Faster DP_fiber with GPU processing... Run test\gpu_cpu_comparison.m to see difference on your setup
This commit is contained in:
silas (home)
@@ -24,6 +24,8 @@ classdef DP_Fiber
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SS_dzmax % [m] max dz (adaptive SSFM)
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SS_dzmin % [m] min dz (adaptive SSFM)
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n_waveplates % number of PMD waveplates
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useGPU % GPU acceleration: true, false, or 'auto' (default)
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useSingle % Use single precision on GPU (default: false)
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% ---- Internal state (persistent between calls) ----
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state % struct mirroring legacy 'state'
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@@ -56,6 +58,8 @@ classdef DP_Fiber
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options.SS_dzmax = 2e4 % m
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options.SS_dzmin = 100 % m
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options.n_waveplates = 100
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options.useGPU = 'auto' % 'auto', true, or false
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options.useSingle = false % single precision GPU
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end
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% Copy provided options into properties
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@@ -228,7 +232,18 @@ classdef DP_Fiber
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x_in = signal_in(:,1).';
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y_in = signal_in(:,2).';
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[x_out, y_out, obj.state] = CNLSE_plain(x_in, y_in, obj.state);
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% Determine GPU usage
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if ischar(obj.useGPU) || isstring(obj.useGPU)
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if strcmpi(obj.useGPU, 'auto')
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gpuFlag = []; % Let CNLSE_plain auto-detect
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else
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error('DP_Fiber:InvalidGPU', 'useGPU must be true, false, or ''auto''');
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end
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else
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gpuFlag = logical(obj.useGPU);
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end
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[x_out, y_out, obj.state] = CNLSE_plain(x_in, y_in, obj.state, gpuFlag, obj.useSingle);
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obj.state.propagated_length = obj.state.propagated_length + obj.state.L;
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@@ -119,29 +119,30 @@ classdef Optical_Demultiplex < handle
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N = size(lo,1);
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C = size(lo,2);
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x_envelopes = zeros(N, C, 'like', signal_in);
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y_envelopes = zeros(N, C, 'like', signal_in);
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% ---- VECTORIZED: Process all channels in parallel ----
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% Batched FFT operates on each column simultaneously on GPU
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s1 = signal_in(:,1);
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s2 = signal_in(:,2);
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% Extract polarization signals
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s1 = signal_in(:,1); % X polarization [N×1]
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s2 = signal_in(:,2); % Y polarization [N×1]
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% Reusable work buffers (avoid reallocations)
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wrk_time = zeros(N,1, 'like', signal_in);
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wrk_freq = zeros(N,1, 'like', signal_in);
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% Broadcast signal to all channels and multiply with LO
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% s1, s2 are [N×1], lo is [N×C] → result is [N×C]
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x_mixed = att .* s1 .* lo; % [N×C]
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y_mixed = att .* s2 .* lo; % [N×C]
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for c = 1:C
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% ---- X branch ----
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wrk_time(:) = att .* s1 .* lo(:,c); % N×1
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wrk_freq(:) = fft(wrk_time); % N×1
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wrk_freq(:) = wrk_freq .* H; % N×1
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x_envelopes(:,c) = ifft(wrk_freq); % N×1
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% Batched FFT: each column computed in parallel
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x_freq = fft(x_mixed); % [N×C]
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y_freq = fft(y_mixed); % [N×C]
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% Apply filter (H is [N×1], broadcasts across columns)
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x_filtered = x_freq .* H; % [N×C]
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y_filtered = y_freq .* H; % [N×C]
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% Batched IFFT
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x_envelopes = ifft(x_filtered); % [N×C]
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y_envelopes = ifft(y_filtered); % [N×C]
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% ---- Y branch ----
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wrk_time(:) = att .* s2 .* lo(:,c);
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wrk_freq(:) = fft(wrk_time);
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wrk_freq(:) = wrk_freq .* H;
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y_envelopes(:,c) = ifft(wrk_freq);
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end
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end
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end
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@@ -140,24 +140,35 @@ classdef Optical_Multiplex < handle
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x_envelopes = NaN([blocklen_out N]);
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y_envelopes = x_envelopes;
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% ---- OPTIMIZED: Pre-compute all LO phases as [blocklen_out × N] matrix ----
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time_idx = (0:blocklen_out-1).'; % [blocklen_out × 1]
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lo_phases = mod(2*pi * time_idx * obj.df_T / obj.fs_out, 2*pi); % [blocklen_out × N]
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lo_all = cos(lo_phases) + 1i*sin(lo_phases); % [blocklen_out × N]
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% Collect all resampled signals first (still requires loop due to cell array)
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x_signals = zeros(blocklen_out, N);
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y_signals = zeros(blocklen_out, N);
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for o = 1:N
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pha = mod(2*pi*(0:blocklen_out-1)*obj.df_T(o)/obj.fs_out,2*pi).';
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lo = cos(pha)+1i*sin(pha);
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data_in_resampled = data_in{o}.resample("fs_out", obj.fs_out);
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res_env = ifft(fft(data_in_resampled.signal(:,1)).*H);
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x_envelopes(:,o) = att.*res_env.*lo;
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res_env = ifft(fft(data_in_resampled.signal(:,2)).*H);
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y_envelopes(:,o) = att.*res_env.*lo;
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x_signals(:, o) = data_in_resampled.signal(:, 1);
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y_signals(:, o) = data_in_resampled.signal(:, 2);
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end
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% ---- VECTORIZED: Batched FFT/IFFT for all channels ----
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% Apply filter to all channels at once
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x_filtered = ifft(fft(x_signals) .* H); % [blocklen_out × N]
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y_filtered = ifft(fft(y_signals) .* H); % [blocklen_out × N]
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% Apply attenuation and LO shift to all channels
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x_envelopes = att .* x_filtered .* lo_all; % [blocklen_out × N]
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y_envelopes = att .* y_filtered .* lo_all; % [blocklen_out × N]
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data_out = data_in_resampled;
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data_out.signal = [sum(x_envelopes,2), sum(y_envelopes,2)];
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data_out.lambda = obj.lambda_T;
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data_out.polrot = polrots;
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end
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end
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@@ -1,7 +1,13 @@
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function [opt_out_x,opt_out_y,state] = CNLSE_plain(opt_in_x,opt_in_y,state)
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function [opt_out_x,opt_out_y,state] = CNLSE_plain(opt_in_x,opt_in_y,state,useGPU,useSingle)
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% GPU auto-detection if not specified
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if nargin < 4 || isempty(useGPU)
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useGPU = canUseGPU();
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end
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if nargin < 5 || isempty(useSingle)
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useSingle = false;
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end
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% pre calculations
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@@ -29,8 +35,33 @@ function [opt_out_x,opt_out_y,state] = CNLSE_plain(opt_in_x,opt_in_y,state)
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deltaomega = state.omega;
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beta_x = beta_const + beta_1 * deltaomega + 1/2 * beta_2 * deltaomega.^2 + 1/6 *beta_3 * deltaomega.^3;
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% opt_in_x = gpuArray(opt_in_x);
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% opt_in_y = gpuArray(opt_in_y);
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% GPU Transfer (if enabled)
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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if useGPU
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% Convert to single precision if requested (faster on most GPUs)
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if useSingle
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opt_in_x = gpuArray(single(opt_in_x));
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opt_in_y = gpuArray(single(opt_in_y));
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state.common_beta.X = gpuArray(single(state.common_beta.X));
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state.common_beta.Y = gpuArray(single(state.common_beta.Y));
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state.brf.db1 = gpuArray(single(state.brf.db1));
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state.brf.db0 = gpuArray(single(state.brf.db0));
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for k = 1:numel(state.brf.matR)
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state.brf.matR{k} = gpuArray(single(state.brf.matR{k}));
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end
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else
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opt_in_x = gpuArray(opt_in_x);
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opt_in_y = gpuArray(opt_in_y);
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state.common_beta.X = gpuArray(state.common_beta.X);
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state.common_beta.Y = gpuArray(state.common_beta.Y);
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state.brf.db1 = gpuArray(state.brf.db1);
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state.brf.db0 = gpuArray(state.brf.db0);
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for k = 1:numel(state.brf.matR)
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state.brf.matR{k} = gpuArray(state.brf.matR{k});
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end
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end
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end
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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@@ -54,8 +85,6 @@ function [opt_out_x,opt_out_y,state] = CNLSE_plain(opt_in_x,opt_in_y,state)
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state.test_dz = [];
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state.powers = [];
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tic
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while state.z_prop < state.L
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% reduce step length (dz) if we are to overshoot the fiber length
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@@ -105,16 +134,26 @@ function [opt_out_x,opt_out_y,state] = CNLSE_plain(opt_in_x,opt_in_y,state)
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end
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toc
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% GPU Gather (if enabled)
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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if useGPU
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opt_out_x = gather(opt_in_x);
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opt_out_y = gather(opt_in_y);
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opt_out_x = (opt_in_x);
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opt_out_y = (opt_in_y);
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% opt_out_x = gather(opt_in_x);
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% opt_out_y = gather(opt_in_y);
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% Gather state arrays back to CPU
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state.common_beta.X = gather(state.common_beta.X);
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state.common_beta.Y = gather(state.common_beta.Y);
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state.brf.db1 = gather(state.brf.db1);
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state.brf.db0 = gather(state.brf.db0);
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for k = 1:numel(state.brf.matR)
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state.brf.matR{k} = gather(state.brf.matR{k});
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end
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else
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opt_out_x = opt_in_x;
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opt_out_y = opt_in_y;
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end
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end
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20
Classes/02_optical/dp_fiber_lib/canUseGPU.m
Normal file
20
Classes/02_optical/dp_fiber_lib/canUseGPU.m
Normal file
@@ -0,0 +1,20 @@
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function canUse = canUseGPU()
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%CANUSEGPU Check if a compatible GPU is available for parallel computing
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% Returns true if MATLAB Parallel Computing Toolbox is available and
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% a CUDA-capable GPU is detected.
