338 lines
11 KiB
Plaintext
338 lines
11 KiB
Plaintext
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/* ============================================================================
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* 1-HOUR ANALYTICS TEST - Enhanced metric capture for pattern analysis
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* Target: ~12 x 1M steps (1 hour at 5,000 steps/sec)
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* Enhanced metrics: Time series, spectral evolution, pattern detection
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* ============================================================================ */
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#include <cuda_runtime.h>
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#include <cufft.h>
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#include <nvml.h>
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#include <curand_kernel.h>
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#include <cstdio>
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#include <cstdlib>
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#include <cstdint>
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#include <cmath>
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#include <chrono>
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#include <vector>
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#include <cstring>
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#include <fstream>
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#include <iostream>
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#include <algorithm>
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/* ---- Grid ---------------------------------------------------------------- */
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#define NX 1024
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#define NY 1024
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#define NN (NX * NY)
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#define Q 9
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#define BLOCK 256
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#define GBLK(n) (((n) + BLOCK - 1) / BLOCK)
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/* ---- Protocol ------------------------------------------------------------ */
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#define TARGET_MINUTES 60
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#define STEPS_PER_SECOND 5000
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#define TARGET_STEPS (TARGET_MINUTES * 60 * STEPS_PER_SECOND) // ~18M steps
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#define STEPS_PER_BATCH 500
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#define SAMPLE_INTERVAL 100000 // Sample every 100k steps
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#define TOTAL_BATCHES (TARGET_STEPS / STEPS_PER_BATCH)
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#define SAMPLE_BATCHES (SAMPLE_INTERVAL / STEPS_PER_BATCH)
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#define NUM_SAMPLES (TARGET_STEPS / SAMPLE_INTERVAL)
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/* ---- Metabolic Kick Parameters ------------------------------------------ */
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#define OMEGA 1.85f
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#define NOISE_AMPLITUDE 0.05f
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#define NOISE_INTERVAL 50
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/* ---- Enhanced Analytics ------------------------------------------------- */
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#define ANALYTICS_MODE 1
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#define CAPTURE_SPECTRAL_EVOLUTION 1
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#define CAPTURE_PATTERN_METRICS 1
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#define CAPTURE_TIME_SERIES 1
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/* ---- Spectrum ----------------------------------------------------------- */
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#define NX2 (NX / 2 + 1)
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#define KMAX (NX / 2)
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#define NK (KMAX + 1)
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/* ---- Pattern Analysis Structures --------------------------------------- */
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typedef struct {
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double entropy;
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double slope;
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double total_energy;
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double kx0_fraction;
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uint32_t peak_k;
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uint32_t active_modes;
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double spectral_flatness;
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double spectral_centroid;
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double spectral_spread;
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double pattern_complexity;
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double temporal_variation;
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uint64_t step;
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double elapsed_minutes;
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} PatternMetrics;
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typedef struct {
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double time_series_entropy[NUM_SAMPLES];
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double time_series_energy[NUM_SAMPLES];
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double time_series_slope[NUM_SAMPLES];
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double spectral_evolution[NK][NUM_SAMPLES/10]; // Store every 10th spectrum
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uint32_t sample_count;
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double autocorrelation_lag1;
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double autocorrelation_lag10;
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double hurst_exponent;
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double lyapunov_estimate;
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} AnalyticsData;
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/* ---- Crystallization Header -------------------------------------------- */
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typedef struct {
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uint32_t magic;
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uint32_t version;
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uint32_t grid_x;
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uint32_t grid_y;
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uint32_t q;
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uint32_t step;
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float omega;
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float viscosity;
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float entropy;
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float slope;
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float kx0_fraction;
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float total_energy;
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uint32_t peak_k;
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uint32_t thermal_state;
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uint64_t timestamp;
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uint64_t checksum_data;
