintf('CPU signal size: [%d x %d]\n', size(sig_cpu,1), size(sig_cpu,2));
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fprintf('GPU Double signal size: [%d x %d]\n', size(sig_gpu_double,1), size(sig_gpu_double,2));
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fprintf('GPU Single signal size: [%d x %d]\n', size(sig_gpu_single,1), size(sig_gpu_single,2));
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% CPU vs GPU Double
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fprintf('\n--- CPU vs GPU Double ---\n');
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diff_cpu_double = abs(sig_cpu - sig_gpu_double);
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max_diff_cpu_double = max(diff_cpu_double(:));
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mean_diff_cpu_double = mean(diff_cpu_double(:));
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rel_diff_cpu_double = max_diff_cpu_double / max(abs(sig_cpu(:)));
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fprintf('Max absolute difference: %.6e\n', max_diff_cpu_double);
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fprintf('Mean absolute difference: %.6e\n', mean_diff_cpu_double);
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fprintf('Max relative difference: %.6e\n', rel_diff_cpu_double);
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% CPU vs GPU Single
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fprintf('\n--- CPU vs GPU Single ---\n');
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diff_cpu_single = abs(sig_cpu - sig_gpu_single);
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max_diff_cpu_single = max(diff_cpu_single(:));
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mean_diff_cpu_single = mean(diff_cpu_single(:));
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rel_diff_cpu_single = max_diff_cpu_single / max(abs(sig_cpu(:)));
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fprintf('Max absolute difference: %.6e\n', max_diff_cpu_single);
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fprintf('Mean absolute difference: %.6e\n', mean_diff_cpu_single);
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fprintf('Max relative difference: %.6e\n', rel_diff_cpu_single);
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% GPU Double vs GPU Single
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fprintf('\n--- GPU Double vs GPU Single ---\n');
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diff_double_single = abs(sig_gpu_double - sig_gpu_single);
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max_diff_double_single = max(diff_double_single(:));
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mean_diff_double_single = mean(diff_double_single(:));
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rel_diff_double_single = max_diff_double_single / max(abs(sig_gpu_double(:)));
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fprintf('Max absolute difference: %.6e\n', max_diff_double_single);
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fprintf('Mean absolute difference: %.6e\n', mean_diff_double_single);
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fprintf('Max relative difference: %.6e\n', rel_diff_double_single);
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% Check tolerances
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fprintf('\n--- Tolerance Check ---\n');
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tol_double = 1e-10;
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tol_single = 1e-5; % Single precision has ~7 significant digits
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if max_diff_cpu_double < tol_double
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fprintf('✓ CPU vs GPU Double: EQUIVALENT (diff < %.0e)\n', tol_double);
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else
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fprintf('✗ CPU vs GPU Double: DIFFER beyond tolerance (%.0e)\n', tol_double);
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end
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if max_diff_cpu_single < tol_single
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fprintf('✓ CPU vs GPU Single: ACCEPTABLE (diff < %.0e)\n', tol_single);
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else
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fprintf('⚠ CPU vs GPU Single: Precision loss detected (diff = %.2e, tol = %.0e)\n', max_diff_cpu_single, tol_single);
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end
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% Performance comparison
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fprintf('\n========== Performance Summary ==========\n');
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fprintf('CPU Time: %.3f s\n', time_cpu);
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fprintf('GPU Double Time: %.3f s\n', time_gpu_double);
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fprintf('GPU Single Time: %.3f s\n', time_gpu_single);
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fprintf('\n');
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fprintf('Speedup (GPU Double vs CPU): %.2fx\n', time_cpu/time_gpu_double);
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fprintf('Speedup (GPU Single vs CPU): %.2fx\n', time_cpu/time_gpu_single);
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fprintf('Speedup (GPU Single vs GPU Double): %.2fx\n', time_gpu_double/time_gpu_single);
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%% ========== BER Comparison ==========
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% Process each fiber output through simplified receiver to check if
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% single-precision affects actual BER performance
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fprintf('\n========== BER Comparison ==========\n');
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fprintf('Processing signals through receiver chain...\n');
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% Receiver parameters
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rop = -7; % Received optical power [dBm]
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len_tr = 4096; % Training length
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mu_dc = 0.005;
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mu_ffe = [0.0001 0.0008 0.001];
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mu_dfe = 0.0004;
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% Helper function to process through receiver and get BER
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function ber = process_receiver(Opt_sig_fib, l, Symbols, Tx_bits, ...
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fdac, kover, upsample_pow, fsym, fadc, rop, s, len_tr, mu_dc, mu_ffe, mu_dfe, duob_mode)
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% Demux single channel
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Opt_sig_demux = Optical_Demultiplex("attenuation",0,"B",200e9,"filtype",1, ...