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canUse = false;
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% Check if Parallel Computing Toolbox is installed
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if ~license('test', 'Distrib_Computing_Toolbox')
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return;
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end
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% Check for GPU device
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try
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gpu = gpuDevice();
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canUse = gpu.DeviceSupported;
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catch
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canUse = false;
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end
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end
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@@ -1,8 +1,8 @@
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function [rDZ] = getNLstepsize(ux,uy,gamma,dzmin,dzmax,dphimax,alpha_lin)
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maxPow = max(gamma.*max(real(ux).^2+imag(ux).^2+real(uy).^2+imag(uy).^2));
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% gather() handles gpuArray inputs - ensures scalar is on CPU for comparisons
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maxPow = gather(max(gamma.*max(real(ux).^2+imag(ux).^2+real(uy).^2+imag(uy).^2)));
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Leff = dphimax/maxPow;
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alpha_lin = max([alpha_lin.X alpha_lin.Y]);
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@@ -52,7 +52,7 @@ else
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end
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plate_steps = repmat(n_step,1,length(plate_sizes));
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plate_steps = repmat(n_step,1,length(plate_sizes)); %#ok<NASGU>
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%
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%figure;stem(plate_sizes);
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@@ -66,23 +66,15 @@ test_plate_numbers = [test_plate_numbers, plate_numbers];
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opt_x=fft(opt_x);
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opt_y=fft(opt_y);
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% db1 = gpuArray(brf.db1);
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% db0 = gpuArray(brf.db0);
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% common_beta_x = gpuArray(common_beta_x);
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% common_beta_y = gpuArray(common_beta_y);
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db1 = (brf.db1);
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db0 = (brf.db0);
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common_beta_x = (common_beta_x);
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common_beta_y = (common_beta_y);
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% Note: db1, db0, common_beta_x, common_beta_y are already on GPU when
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% useGPU=true (transferred in CNLSE_plain)
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db1 = brf.db1;
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db0 = brf.db0;
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% process every waveplate with given sizes in plate_sizes
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for n=1:length(plate_sizes)
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dz = plate_sizes(n);
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% figure(87);subplot(2,1,1);plot(real(x(900:1150)));subplot(2,1,2);plot(real(y(900:1150)));
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% MOV1=[MOV1 getframe(87)];
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% extract rotation matrix from pre calculated matrices
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matR = brf.matR{plate_numbers(n)};
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@@ -92,15 +84,12 @@ for n=1:length(plate_sizes)
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% calculate statistical delta beta for pmd
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delta_beta = 0.5*(db1+db0(n))/corr_length;
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% build transfer function with delta beta
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%common.beta = beta1+beta2*omega^2
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%accumulate delta beta for log...
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brf.simdgd = brf.simdgd + (db1(length(db1)/2+1)+db0(n))/corr_length;
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h.X = exp(-1j*(common_beta_x-delta_beta)*dz);
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h.Y = exp(-1j*(common_beta_y+delta_beta)*dz);
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% delta_beta has to be added to the transfer function
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% process with transfer function
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tOpt.X = h.X.*tOpt.X ;
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@@ -117,9 +106,8 @@ lin_z_test = lin_z_test + sum(plate_sizes,2);
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%update the number of processed plates so far
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n_plates_done = n_plates_done + n_plates_left;
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% attanuate the signal each linear state with alpha
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% ( 0.2dB = 4.6052e-05 )
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rOpt_x=ifft(exp(-alpha_lin_x*aStepSize/2).*opt_x); % /2 not sure why (have to find it in formulas)
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rOpt_y=ifft(exp(-alpha_lin_y*aStepSize/2).*opt_y); % but not relevant for now
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% attenuate the signal each linear step with alpha
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rOpt_x=ifft(exp(-alpha_lin_x*aStepSize/2).*opt_x);
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rOpt_y=ifft(exp(-alpha_lin_y*aStepSize/2).*opt_y);
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end
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@@ -23,6 +23,12 @@ classdef DBHandler < handle
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arguments
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options.dataBase = ""; % Default value for pathToDB if not provided
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options.type = "mysql";
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options.server = "";
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options.port = 3306;
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options.user = "";
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options.password = "";
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end
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% Assign values to class properties based on input arguments
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@@ -46,12 +52,13 @@ classdef DBHandler < handle
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obj.conn = database( ...
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string(obj.dataBase), ... % Database name
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"silas", ... % Username
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"silas", ... % Password (or getSecret)
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options.user, ... % Username
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options.password, ... % Password (or getSecret)
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"Vendor", "MySQL", ...
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"Server", "134.245.243.254", ...
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"Server", options.server, ...
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"PortNumber", 3306, ...
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"JDBCDriverLocation", "C:\Users\Silas\Documents\mysql-connector-j-9.3.0\mysql-connector-j-9.3.0.jar");
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end
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catch e
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@@ -1,16 +1,4 @@
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% Minimal MZM transfer-function demo (sinusoidal drive) — aligned with your notation
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%
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% Implements exactly:
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% E_out(t) = E0 * exp(j*w0*t) * exp(-j*w0*L*n_eff/c0) * 1/2 * [ exp(-j*phi1(t)) + rho*exp(-j*phi2(t)) ]
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% with phi_{1,2}(t) = pi * v_{1,2}(t)/Vpi
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%
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% Push-pull:
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% v1(t) = +v_drive(t)/2 , v2(t) = -v_drive(t)/2 => phi1 = +pi/2 * v_drive/Vpi, phi2 = -pi/2 * v_drive/Vpi
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%
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% And the ideal TF (rho=1):
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% E_out/E_in = exp(-j*w0*L*n_eff/c0) * cos( (pi/2) * v_drive/Vpi )
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%
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% Note: E_in(t) = E0 * exp(j*w0*t) in this script.
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% Parameters
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c0 = physconst('lightspeed'); % [m/s]
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@@ -25,28 +13,61 @@ Vpi = 3.2; % [V] half-wave voltage (your V_pi)
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% Drive
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f0 = 1e9; % [Hz]
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fs = 100e9; % [Hz]
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Nper = 1; % number of periods
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Vpp = 0.5*Vpi; % [V] peak-to-peak of v_drive(t)
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fs = 200e9; % [Hz]
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Nper = 2; % number of periods
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Vpp = 0.6*Vpi; % [V] peak-to-peak of v_drive(t)
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biasV = 2; % [V] differential bias added to v_drive
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% Analytic
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v_ = linspace(-1,2, 2001);
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% Field transfer function (amplitude)
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Field_mzm_analytic = cos((pi/2)*v_);
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% Power transfer function (intensity)
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P_mzm_analytic = Field_mzm_analytic.^2;
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% Imbalance factor in YOUR notation:
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rho = 1; % rho=1 -> ideal balanced MZM (collapses to ideal TF)
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biasV = 1.1; % [V] differential bias added to v_drive
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% Time axis + differential drive voltage v_drive(t)
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T = Nper/f0;
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t = (0:1/fs:T-1/fs).';
|
||||
|
||||
|
||||
if 1
|
||||
% SINE
|
||||
v_drive = biasV + (Vpp/2)*sin(2*pi*f0*t); % v_drive(t) (peak = Vpp/2)
|
||||
|
||||
else
|
||||
|
||||
% --- Generate PAM-4 Sequence ---
|
||||
symbols = linspace(-0.5, 0.5, 4);
|
||||
num_symbols = 12; % Increased slightly for better visual
|
||||
rng(44);
|
||||
random_data = symbols(randi(4, 1, num_symbols));
|
||||
|
||||
% Create time axis (Note: T is your period from the sine code)
|
||||
sps = round(T * fs);
|
||||
t = (0:1/fs:(num_symbols*T)-1/fs).';
|
||||
|
||||
% Upsample to rectangular waveform
|
||||
v_pam = repelem(random_data, sps).';
|
||||
|
||||
% Apply swing and bias: Resulting range is [biasV-Vpp/2, biasV+Vpp/2]
|
||||
v_drive_rect = biasV + (v_pam * Vpp);
|
||||
|
||||
% --- Round the edges ---
|
||||
filter_span = round(sps/1.5); % Increased span for smoother "rounding"
|
||||
window = gausswin(filter_span);
|
||||
window = window / sum(window);
|
||||
|
||||
% Apply filter (using 'same' to keep vector length, but be aware of edge transients)
|
||||
v_drive = conv(v_drive_rect, window, 'same');
|
||||
|
||||
end
|
||||
|
||||
|
||||
% Analytic
|
||||
v_ = linspace(-1,2, 2001);
|
||||
% Field transfer function (amplitude)
|
||||
Field_mzm_analytic = cos((pi/2)*v_);
|
||||
|
||||
% Power transfer function (intensity)
|
||||
P_mzm_analytic = Field_mzm_analytic.^2;
|
||||
|
||||
% Imbalance factor in YOUR notation:
|
||||
rho = 1;
|
||||
|
||||
% Push-pull branch voltages (consistent with v_drive = v1 - v2)
|
||||
v1 = +0.5*v_drive; % arm 1
|
||||
v2 = -0.5*v_drive; % arm 2
|
||||
@@ -69,8 +90,6 @@ H_num = E_out ./ E_in;
|
||||
% Power (normalized)
|
||||
Pnorm_num = abs(H_num).^2; % since |E_out/E_in|^2
|
||||
|
||||
|
||||
|
||||
% Ideal TF (analytic) for comparison (rho=1, push-pull)
|
||||
H_ideal = common_phase .* cos( (pi/2) * (v_drive./Vpi) );
|
||||
|
||||
@@ -78,9 +97,6 @@ Pnorm_ideal = abs(H_ideal).^2;
|
||||
Pnorm_math = cos( (pi/2) * (v_drive./Vpi) ).^2;
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
set(groot, 'defaultLegendInterpreter', 'tex');
|
||||
set(groot, 'defaultAxesTickLabelInterpreter', 'tex');
|
||||
set(groot, 'defaultTextInterpreter', 'tex');
|
||||
@@ -100,8 +116,11 @@ plot(v_norm,t*1e9, 'LineWidth', 1.0,'Color',colvdrive); grid on;
|
||||
ylabel('t [ns]'); xlabel('v_{drive}(t)/V_\pi');
|
||||
title('Drive voltage (normalized)');
|
||||
xlim([min(v_) max(v_)]);
|
||||
% mat2tikz_improved('C:\Users\Silas\Documents\6971e0b65b380ca6d71c837f\02_IMDD_System\tikz\linear_casee\mzm_input_signal.tex');
|
||||
|
||||
|
||||
%% IN/OUT (static transfer) — normalized x-axis + analytic curve
|
||||
if 0
|
||||
figure(2); clf
|
||||
plot(v_, Field_mzm_analytic, 'LineWidth', 1.2,'LineStyle','--','Color',colfield); hold on;% analytic power TF
|
||||
plot(v_, P_mzm_analytic, 'LineWidth', 1.2, 'Color',colpow); hold on;% analytic power TF
|
||||
@@ -123,18 +142,78 @@ xlim([min(v_) max(v_)+1]);
|
||||
ylim([-1 1]);
|
||||
|
||||
% mat2tikz_improved('C:\Users\Silas\Documents\6971e0b65b380ca6d71c837f\02_IMDD_System\tikz\mzm.tex');
|
||||
end
|
||||
%%
|
||||
|
||||
figure(3); clf
|
||||
plot(v_, Field_mzm_analytic, 'LineWidth', 1.2,'LineStyle','--','Color',colfield); hold on;% analytic power TF
|
||||
plot(v_, P_mzm_analytic, 'LineWidth', 1.2, 'Color',colpow); hold on;% analytic power TF
|
||||
|
||||
scatter(v_norm, Pnorm_num, 12, '.', 'LineWidth', 1,'MarkerEdgeColor',colvdrive);
|
||||
scatter(biasV./Vpi,(cos((pi/2)*biasV./Vpi)^2),10,'Marker','o');
|
||||
line([min(v_drive), min(v_drive)]./Vpi,[(cos((pi/2)*min(v_drive)./Vpi)^2), -2],'linewidth',0.5,'color','black','linestyle','--');
|
||||
line([max(v_drive) max(v_drive)]./Vpi,[(cos((pi/2)*max(v_drive)./Vpi)^2), -2],'linewidth',0.5,'color','black','linestyle','--');
|
||||
xline([min(v_norm) max(v_norm)])
|
||||
|
||||
grid on;
|
||||
xlabel('v_{drive}(t)/V_\pi'); ylabel('|E_{out}/E_{in}|^2');
|
||||
% legend
|
||||
xlim([min(v_) max(v_)]);
|
||||
ylim([-1 1]);
|
||||
|
||||
% mat2tikz_improved('C:\Users\Silas\Documents\6971e0b65b380ca6d71c837f\02_IMDD_System\tikz\mzm_tramsfer_function_matlab.tex');
|
||||
|
||||
|
||||
%%
|
||||
|
||||
figure(4); clf
|
||||
% plot(v_, Field_mzm_analytic, 'LineWidth', 1.2,'LineStyle','--','Color',colfield); hold on;% analytic power TF
|
||||
plot(v_, P_mzm_analytic, 'LineWidth', 1.2, 'Color','black'); hold on;% analytic power TF
|
||||
input_dots = linspace(min(v_drive),max(v_drive),4)./Vpi;
|
||||
% input_dots = unique(v_drive_rect)./Vpi;
|
||||
output_dots = (cos((pi/2)*input_dots).^2);
|
||||
scatter(input_dots,output_dots,'Marker','x','LineWidth',1,'MarkerEdgeColor','black');
|
||||
scatter(input_dots,zeros(size(input_dots)),'Marker','^','LineWidth',2,'MarkerEdgeColor','black');
|
||||
% scatter(ones(size(input_dots)),output_dots,'Marker','<','LineWidth',2,'MarkerEdgeColor','black');
|
||||
|
||||
for i = 1:numel(input_dots)
|
||||
% Draw the dashed projection lines
|
||||
line([input_dots(i), input_dots(i)], [output_dots(i), 0], 'linewidth', 0.5, 'color', 'black', 'linestyle', '--', 'handlevisibility', 'off');
|
||||
line([input_dots(i), 1], [output_dots(i), output_dots(i)], 'linewidth', 0.5, 'color', 'black', 'linestyle', '--', 'handlevisibility', 'off');
|
||||
|
||||
% Add the level annotation boxes near the output (y-axis)
|
||||
% Adjust the '1.05' to move the box further right or 'output_dots(i)' for height
|
||||
j = 3-(i-1)*2;
|
||||
text(1, output_dots(i), sprintf('Level %d', j), ...
|
||||
'FontSize', 8, ...
|
||||
'EdgeColor', 'black', ...
|
||||
'BackgroundColor', 'white', ...
|
||||
'Margin', 2);
|
||||
end
|
||||
|
||||
xlim([0,1.5]);
|
||||
ylim([0,1])
|
||||
|
||||
% line([min(v_drive), min(v_drive)]./Vpi,[(cos((pi/2)*min(v_drive)./Vpi)^2), 0],'linewidth',0.5,'color','black','linestyle','--');
|
||||
% line([max(v_drive), max(v_drive)]./Vpi,[(cos((pi/2)*max(v_drive)./Vpi)^2), 0],'linewidth',0.5,'color','black','linestyle','--');
|
||||
|
||||
% mat2tikz_improved('C:\Users\Silas\Documents\6971e0b65b380ca6d71c837f\02_IMDD_System\tikz\linear_casee\mzm_tf.tex');
|
||||
% xticks(sort(input_dots));
|
||||
% yticks(sort(output_dots));
|
||||
grid off
|
||||
|
||||
|
||||
%%
|
||||
% % FIELD TF (only field here; do not mix power into this figure)
|
||||
figure(3); clf
|
||||
figure(5); clf
|
||||
% plot(t*1e9, real(H_num), 'LineWidth', 1.0); hold on;
|
||||
% plot(t*1e9, real(H_ideal), '--', 'LineWidth', 1.0,'DisplayName','Field','Color',colfield); hold on;
|
||||
plot(t*1e9, Pnorm_num, 'LineWidth', 1.0,'DisplayName','Intensity', 'Color',colpow); hold on;
|
||||
grid on;
|
||||
xlabel('t [ns]'); ylabel('Re\{E_{out}/E_{in}\}');
|
||||
legend
|
||||
mat2tikz_improved('C:\Users\Silas\Documents\6971e0b65b380ca6d71c837f\02_IMDD_System\tikz\mzm_out.tex');
|
||||
yticks(sort(output_dots));
|
||||
% mat2tikz_improved('C:\Users\Silas\Documents\6971e0b65b380ca6d71c837f\02_IMDD_System\tikz\linear_casee\mzm_output_signal.tex');
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -10,10 +10,11 @@ if dsp_options.mode == "load_run_id"
|
||||
|
||||
if experiment == "highspeed_2024"
|
||||
|
||||
dsp_options.database_type = 'mysql';
|
||||
dsp_options.database_type = "mysql";
|
||||
dsp_options.dataBase = 'labor_highspeed';%'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\silas_labor_newdsp_newstructure.db';
|
||||
dsp_options.storage_path = 'Z:\2024\sioe_labor\';
|
||||
db = DBHandler("dataBase", [dsp_options.dataBase], "type", dsp_options.database_type);
|
||||
db = DBHandler("dataBase", [dsp_options.dataBase],...
|
||||
"type", dsp_options.database_type,"server","192.168.178.192","user","silas","password","silas");
|
||||
|
||||
elseif experiment == "mpi_ecoc_2025"
|
||||
|
||||
|
||||
@@ -312,7 +312,7 @@ for realiz = 1:s.num_realiz
|
||||
Opt_sig_wdm_fib = DP_Fiber("L",segment_length,"D",Dvec(seg),"Dpmd",s.pmd,"Ds",0.07, ...
|
||||
"beat_len",10,"corr_len",100,"dz",1,"manakov",0, ...
|
||||
"gamma",s.gamma,"lambda",zdw,"n_waveplates",10,"SS_dphimax",0.01, ...
|
||||
"SS_dzmax",50,"SS_dzmin",10,"X_alpha",0.3,"X_beta",0,"rng",1).process(Opt_sig_wdm_fib);
|
||||
"SS_dzmax",50,"SS_dzmin",10,"X_alpha",0.3,"X_beta",0,"rng",1,"useGPU",1,"useSingle",1).process(Opt_sig_wdm_fib);
|
||||
|
||||
% -------- Evaluate at intermediate distance (enqueue jobs) --------
|
||||
if eval_ptr <= nEval && seg == eval_seg(eval_ptr)
|
||||
|
||||
219
test/bayesopt_ffe_tuning.m
Normal file
219
test/bayesopt_ffe_tuning.m
Normal file
@@ -0,0 +1,219 @@
|
||||
%% Bayesian Optimization for FFE Parameter Tuning
|
||||
% This script uses bayesopt to find optimal mu_dd and mu_tr values
|
||||
% that minimize BER for the FFE equalizer.
|
||||
|
||||
clear; clc;
|
||||
|
||||
%% Setup - Same as gpu_processing_dpfiber.m
|
||||
s.wavelengthplan = calcWavelengthPlan(4, 400e9, 1310);
|
||||
link_length = 10;
|
||||
s.pmd = 0.1;
|
||||
s.gamma = 0.0023;
|
||||
|
||||
s.M = 4;
|
||||
fsym = 112e9;
|
||||
fdac = 2*fsym;
|
||||
fadc = 120000000000;
|
||||
s.random_key = 1;
|
||||
|
||||
% Laser / Modulator
|
||||
vbias_rel = 0.5;
|
||||
u_pi = 4.6;
|
||||
vbias = -vbias_rel*u_pi;
|
||||
laser_linewidth = 0e6;
|
||||
|
||||
duob_mode = db_mode.no_db;
|
||||
rcalpha = 0.05;
|
||||
Pform = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rc","pulselength",16,"alpha",rcalpha);
|
||||
|
||||
s.chirpalpha = 0;
|
||||
s.p_launch = 3;
|
||||
s.p = "co";
|
||||
|
||||
N = numel(s.wavelengthplan);
|
||||
|
||||
switch s.p
|
||||
case "co"
|
||||
pol_rot = 100.*ones(1,N);
|
||||
d_local = 0;
|
||||
end
|
||||
|
||||
f_plan = physconst('lightspeed')./(s.wavelengthplan.*1e-9);
|
||||
margin = 25e12;
|
||||
f_span = (max(f_plan)+margin)-(min(f_plan)-margin);
|
||||
f_nyq = f_span/2;
|
||||
|
||||
kover = 4;
|
||||
upsample_required = f_nyq./(fdac*kover/2);
|
||||
upsample_pow = 2^nextpow2(upsample_required);
|
||||
|
||||
s.f_opt = fdac*kover*upsample_pow;
|
||||
s.f_opt_nyq = s.f_opt/2;
|
||||
|
||||
s.rop = -8; % Fixed ROP for optimization
|
||||
|
||||
%% Generate TX signals (run once)
|
||||
fprintf('Generating TX signals...\n');
|
||||
for l = 1:N
|
||||
[Digi_sig,Symbols{l},Tx_bits{l}] = PAMsource( ...