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"fs_out",fdac*kover,"fs_in",fdac*kover*upsample_pow,"lambda_center",1310).process(Opt_sig_fib);
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% ROP amplifier
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Opt_sig_rx = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power", ...
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"amplification_db",rop).process(Opt_sig_demux{l});
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% Photodiode
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PD_sig = Photodiode("fsimu",fdac*kover,"dark_current",2e-08,"responsivity",1,"temperature",20, ...
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"nep",1.8e-11,"randomkey",s.random_key+l).process(Opt_sig_rx);
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% Low-pass filter
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rx_bwl = 100e9;
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PD_sig = Filter('filtdegree',4,"f_cutoff",rx_bwl,"fs",fdac*kover, ...
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"filterType",filtertypes.butterworth,"active",true).process(PD_sig);
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% Scope
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Lp_scpe = Filter('filtdegree',4,"f_cutoff",80e9,"fs",fadc,"filterType",filtertypes.butterworth,"active",true);
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Scpe_sig = Scope("fsimu",fdac*kover,"fadc",fadc, ...
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"delay",0,"fixed_delay",0,"filtertype",filtertypes.butterworth, ...
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"samplingdelay",0,"rand_samplingdelay",0,"freq_offset",0,"samp_jitter",0, ...
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"adcresolution",8,"quantbuffer",0.1,'block_dc',1,'lpf_active',0,'H_lpf',Lp_scpe).process(PD_sig);
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% Resample to 2 sps
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Scpe_sig_2sps = Scpe_sig.resample("fs_out",2*fsym);
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% Time sync
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[~, Scpe_cell, ~, ~] = Scpe_sig_2sps.tsynch("reference", Symbols{l}, "fs_ref", fsym, "debug_plots", 0);
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Rx_sig = Scpe_cell{1};
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Rx_sig = Rx_sig.normalize("mode","rms");
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% FFE Equalizer
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ffe_order = [50, 0, 0];
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eq_ffe = EQ("Ne",ffe_order,"Nb",[0,0,0],"training_length",len_tr,"training_loops",5,"dd_loops",5, ...
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"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",0);
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ffe_results = ffe(eq_ffe,s.M,Rx_sig,Symbols{l},Tx_bits{l}, ...
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"precode_mode",duob_mode, ...
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'showAnalysis',0, ...
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"postFFE",[], ...
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"eth_style_symbol_mapping",0);
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ber = ffe_results.metrics.BER;
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end
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% Process each mode for channel 1
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l = 4; % Use first channel for comparison
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fprintf('Processing CPU result...\n');
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ber_cpu = process_receiver(Opt_sig_cpu, l, Symbols, Tx_bits, ...
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fdac, kover, upsample_pow, fsym, fadc, rop, s, len_tr, mu_dc, mu_ffe, mu_dfe, duob_mode);
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fprintf('Processing GPU Double result...\n');
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ber_gpu_double = process_receiver(Opt_sig_gpu_double, l, Symbols, Tx_bits, ...
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fdac, kover, upsample_pow, fsym, fadc, rop, s, len_tr, mu_dc, mu_ffe, mu_dfe, duob_mode);
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fprintf('Processing GPU Single result...\n');
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ber_gpu_single = process_receiver(Opt_sig_gpu_single, l, Symbols, Tx_bits, ...
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fdac, kover, upsample_pow, fsym, fadc, rop, s, len_tr, mu_dc, mu_ffe, mu_dfe, duob_mode);
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% Display BER results
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fprintf('\n========== BER Results (Channel %d, ROP = %d dBm) ==========\n', l, rop);
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fprintf('CPU: BER = %.4e\n', ber_cpu);
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fprintf('GPU Double: BER = %.4e\n', ber_gpu_double);
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fprintf('GPU Single: BER = %.4e\n', ber_gpu_single);
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fprintf('\n--- BER Comparison ---\n');
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if ber_cpu == 0 && ber_gpu_double == 0 && ber_gpu_single == 0
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fprintf('✓ All BERs are zero (no errors detected)\n');
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else
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ber_diff_double = abs(ber_cpu - ber_gpu_double);
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ber_diff_single = abs(ber_cpu - ber_gpu_single);
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fprintf('|BER_cpu - BER_gpu_double| = %.4e\n', ber_diff_double);
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fprintf('|BER_cpu - BER_gpu_single| = %.4e\n', ber_diff_single);
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if ber_diff_double < 1e-6 && ber_diff_single < 1e-6
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fprintf('✓ BER differences are negligible\n');
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elseif ber_diff_single > ber_diff_double * 10
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fprintf('⚠ Single precision shows measurable BER impact\n');
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else
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fprintf('✓ BER differences within acceptable range\n');
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end
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end
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fprintf('\n========== Test Complete ==========\n');
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