|
||||
"fsym",fsym,"M",s.M,"order",15,"useprbs",0, ...
|
||||
"fs_out",fdac, ...
|
||||
"applyclipping",0,"clipfactor",1.5, ...
|
||||
"applypulseform",1,"pulseformer",Pform, ...
|
||||
"randkey",s.random_key+l, ...
|
||||
"mrds_code",0,"mrds_blocklength",512,"duobinary_mode",duob_mode ...
|
||||
).process();
|
||||
|
||||
Lp_awg = Filter('filtdegree',3,"f_cutoff",56e9,"fs",fdac*kover, ...
|
||||
"filterType",filtertypes.gaussian,"active",true);
|
||||
|
||||
El_sig = AWG("fdac",fdac,"f_cutoff",fsym,"lpf_active",1,"kover",kover, ...
|
||||
"bit_resolution",6,"upsampling_method","samplehold","precomp_sinc_rolloff",0, ...
|
||||
"H_lpf",Lp_awg,"dac_max",0.6,"dac_min",-0.6).process(Digi_sig);
|
||||
|
||||
El_sig = El_sig.normalize("mode","oneone");
|
||||
scaling = 0.6*(u_pi/2-abs(vbias-u_pi/2));
|
||||
El_sig = El_sig .* scaling;
|
||||
|
||||
Eml_out = EML("mode",eml_mode.im_cosinus,"power",3,"fsimu",El_sig.fs, ...
|
||||
"lambda",s.wavelengthplan(l),"bias",vbias,"u_pi",u_pi, ...
|
||||
"linewidth",laser_linewidth,"randomkey",s.random_key+l,"alpha",s.chirpalpha).process(El_sig);
|
||||
|
||||
signal_cell{l} = Polarization_Controller("mode","rot_power","desired_power",pol_rot(l)).process(Eml_out);
|
||||
end
|
||||
|
||||
%% WDM mux + launch
|
||||
Opt_sig_wdm = Optical_Multiplex("fs_in",fdac*kover,"fs_out",upsample_pow*fdac*kover, ...
|
||||
"lambda_center",1310,"random_key",0,"filtype",1,"B",120e9).process(signal_cell);
|
||||
|
||||
Opt_sig_wdm = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power", ...
|
||||
"amplification_db",s.p_launch+10*log10(N)).process(Opt_sig_wdm);
|
||||
|
||||
%% Fiber propagation
|
||||
segment_length = 1;
|
||||
nSegments = link_length/segment_length;
|
||||
nSegments = round(nSegments);
|
||||
|
||||
zdw = 1310;
|
||||
randomize_D = true;
|
||||
Dvec = getDispersionVector(nSegments, d_local, zdw, randomize_D, s.random_key);
|
||||
|
||||
Opt_sig_wdm_fib = Opt_sig_wdm;
|
||||
fprintf('Running fiber propagation...\n');
|
||||
for seg = 1:nSegments
|
||||
fprintf('Segment %d/%d\n', seg, nSegments);
|
||||
Opt_sig_wdm_fib = DP_Fiber("L",segment_length,"D",Dvec(seg),"Dpmd",s.pmd,"Ds",0.07, ...
|
||||
"beat_len",10,"corr_len",100,"dz",1,"manakov",0, ...
|
||||
"gamma",s.gamma,"lambda",zdw,"n_waveplates",10,"SS_dphimax",0.01, ...
|
||||
"SS_dzmax",50,"SS_dzmin",10,"X_alpha",0.3,"X_beta",0,"rng",1,"useGPU",true,"useSingle",true).process(Opt_sig_wdm_fib);
|
||||
end
|
||||
|
||||
%% Pre-process to get Rx_sig (do demux once)
|
||||
fprintf('Pre-processing receiver chain...\n');
|
||||
l = 1; % Use channel 1 for optimization
|
||||
|
||||
Opt_sig_demux = Optical_Demultiplex("attenuation",0,"B",200e9,"filtype",1, ...
|
||||
"fs_out",fdac*kover,"fs_in",fdac*kover*upsample_pow,"lambda_center",1310).process(Opt_sig_wdm_fib);
|
||||
|
||||
Opt_sig_rx = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power", ...
|
||||
"amplification_db",s.rop).process(Opt_sig_demux{l});
|
||||
|
||||
PD_sig = Photodiode("fsimu",fdac*kover,"dark_current",2e-08,"responsivity",1,"temperature",20, ...
|
||||
"nep",1.8e-11,"randomkey",s.random_key+l).process(Opt_sig_rx);
|
||||
|
||||
rx_bwl = 100e9;
|
||||
PD_sig = Filter('filtdegree',4,"f_cutoff",rx_bwl,"fs",fdac*kover, ...
|
||||
"filterType",filtertypes.butterworth,"active",true).process(PD_sig);
|
||||
|
||||
Lp_scpe = Filter('filtdegree',4,"f_cutoff",80e9,"fs",fadc,"filterType",filtertypes.butterworth,"active",true);
|
||||
Scpe_sig = Scope("fsimu",fdac*kover,"fadc",fadc, ...
|
||||
"delay",0,"fixed_delay",0,"filtertype",filtertypes.butterworth, ...
|
||||
"samplingdelay",0,"rand_samplingdelay",0,"freq_offset",0,"samp_jitter",0, ...
|
||||
"adcresolution",8,"quantbuffer",0.1,'block_dc',1,'lpf_active',0,'H_lpf',Lp_scpe).process(PD_sig);
|
||||
|
||||
Scpe_sig_2sps = Scpe_sig.resample("fs_out",2*fsym);
|
||||
|
||||
[~, Scpe_cell, ~, ~] = Scpe_sig_2sps.tsynch("reference", Symbols{l}, "fs_ref", fsym, "debug_plots", 0);
|
||||
Rx_sig = Scpe_cell{1};
|
||||
Rx_sig = Rx_sig.normalize("mode","rms");
|
||||
|
||||
fprintf('Receiver pre-processing complete. Ready for optimization.\n\n');
|
||||
|
||||
%% Define the objective function for bayesopt
|
||||
function ber = ffe_objective(params, Rx_sig, Symbols_l, Tx_bits_l, M, duob_mode)
|
||||
mu_dd = params.mu_dd;
|
||||
mu_tr = params.mu_tr;
|
||||
|
||||
try
|
||||
eq_ffe = FFE("epochs_tr", 5, "epochs_dd", 2, "len_tr", 2^13, ...
|
||||
"mu_dd", mu_dd, "mu_tr", mu_tr, ...
|
||||
"order", 50, "sps", 2, "decide", 0, ...
|
||||
"adaption", adaption_method.nlms, "dd_mode", 1);
|
||||
|
||||
ffe_results = ffe(eq_ffe, M, Rx_sig, Symbols_l, Tx_bits_l, ...
|
||||
"precode_mode", duob_mode, ...
|
||||
'showAnalysis', 0, ...
|
||||
"postFFE", [], ...
|
||||
"eth_style_symbol_mapping", 0);
|
||||
|
||||
ber = ffe_results.metrics.BER;
|
||||
|
||||
if ber == 0
|
||||
ber = 1e-10;
|
||||
end
|
||||
|
||||
if ~isfinite(ber)
|
||||
ber = 0.5;
|
||||
end
|
||||
|
||||
fprintf(' mu_dd=%.4e, mu_tr=%.4e -> BER=%.4e\n', mu_dd, mu_tr, ber);
|
||||
|
||||
catch ME
|
||||
fprintf(' mu_dd=%.4e, mu_tr=%.4e -> FAILED (%s)\n', mu_dd, mu_tr, ME.message);
|
||||
ber = 0.5;
|
||||
end
|
||||
end
|
||||
|
||||
%% Define optimizable variables
|
||||
mu_dd_var = optimizableVariable('mu_dd', [1e-5, 0.1], 'Transform', 'log');
|
||||
mu_tr_var = optimizableVariable('mu_tr', [1e-5, 0.1], 'Transform', 'log');
|
||||
|
||||
%% Run Bayesian Optimization
|
||||
fprintf('========== Starting Bayesian Optimization ==========\n');
|
||||
fprintf('Optimizing mu_dd and mu_tr to minimize BER\n');
|
||||
fprintf('Search range: mu_dd=[1e-5, 0.1], mu_tr=[1e-5, 0.1]\n\n');
|
||||
|
||||
objective_fn = @(params) ffe_objective(params, Rx_sig, Symbols{l}, Tx_bits{l}, s.M, duob_mode);
|
||||
|
||||
results = bayesopt(objective_fn, [mu_dd_var, mu_tr_var], ...
|
||||
'MaxObjectiveEvaluations', 30, ...
|
||||
'AcquisitionFunctionName', 'expected-improvement-plus', ...
|
||||
'IsObjectiveDeterministic', false, ...
|
||||
'ExplorationRatio', 0.5, ...
|
||||
'Verbose', 1, ...
|
||||
'PlotFcn', []);
|
||||
|
||||
%% Display Results
|
||||
fprintf('\n========== FFE Optimization Complete ==========\n');
|
||||
fprintf('Best FFE parameters found:\n');
|
||||
fprintf(' mu_dd = %.6e\n', results.XAtMinObjective.mu_dd);
|
||||
fprintf(' mu_tr = %.6e\n', results.XAtMinObjective.mu_tr);
|
||||
fprintf(' BER = %.6e\n', results.MinObjective);
|
||||
|
||||
%% Verify with optimal parameters
|
||||
fprintf('\nVerifying optimal FFE parameters...\n');
|
||||
best_mu_dd = results.XAtMinObjective.mu_dd;
|
||||
best_mu_tr = results.XAtMinObjective.mu_tr;
|
||||
|
||||
eq_ffe_best = FFE("epochs_tr", 5, "epochs_dd", 2, "len_tr", 2^13, ...
|
||||
"mu_dd", best_mu_dd, "mu_tr", best_mu_tr, ...
|
||||
"order", 50, "sps", 2, "decide", 0, ...
|
||||
"adaption", adaption_method.nlms, "dd_mode", 1);
|
||||
|
||||
ffe_results_best = ffe(eq_ffe_best, s.M, Rx_sig, Symbols{l}, Tx_bits{l}, ...
|
||||
"precode_mode", duob_mode, ...
|
||||
'showAnalysis', 1, ...
|
||||
"postFFE", [], ...
|
||||
"eth_style_symbol_mapping", 0);
|
||||
|
||||
fprintf('\nFinal FFE BER with optimal parameters: %.6e\n', ffe_results_best.metrics.BER);
|
||||
349
test/gpu_cpu_comparison.m
Normal file
349
test/gpu_cpu_comparison.m
Normal file
@@ -0,0 +1,349 @@
|
||||
%% GPU vs CPU Comparison Test for DP_Fiber
|
||||
% This script runs the fiber simulation with and without GPU acceleration
|
||||
% and compares the numerical results.
|
||||
|
||||
% clear; clc;
|
||||
|
||||
%% Setup (same as gpu_processing_dpfiber.m but simplified)
|
||||
s.wavelengthplan = calcWavelengthPlan(4, 400e9, 1310);
|
||||
link_length = 10;
|
||||
s.pmd = 0.1;
|
||||
s.gamma = 0.0023;
|
||||
|
||||
s.M = 4;
|
||||
fsym = 112e9;
|
||||
fdac = 2*fsym;
|
||||
fadc = 120000000000;
|
||||
s.random_key = 1;
|
||||
|
||||
% Laser / Modulator
|
||||
vbias_rel = 0.5;
|
||||
u_pi = 4.6;
|
||||
vbias = -vbias_rel*u_pi;
|
||||
laser_linewidth = 0e6;
|
||||
|
||||
% DB Stuff
|
||||
duob_mode = db_mode.no_db;
|
||||
|
||||
rcalpha = 0.05;
|
||||
Pform = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rc","pulselength",16,"alpha",rcalpha);
|
||||
|
||||
s.chirpalpha = 0;
|
||||
s.p_launch = 3;
|
||||
s.p = "co";
|
||||
|
||||
N = numel(s.wavelengthplan);
|
||||
|
||||
switch s.p
|
||||
case "co"
|
||||
pol_rot = 100.*ones(1,N);
|
||||
d_local = 0;
|
||||
case "pair"
|
||||
pol_rot = repmat([100,100,0,0],1,N/4);
|
||||
d_local = 0;
|
||||
case "alt"
|
||||
pol_rot = repmat([100,0,100,0],1,N/4);
|
||||
d_local = 0;
|
||||
case "seg"
|
||||
pol_rot = 100.*ones(1,N);
|
||||
d_local = 3;
|
||||
otherwise
|
||||
error('Unknown fwm_mitigation_technique: %s', string(s.p));
|
||||
end
|
||||
|
||||
f_plan = physconst('lightspeed')./(s.wavelengthplan.*1e-9);
|
||||
margin = 5e12;
|
||||
f_span = (max(f_plan)+margin)-(min(f_plan)-margin);
|
||||
f_nyq = f_span/2;
|
||||
|
||||
kover = 4;
|
||||
upsample_required = f_nyq./(fdac*kover/2);
|
||||
upsample_pow = 2^nextpow2(upsample_required);
|
||||
|
||||
s.f_opt = fdac*kover*upsample_pow;
|
||||
s.f_opt_nyq = s.f_opt/2;
|
||||
|
||||
%% TX per channel
|
||||
for l = 1:N
|
||||
[Digi_sig,Symbols{l},Tx_bits{l}] = PAMsource( ...
|
||||
"fsym",fsym,"M",s.M,"order",15,"useprbs",0, ...
|
||||
"fs_out",fdac, ...
|
||||
"applyclipping",0,"clipfactor",1.5, ...
|
||||
"applypulseform",1,"pulseformer",Pform, ...
|
||||
"randkey",s.random_key+l, ...
|
||||
"mrds_code",0,"mrds_blocklength",512,"duobinary_mode",duob_mode ...
|
||||
).process();
|
||||
|
||||
Lp_awg = Filter('filtdegree',3,"f_cutoff",56e9,"fs",fdac*kover, ...
|
||||
"filterType",filtertypes.gaussian,"active",true);
|
||||
|
||||
El_sig = AWG("fdac",fdac,"f_cutoff",fsym,"lpf_active",1,"kover",kover, ...
|
||||
"bit_resolution",6,"upsampling_method","samplehold","precomp_sinc_rolloff",0, ...
|
||||
"H_lpf",Lp_awg,"dac_max",0.6,"dac_min",-0.6).process(Digi_sig);
|
||||
|
||||
clear Digi_sig
|
||||
|
||||
El_sig = El_sig.normalize("mode","oneone");
|
||||
scaling = 0.6*(u_pi/2-abs(vbias-u_pi/2));
|
||||
El_sig = El_sig .* scaling;
|
||||
|
||||
Eml_out = EML("mode",eml_mode.im_cosinus,"power",3,"fsimu",El_sig.fs, ...
|
||||
"lambda",s.wavelengthplan(l),"bias",vbias,"u_pi",u_pi, ...
|
||||
"linewidth",laser_linewidth,"randomkey",s.random_key+l,"alpha",s.chirpalpha).process(El_sig);
|
||||
|
||||
clear El_sig
|
||||
|
||||
signal_cell{l} = Polarization_Controller("mode","rot_power","desired_power",pol_rot(l)).process(Eml_out);
|
||||
|
||||
clear Eml_out Lp_awg
|
||||
end
|
||||
|
||||
disp('Signal generated for all channels.');
|
||||
|
||||
%% WDM mux + launch
|
||||
Opt_sig_wdm = Optical_Multiplex("fs_in",fdac*kover,"fs_out",upsample_pow*fdac*kover, ...
|
||||
"lambda_center",1310,"random_key",0,"filtype",1,"B",120e9).process(signal_cell);
|
||||
|
||||
Opt_sig_wdm = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power", ...
|
||||
"amplification_db",s.p_launch+10*log10(N)).process(Opt_sig_wdm);
|
||||
|
||||
% Save input for both runs
|
||||
Opt_sig_input = Opt_sig_wdm;
|
||||
|
||||
segment_length = 1;
|
||||
nSegments = link_length/segment_length;
|
||||
if abs(nSegments - round(nSegments)) > 1e-12
|
||||
error('fiber_length_km=%g must be an integer multiple of segment_length=%g km.', link_length, segment_length);
|
||||
end
|
||||
nSegments = round(nSegments);
|
||||
|
||||
zdw = 1310;
|
||||
randomize_D = true;
|
||||
|
||||
if nSegments > 0
|
||||
Dvec = getDispersionVector(nSegments, d_local, zdw, randomize_D, s.random_key);
|
||||
else
|
||||
Dvec = [];
|
||||
end
|
||||
|
||||
%% Run WITHOUT GPU
|
||||
fprintf('\n========== Running WITHOUT GPU (CPU) ==========\n');
|
||||
Opt_sig_cpu = Opt_sig_input;
|
||||
|
||||
tic;
|
||||
for seg = 1:nSegments
|
||||
fprintf('CPU Segment %d/%d \n',seg, nSegments);
|
||||
|
||||
Opt_sig_cpu = DP_Fiber("L",segment_length,"D",Dvec(seg),"Dpmd",s.pmd,"Ds",0.07, ...
|
||||
"beat_len",10,"corr_len",100,"dz",1,"manakov",0, ...
|
||||
"gamma",s.gamma,"lambda",zdw,"n_waveplates",10,"SS_dphimax",0.01, ...
|
||||
"SS_dzmax",50,"SS_dzmin",10,"X_alpha",0.3,"X_beta",0,"rng",1,"useGPU",false).process(Opt_sig_cpu);
|
||||
end
|
||||
time_cpu = toc;
|
||||
fprintf('CPU Time: %.3f seconds\n', time_cpu);
|
||||
|
||||
%% Run WITH GPU (Double Precision)
|
||||
fprintf('\n========== Running WITH GPU (Double Precision) ==========\n');
|
||||
Opt_sig_gpu_double = Opt_sig_input;
|
||||
|
||||
tic;
|
||||
for seg = 1:nSegments
|
||||
fprintf('GPU-Double Segment %d/%d \n',seg, nSegments);
|
||||
|
||||
Opt_sig_gpu_double = DP_Fiber("L",segment_length,"D",Dvec(seg),"Dpmd",s.pmd,"Ds",0.07, ...
|
||||
"beat_len",10,"corr_len",100,"dz",1,"manakov",0, ...
|
||||
"gamma",s.gamma,"lambda",zdw,"n_waveplates",10,"SS_dphimax",0.01, ...
|
||||
"SS_dzmax",50,"SS_dzmin",10,"X_alpha",0.3,"X_beta",0,"rng",1,"useGPU",true,"useSingle",false).process(Opt_sig_gpu_double);
|
||||
end
|
||||
time_gpu_double = toc;
|
||||
fprintf('GPU Double Time: %.3f seconds\n', time_gpu_double);
|
||||
|
||||
%% Run WITH GPU (Single Precision)
|
||||
fprintf('\n========== Running WITH GPU (Single Precision) ==========\n');
|
||||
Opt_sig_gpu_single = Opt_sig_input;
|
||||
|
||||
tic;
|
||||
for seg = 1:nSegments
|
||||
fprintf('GPU-Single Segment %d/%d \n',seg, nSegments);
|
||||
|
||||
Opt_sig_gpu_single = DP_Fiber("L",segment_length,"D",Dvec(seg),"Dpmd",s.pmd,"Ds",0.07, ...
|
||||
"beat_len",10,"corr_len",100,"dz",1,"manakov",0, ...
|
||||
"gamma",s.gamma,"lambda",zdw,"n_waveplates",10,"SS_dphimax",0.01, ...
|
||||
"SS_dzmax",50,"SS_dzmin",10,"X_alpha",0.3,"X_beta",0,"rng",1,"useGPU",true,"useSingle",true).process(Opt_sig_gpu_single);
|
||||
end
|
||||
time_gpu_single = toc;
|
||||
fprintf('GPU Single Time: %.3f seconds\n', time_gpu_single);
|
||||
|
||||
%% Compare Results
|
||||
fprintf('\n========== Numerical Comparison ==========\n');
|
||||
|
||||
sig_cpu = Opt_sig_cpu.signal;
|
||||
sig_gpu_double = Opt_sig_gpu_double.signal;
|
||||
sig_gpu_single = Opt_sig_gpu_single.signal;
|
||||
|
||||
% Check dimensions
|
||||
fprintf('CPU signal size: [%d x %d]\n', size(sig_cpu,1), size(sig_cpu,2));
|
||||
fprintf('GPU Double signal size: [%d x %d]\n', size(sig_gpu_double,1), size(sig_gpu_double,2));
|
||||
fprintf('GPU Single signal size: [%d x %d]\n', size(sig_gpu_single,1), size(sig_gpu_single,2));
|
||||
|
||||
% CPU vs GPU Double
|
||||
fprintf('\n--- CPU vs GPU Double ---\n');
|
||||
diff_cpu_double = abs(sig_cpu - sig_gpu_double);
|
||||
max_diff_cpu_double = max(diff_cpu_double(:));
|
||||
mean_diff_cpu_double = mean(diff_cpu_double(:));
|
||||
rel_diff_cpu_double = max_diff_cpu_double / max(abs(sig_cpu(:)));
|
||||
fprintf('Max absolute difference: %.6e\n', max_diff_cpu_double);
|
||||
fprintf('Mean absolute difference: %.6e\n', mean_diff_cpu_double);
|
||||
fprintf('Max relative difference: %.6e\n', rel_diff_cpu_double);
|
||||
|
||||
% CPU vs GPU Single
|
||||
fprintf('\n--- CPU vs GPU Single ---\n');
|
||||
diff_cpu_single = abs(sig_cpu - sig_gpu_single);
|
||||
max_diff_cpu_single = max(diff_cpu_single(:));
|
||||
mean_diff_cpu_single = mean(diff_cpu_single(:));
|
||||
rel_diff_cpu_single = max_diff_cpu_single / max(abs(sig_cpu(:)));
|
||||
fprintf('Max absolute difference: %.6e\n', max_diff_cpu_single);
|
||||
fprintf('Mean absolute difference: %.6e\n', mean_diff_cpu_single);
|
||||
fprintf('Max relative difference: %.6e\n', rel_diff_cpu_single);
|
||||
|
||||
% GPU Double vs GPU Single
|
||||
fprintf('\n--- GPU Double vs GPU Single ---\n');
|
||||
diff_double_single = abs(sig_gpu_double - sig_gpu_single);
|
||||
max_diff_double_single = max(diff_double_single(:));
|
||||
mean_diff_double_single = mean(diff_double_single(:));
|
||||
rel_diff_double_single = max_diff_double_single / max(abs(sig_gpu_double(:)));
|
||||
fprintf('Max absolute difference: %.6e\n', max_diff_double_single);
|
||||
fprintf('Mean absolute difference: %.6e\n', mean_diff_double_single);
|
||||
fprintf('Max relative difference: %.6e\n', rel_diff_double_single);
|
||||
|
||||
% Check tolerances
|
||||
fprintf('\n--- Tolerance Check ---\n');
|
||||
tol_double = 1e-10;
|
||||
tol_single = 1e-5; % Single precision has ~7 significant digits
|
||||
|
||||
if max_diff_cpu_double < tol_double
|
||||
fprintf('✓ CPU vs GPU Double: EQUIVALENT (diff < %.0e)\n', tol_double);
|
||||
else
|
||||
fprintf('✗ CPU vs GPU Double: DIFFER beyond tolerance (%.0e)\n', tol_double);
|
||||
end
|
||||
|
||||
if max_diff_cpu_single < tol_single
|
||||
fprintf('✓ CPU vs GPU Single: ACCEPTABLE (diff < %.0e)\n', tol_single);
|
||||
else
|
||||
fprintf('⚠ CPU vs GPU Single: Precision loss detected (diff = %.2e, tol = %.0e)\n', max_diff_cpu_single, tol_single);
|
||||
end
|
||||
|
||||
% Performance comparison
|
||||
fprintf('\n========== Performance Summary ==========\n');
|
||||
fprintf('CPU Time: %.3f s\n', time_cpu);
|
||||
fprintf('GPU Double Time: %.3f s\n', time_gpu_double);
|
||||
fprintf('GPU Single Time: %.3f s\n', time_gpu_single);
|
||||
fprintf('\n');
|
||||
fprintf('Speedup (GPU Double vs CPU): %.2fx\n', time_cpu/time_gpu_double);
|
||||
fprintf('Speedup (GPU Single vs CPU): %.2fx\n', time_cpu/time_gpu_single);
|
||||
fprintf('Speedup (GPU Single vs GPU Double): %.2fx\n', time_gpu_double/time_gpu_single);
|
||||
|
||||
%% ========== BER Comparison ==========
|
||||
% Process each fiber output through simplified receiver to check if
|
||||
% single-precision affects actual BER performance
|
||||
|
||||
fprintf('\n========== BER Comparison ==========\n');
|
||||
fprintf('Processing signals through receiver chain...\n');
|
||||
|
||||
% Receiver parameters
|
||||
rop = -7; % Received optical power [dBm]
|
||||
len_tr = 4096; % Training length
|
||||
mu_dc = 0.005;
|
||||
mu_ffe = [0.0001 0.0008 0.001];
|
||||
mu_dfe = 0.0004;
|
||||
|
||||
% Helper function to process through receiver and get BER
|
||||
function ber = process_receiver(Opt_sig_fib, l, Symbols, Tx_bits, ...
|
||||
fdac, kover, upsample_pow, fsym, fadc, rop, s, len_tr, mu_dc, mu_ffe, mu_dfe, duob_mode)
|
||||
|
||||
% Demux single channel
|
||||
Opt_sig_demux = Optical_Demultiplex("attenuation",0,"B",200e9,"filtype",1, ...
|
||||
"fs_out",fdac*kover,"fs_in",fdac*kover*upsample_pow,"lambda_center",1310).process(Opt_sig_fib);
|
||||
|
||||
% ROP amplifier
|
||||
Opt_sig_rx = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power", ...
|
||||
"amplification_db",rop).process(Opt_sig_demux{l});
|
||||
|
||||
% Photodiode
|
||||
PD_sig = Photodiode("fsimu",fdac*kover,"dark_current",2e-08,"responsivity",1,"temperature",20, ...
|
||||
"nep",1.8e-11,"randomkey",s.random_key+l).process(Opt_sig_rx);
|
||||
|
||||
% Low-pass filter
|
||||
rx_bwl = 100e9;
|
||||
PD_sig = Filter('filtdegree',4,"f_cutoff",rx_bwl,"fs",fdac*kover, ...
|
||||
"filterType",filtertypes.butterworth,"active",true).process(PD_sig);
|
||||
|
||||
% Scope
|
||||
Lp_scpe = Filter('filtdegree',4,"f_cutoff",80e9,"fs",fadc,"filterType",filtertypes.butterworth,"active",true);
|
||||
Scpe_sig = Scope("fsimu",fdac*kover,"fadc",fadc, ...
|
||||
"delay",0,"fixed_delay",0,"filtertype",filtertypes.butterworth, ...
|
||||
"samplingdelay",0,"rand_samplingdelay",0,"freq_offset",0,"samp_jitter",0, ...
|
||||
"adcresolution",8,"quantbuffer",0.1,'block_dc',1,'lpf_active',0,'H_lpf',Lp_scpe).process(PD_sig);
|
||||
|
||||
% Resample to 2 sps
|
||||
Scpe_sig_2sps = Scpe_sig.resample("fs_out",2*fsym);
|
||||
|
||||
% Time sync
|
||||
[~, Scpe_cell, ~, ~] = Scpe_sig_2sps.tsynch("reference", Symbols{l}, "fs_ref", fsym, "debug_plots", 0);
|
||||
Rx_sig = Scpe_cell{1};
|
||||
Rx_sig = Rx_sig.normalize("mode","rms");
|
||||
|
||||
% FFE Equalizer
|
||||
ffe_order = [50, 0, 0];
|
||||
eq_ffe = EQ("Ne",ffe_order,"Nb",[0,0,0],"training_length",len_tr,"training_loops",5,"dd_loops",5, ...
|
||||
"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",0);
|
||||
|
||||
ffe_results = ffe(eq_ffe,s.M,Rx_sig,Symbols{l},Tx_bits{l}, ...
|
||||
"precode_mode",duob_mode, ...
|
||||
'showAnalysis',0, ...
|
||||
"postFFE",[], ...
|
||||
"eth_style_symbol_mapping",0);
|
||||
|
||||
ber = ffe_results.metrics.BER;
|
||||
end
|
||||
|
||||
% Process each mode for channel 1
|
||||
l = 4; % Use first channel for comparison
|
||||
|
||||
fprintf('Processing CPU result...\n');
|
||||
ber_cpu = process_receiver(Opt_sig_cpu, l, Symbols, Tx_bits, ...
|
||||
fdac, kover, upsample_pow, fsym, fadc, rop, s, len_tr, mu_dc, mu_ffe, mu_dfe, duob_mode);
|
||||
|
||||
fprintf('Processing GPU Double result...\n');
|
||||
ber_gpu_double = process_receiver(Opt_sig_gpu_double, l, Symbols, Tx_bits, ...
|
||||
fdac, kover, upsample_pow, fsym, fadc, rop, s, len_tr, mu_dc, mu_ffe, mu_dfe, duob_mode);
|
||||
|
||||
fprintf('Processing GPU Single result...\n');
|
||||
ber_gpu_single = process_receiver(Opt_sig_gpu_single, l, Symbols, Tx_bits, ...
|
||||
fdac, kover, upsample_pow, fsym, fadc, rop, s, len_tr, mu_dc, mu_ffe, mu_dfe, duob_mode);
|
||||
|
||||
% Display BER results
|
||||
fprintf('\n========== BER Results (Channel %d, ROP = %d dBm) ==========\n', l, rop);
|
||||
fprintf('CPU: BER = %.4e\n', ber_cpu);
|
||||
fprintf('GPU Double: BER = %.4e\n', ber_gpu_double);
|
||||
fprintf('GPU Single: BER = %.4e\n', ber_gpu_single);
|
||||
|
||||
fprintf('\n--- BER Comparison ---\n');
|
||||
if ber_cpu == 0 && ber_gpu_double == 0 && ber_gpu_single == 0
|
||||
fprintf('✓ All BERs are zero (no errors detected)\n');
|
||||
else
|
||||
ber_diff_double = abs(ber_cpu - ber_gpu_double);
|
||||
ber_diff_single = abs(ber_cpu - ber_gpu_single);
|
||||
fprintf('|BER_cpu - BER_gpu_double| = %.4e\n', ber_diff_double);
|
||||
fprintf('|BER_cpu - BER_gpu_single| = %.4e\n', ber_diff_single);
|
||||
|
||||
if ber_diff_double < 1e-6 && ber_diff_single < 1e-6
|
||||
fprintf('✓ BER differences are negligible\n');
|
||||
elseif ber_diff_single > ber_diff_double * 10
|
||||
fprintf('⚠ Single precision shows measurable BER impact\n');
|
||||
else
|
||||
fprintf('✓ BER differences within acceptable range\n');
|
||||
end
|
||||
end
|
||||
|
||||
fprintf('\n========== Test Complete ==========\n');
|
||||
265
test/gpu_processing_dpfiber.m
Normal file
265
test/gpu_processing_dpfiber.m
Normal file
@@ -0,0 +1,265 @@
|
||||
|
||||
|
||||
s.wavelengthplan = calcWavelengthPlan(16, 400e9, 1310);
|
||||
N = numel(s.wavelengthplan);
|
||||
link_length = 10;
|
||||
s.pmd = 0.1;%0.1;
|
||||
s.gamma = 0.0023;
|
||||
|
||||
s.M = 4;
|
||||
fsym = 112e9;
|
||||
fdac = 2*fsym;
|
||||
fadc = 120000000000;
|
||||
s.random_key = 1;
|
||||
|
||||
% Laser / s.Modulator
|
||||
vbias_rel = 0.5;
|
||||
u_pi = 4.6;
|
||||
vbias = -vbias_rel*u_pi;
|
||||
laser_linewidth = 0e6;
|
||||
|
||||
% DB Stuff
|
||||
duob_mode = db_mode.no_db;
|
||||
|
||||
rcalpha = 0.05;
|
||||
Pform = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rc","pulselength",16,"alpha",rcalpha);
|
||||
|
||||
s.chirpalpha = 0;
|
||||
|
||||
s.p_launch = 3;
|
||||
s.p = "co";
|
||||
|
||||
switch s.p
|
||||
case "co"
|
||||
pol_rot = 100.*ones(1,N);
|
||||
d_local = 0;
|
||||
case "pair"
|
||||
pol_rot = repmat([100,100,0,0],1,N/4);
|
||||
d_local = 0;
|
||||
case "alt"
|
||||
pol_rot = repmat([100,0,100,0],1,N/4);
|
||||
d_local = 0;
|
||||
case "seg"
|
||||
pol_rot = 100.*ones(1,N);
|
||||
d_local = 3;
|
||||
otherwise
|
||||
error('Unknown fwm_mitigation_technique: %s', string(s.p));
|
||||
end
|
||||
|
||||
f_plan = physconst('lightspeed')./(s.wavelengthplan.*1e-9);
|
||||
margin = 25e12; % some THz left and right
|
||||
f_span = (max(f_plan)+margin)-(min(f_plan)-margin);
|
||||
f_nyq = f_span/2;
|
||||
|
||||
kover = 4;
|
||||
upsample_required = f_nyq./(fdac*kover/2);
|
||||
upsample_pow = 2^nextpow2(upsample_required);
|
||||
|
||||
s.f_opt = fdac*kover*upsample_pow;
|
||||
s.f_opt_nyq = s.f_opt/2;
|
||||
|
||||
s.rop = -10:1:0;
|
||||
|
||||
profile on
|
||||
|
||||
%% ---------- TX per channel ----------
|
||||
for l = 1:N
|
||||
|
||||
[Digi_sig,Symbols{l},Tx_bits{l}] = PAMsource( ...
|
||||
"fsym",fsym,"M",s.M,"order",17,"useprbs",0, ...
|
||||
"fs_out",fdac, ...
|
||||
"applyclipping",0,"clipfactor",1.5, ...
|
||||
"applypulseform",1,"pulseformer",Pform, ...
|
||||
"randkey",s.random_key+l, ...
|
||||
"mrds_code",0,"mrds_blocklength",512,"duobinary_mode",duob_mode ...
|
||||
).process();
|
||||
|
||||
Lp_awg = Filter('filtdegree',3,"f_cutoff",56e9,"fs",fdac*kover, ...
|
||||
"filterType",filtertypes.gaussian,"active",true);
|
||||
|
||||
El_sig = AWG("fdac",fdac,"f_cutoff",fsym,"lpf_active",1,"kover",kover, ...
|
||||
"bit_resolution",6,"upsampling_method","samplehold","precomp_sinc_rolloff",0, ...
|
||||
"H_lpf",Lp_awg,"dac_max",0.6,"dac_min",-0.6).process(Digi_sig);
|
||||
|
||||
% Digi_sig not needed after AWG
|
||||
clear Digi_sig
|
||||
|
||||
% Electrical Driver Amplifier
|
||||
El_sig = El_sig.normalize("mode","oneone");
|
||||
scaling = 0.6*(u_pi/2-abs(vbias-u_pi/2));
|
||||
El_sig = El_sig .* scaling;
|
||||
|
||||
% E/O Conversion
|
||||
Eml_out = EML("mode",eml_mode.im_cosinus,"power",3,"fsimu",El_sig.fs, ...
|
||||
"lambda",s.wavelengthplan(l),"bias",vbias,"u_pi",u_pi, ...
|
||||
"linewidth",laser_linewidth,"randomkey",s.random_key+l,"alpha",s.chirpalpha).process(El_sig);
|
||||
|
||||
% El_sig not needed after EML
|
||||
clear El_sig
|
||||
|
||||
signal_cell{l} = Polarization_Controller("mode","rot_power","desired_power",pol_rot(l)).process(Eml_out);
|
||||
|
||||
% Eml_out not needed after pol controller
|
||||
clear Eml_out Lp_awg
|
||||
end
|
||||
|
||||
disp('Signal generated for all channels.');
|
||||
|
||||
%% ---------- WDM mux + launch ----------
|
||||
Opt_sig_wdm = Optical_Multiplex("fs_in",fdac*kover,"fs_out",upsample_pow*fdac*kover, ...
|
||||
"lambda_center",1310,"random_key",0,"filtype",1,"B",120e9).process(signal_cell);
|
||||
|
||||
Opt_sig_wdm.spectrum();
|
||||
|
||||
Opt_sig_wdm = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power", ...
|
||||
"amplification_db",s.p_launch+10*log10(N)).process(Opt_sig_wdm);
|
||||
|
||||
Opt_sig_wdm_fib = Opt_sig_wdm;
|
||||
|
||||
segment_length = 1;
|
||||
nSegments = link_length/segment_length;
|
||||
if abs(nSegments - round(nSegments)) > 1e-12
|
||||
error('fiber_length_km=%g must be an integer multiple of segment_length=%g km.', link_length, segment_length);
|
||||
end
|
||||
nSegments = round(nSegments);
|
||||
|
||||
zdw = 1310;
|
||||
randomize_D = true;
|
||||
|
||||
% Guard for 0 km: avoid calling getDispersionVector(0,...) if it doesn't support it
|
||||
if nSegments > 0
|
||||
Dvec = getDispersionVector(nSegments, d_local, zdw, randomize_D, s.random_key);
|
||||
else
|
||||
Dvec = [];
|
||||
end
|
||||
|
||||
for seg = 1:nSegments
|
||||
|
||||
fprintf('Segment %d/%d \n',seg, nSegments);
|
||||
|
||||
Opt_sig_wdm_fib = DP_Fiber("L",segment_length,"D",Dvec(seg),"Dpmd",s.pmd,"Ds",0.07, ...
|
||||
"beat_len",10,"corr_len",100,"dz",1,"manakov",0, ...
|
||||
"gamma",s.gamma,"lambda",zdw,"n_waveplates",10,"SS_dphimax",0.01, ...
|
||||
"SS_dzmax",50,"SS_dzmin",10,"X_alpha",0.3,"X_beta",0,"rng",1,"useGPU",true,"useSingle",1).process(Opt_sig_wdm_fib);
|
||||
|
||||
end
|
||||
|
||||
profile off
|
||||
profile viewer
|
||||
|
||||
Opt_sig_wdm_fib.spectrum();
|
||||
|
||||
%% ========== BER Evaluation ==========
|
||||
fprintf('\n========== BER Evaluation ==========\n');
|
||||
fprintf('Processing signals through receiver chain...\n');
|
||||
|
||||
% Receiver parameters
|
||||
len_tr = 4096; % Training length
|
||||
mu_dc = 0.005;
|
||||
mu_ffe = [0.0001 0.0008 0.001];
|
||||
mu_dfe = 0.0004;
|
||||
|
||||
% Helper function to process through receiver and get BER
|
||||
function ber = process_receiver(Opt_sig_fib, l, Symbols, Tx_bits, ...
|
||||
fdac, kover, upsample_pow, fsym, fadc, rop, s, len_tr, mu_dc, mu_ffe, mu_dfe, duob_mode)
|
||||
|
||||
% Demux single channel
|
||||
Opt_sig_demux = Optical_Demultiplex("attenuation",0,"B",200e9,"filtype",1, ...
|
||||
"fs_out",fdac*kover,"fs_in",fdac*kover*upsample_pow,"lambda_center",1310).process(Opt_sig_fib);
|
||||
|
||||
% ROP amplifier
|
||||
Opt_sig_rx = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power", ...
|
||||
"amplification_db",rop).process(Opt_sig_demux{l});
|
||||
|
||||
% Photodiode
|
||||
PD_sig = Photodiode("fsimu",fdac*kover,"dark_current",2e-08,"responsivity",1,"temperature",20, ...
|
||||
"nep",1.8e-11,"randomkey",s.random_key+l).process(Opt_sig_rx);
|
||||
|
||||
% Low-pass filter
|
||||
rx_bwl = 100e9;
|
||||
PD_sig = Filter('filtdegree',4,"f_cutoff",rx_bwl,"fs",fdac*kover, ...
|
||||
"filterType",filtertypes.butterworth,"active",true).process(PD_sig);
|
||||
|
||||
% Scope
|
||||
Lp_scpe = Filter('filtdegree',4,"f_cutoff",80e9,"fs",fadc,"filterType",filtertypes.butterworth,"active",true);
|
||||
Scpe_sig = Scope("fsimu",fdac*kover,"fadc",fadc, ...
|
||||
"delay",0,"fixed_delay",0,"filtertype",filtertypes.butterworth, ...
|
||||
"samplingdelay",0,"rand_samplingdelay",0,"freq_offset",0,"samp_jitter",0, ...
|
||||
"adcresolution",8,"quantbuffer",0.1,'block_dc',1,'lpf_active',0,'H_lpf',Lp_scpe).process(PD_sig);
|
||||
|
||||
% Resample to 2 sps
|
||||
Scpe_sig_2sps = Scpe_sig.resample("fs_out",2*fsym);
|
||||
|
||||
% Time sync
|
||||
[~, Scpe_cell, ~, ~] = Scpe_sig_2sps.tsynch("reference", Symbols{l}, "fs_ref", fsym, "debug_plots", 0);
|
||||
Rx_sig = Scpe_cell{1};
|
||||
Rx_sig = Rx_sig.normalize("mode","rms");
|
||||
|
||||
% FFE Equalizer
|
||||
ffe_order = [50, 0, 0];
|
||||
eq_ffe = EQ("Ne",ffe_order,"Nb",[0,0,0],"training_length",len_tr,"training_loops",5,"dd_loops",5, ...
|
||||
"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",0);
|
||||
eq_ffe = FFE("epochs_tr",5,"epochs_dd",2,"len_tr",2^13,"mu_dd",6.624e-05,"mu_tr",0.058136,"order",50,"sps",2,"decide",0, "adaption",adaption_method.nlms,"dd_mode",1);
|
||||
|
||||
ffe_results = ffe(eq_ffe,s.M,Rx_sig,Symbols{l},Tx_bits{l}, ...
|
||||
"precode_mode",duob_mode, ...
|
||||
'showAnalysis',0, ...
|
||||
"postFFE",[], ...
|
||||
"eth_style_symbol_mapping",0);
|
||||
|
||||
ber = ffe_results.metrics.BER;
|
||||
end
|
||||
|
||||
% Process each ROP value for selected channels using parfor
|
||||
ber_results = zeros(length(s.rop), N);
|
||||
|
||||
% Flatten loop for parfor: iterate over all (ROP, channel) combinations
|
||||
num_rop = length(s.rop);
|
||||
rop_vals = s.rop;
|
||||
ber_flat = zeros(num_rop * N, 1);
|
||||
|
||||
parfor idx = 1:(num_rop * N)
|
||||
% Convert linear index to (ri, l) subscripts
|
||||
ri = ceil(idx / N);
|
||||
l = mod(idx - 1, N) + 1;
|
||||
|
||||
fprintf('ROP %d dBm, Channel %d/%d\n', rop_vals(ri), l, N);
|
||||
ber_flat(idx) = process_receiver(Opt_sig_wdm_fib, l, Symbols, Tx_bits, ...
|
||||
fdac, kover, upsample_pow, fsym, fadc, rop_vals(ri), s, len_tr, mu_dc, mu_ffe, mu_dfe, duob_mode);
|
||||
end
|
||||
|
||||
% Reshape back to [num_rop × N] matrix
|
||||
ber_results = reshape(ber_flat, [N, num_rop]).';
|
||||
|
||||
|
||||
%% Display BER Results
|
||||
fprintf('\n========== BER Results ==========\n');
|
||||
fprintf('ROP [dBm] | ');
|
||||
for l = 1:N
|
||||
fprintf('Ch%d | ', l);
|
||||
end
|
||||
fprintf('\n');
|
||||
|
||||
for ri = 1:length(s.rop)
|
||||
fprintf('%8d | ', s.rop(ri));
|
||||
for l = 1:N
|
||||
fprintf('%.2e | ', ber_results(ri, l));
|
||||
end
|
||||
fprintf('\n');
|
||||
end
|
||||
|
||||
% Plot BER vs ROP
|
||||
figure;
|
||||
semilogy(s.rop, mean(ber_results, 2), '-o', 'LineWidth', 2);
|
||||
hold on;
|
||||
for l = 1:N
|
||||
semilogy(s.rop, ber_results(:, l), '--', 'LineWidth', 1);
|
||||
end
|
||||
hold off;
|
||||
xlabel('ROP [dBm]');
|
||||
ylabel('BER');
|
||||
title('BER vs Received Optical Power (GPU Single Precision)');
|
||||
legend(['Mean', arrayfun(@(x) sprintf('Ch%d', x), 1:N, 'UniformOutput', false)]);
|
||||
grid on;
|
||||
|
||||
fprintf('\n========== Test Complete ==========\n');
|
||||
70
test/gpu_processing_test.m
Normal file
70
test/gpu_processing_test.m
Normal file
@@ -0,0 +1,70 @@
|
||||
% 1. Setup Data OUTSIDE the timer
|
||||
d = gpuDevice;
|
||||
N = 10000;
|
||||
|
||||
fprintf('Preparing data...\n');
|
||||
A_cpu_double = rand(N, N); % Create double on CPU
|
||||
A_cpu_single = single(A_cpu_double); % Create single on CPU
|
||||
|
||||
% Warmup run (wakes up the GPU from idle state)
|
||||
A_warm = gpuArray.rand(1000, 1000, 'single');
|
||||
B_warm = A_warm * A_warm;
|
||||
wait(d);
|
||||
|
||||
fprintf('------------------------------------------------\n');
|
||||
|
||||
% TEST 1: Double Precision (The "Slow" way)
|
||||
% We move data to GPU first so we only measure calculation time
|
||||
A_gpu_double = gpuArray(A_cpu_double);
|
||||
wait(d); % Ensure transfer is done before starting timer
|
||||
|
||||
fprintf('Running DOUBLE precision test... ');
|
||||
tic;
|
||||
B_gpu = A_gpu_double * A_gpu_double;
|
||||
wait(d); % FORCE MATLAB TO WAIT FOR GPU
|
||||
time_double = toc;
|
||||
fprintf('Done.\n');
|
||||
fprintf('Double Precision Time: %.4f seconds\n', time_double);
|
||||
|
||||
% TEST 2: Single Precision (The "Fast" way)
|
||||
A_gpu_single = gpuArray(A_cpu_single);
|
||||
wait(d); % Ensure transfer is done
|
||||
|
||||
fprintf('Running SINGLE precision test... ');
|
||||
tic;
|
||||
B_gpu = A_gpu_single * A_gpu_single;
|
||||
wait(d); % FORCE MATLAB TO WAIT FOR GPU
|
||||
time_single = toc;
|
||||
fprintf('Done.\n');
|
||||
fprintf('Single Precision Time: %.4f seconds\n', time_single);
|
||||
|
||||
% Calculate Speedup
|
||||
fprintf('------------------------------------------------\n');
|
||||
fprintf('Speedup Factor using single precision: %.2fx\n', time_double / time_single);
|
||||
|
||||
%
|
||||
% Create 10,000 small matrices (10x10) stacked in a 3D array
|
||||
A_stack = gpuArray.rand(10, 10, 10000, 'single');
|
||||
B_stack = gpuArray.rand(10, 10, 10000, 'single');
|
||||
|
||||
% BAD: Looping (GPU overhead kills you)
|
||||
tic;
|
||||
for i=1:10000
|
||||
C(:,:,i) = A_stack(:,:,i) * B_stack(:,:,i);
|
||||
end
|
||||
wait(d);
|
||||
loop_time = toc;
|
||||
|
||||
% GOOD: Pagefun (Executes all 10,000 mults simultaneously)
|
||||
tic;
|
||||
C_stack = pagefun(@mtimes, A_stack, B_stack);
|
||||
wait(d);
|
||||
pagefun_time = toc;
|
||||
|
||||
fprintf('------------------------------------------------\n');
|
||||
fprintf('Speedup Factor using pagefun: %.2fx\n', loop_time / pagefun_time);
|
||||
|
||||
|
||||
fprintf('Total VRAM: %.2f GB\n', d.TotalMemory / 1e9);
|
||||
fprintf('Available VRAM: %.2f GB\n', d.AvailableMemory / 1e9);
|
||||
fprintf('Usage: %.1f%%\n', 100 * (1 - d.AvailableMemory / d.TotalMemory));
|
||||
Reference in New Issue
Block a user