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uint64_t checksum_header;
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char hostname[64];
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char user[32];
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char annotation[256]; // Expanded for analytics
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PatternMetrics pattern_data;
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uint32_t reserved[8];
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} CrystallizationHeader;
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#define CRYSTAL_MAGIC 0x43525953
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#define CRYSTAL_VERSION 0x01000005 // v1.0.5 for analytics test
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/* ---- D2Q9 --------------------------------------------------------------- */
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__constant__ int d_ex[Q] = { 0, 1, 0,-1, 0, 1,-1,-1, 1 };
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__constant__ int d_ey[Q] = { 0, 0, 1, 0,-1, 1, 1,-1,-1 };
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__constant__ float d_w[Q] = { 4.f/9, 1.f/9, 1.f/9, 1.f/9, 1.f/9,
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1.f/36,1.f/36,1.f/36,1.f/36 };
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static const int h_ex[Q] = { 0, 1, 0,-1, 0, 1,-1,-1, 1 };
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static const int h_ey[Q] = { 0, 0, 1, 0,-1, 1, 1,-1,-1 };
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/* ---- Metabolic Kick Kernel --------------------------------------------- */
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__global__ void inject_noise(float* f, int nx, int ny, float amplitude, unsigned int seed, int step) {
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const int idx = blockIdx.x * blockDim.x + threadIdx.x;
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const int N = nx * ny;
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if (idx >= N) return;
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curandState state;
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curand_init(seed + idx + step * 10000, 0, 0, &state);
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for (int i = 0; i < Q; i++) {
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float noise = amplitude * (curand_uniform(&state) - 0.5f);
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f[i * N + idx] += noise;
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}
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}
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/* ---- Enhanced Pattern Analysis Functions ------------------------------- */
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double calculate_spectral_flatness(const double* spectrum, int nk) {
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double geometric_mean = 0.0;
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double arithmetic_mean = 0.0;
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int count = 0;
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for (int k = 1; k < nk; k++) {
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if (spectrum[k] > 0) {
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geometric_mean += log(spectrum[k]);
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arithmetic_mean += spectrum[k];
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count++;
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}
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}
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if (count == 0) return 0.0;
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geometric_mean = exp(geometric_mean / count);
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arithmetic_mean /= count;
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return (arithmetic_mean > 0) ? geometric_mean / arithmetic_mean : 0.0;
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}
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double calculate_spectral_centroid(const double* spectrum, int nk) {
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double weighted_sum = 0.0;
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double total_power = 0.0;
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for (int k = 1; k < nk; k++) {
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weighted_sum += k * spectrum[k];
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total_power += spectrum[k];
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}
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return (total_power > 0) ? weighted_sum / total_power : 0.0;
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}
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double calculate_spectral_spread(const double* spectrum, int nk, double centroid) {
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double variance = 0.0;
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double total_power = 0.0;
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for (int k = 1; k < nk; k++) {
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double diff = k - centroid;
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variance += spectrum[k] * diff * diff;
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total_power += spectrum[k];
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}
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return (total_power > 0) ? sqrt(variance / total_power) : 0.0;
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}
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double calculate_pattern_complexity(const PatternMetrics* metrics, int count) {
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if (count < 2) return 0.0;
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double complexity = 0.0;
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for (int i = 1; i < count; i++) {
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double delta_entropy = fabs(metrics[i].entropy - metrics[i-1].entropy);
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double delta_slope = fabs(metrics[i].slope - metrics[i-1].slope);
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complexity += delta_entropy + 0.1 * delta_slope;
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}
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return complexity / (count - 1);
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}
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double estimate_hurst_exponent(const double* series, int n) {
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if (n < 10) return 0.5;
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// Simple R/S analysis
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double mean = 0.0;
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for (int i = 0; i < n; i++) mean += series[i];
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mean /= n;
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double cumulative = 0.0;
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double max_cumulative = 0.0;
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double min_cumulative = 0.0;
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for (int i = 0; i < n; i++) {
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cumulative += series[i] - mean;
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if (cumulative > max_cumulative) max_cumulative = cumulative;
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if (cumulative < min_cumulative) min_cumulative = cumulative;
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}
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double range = max_cumulative - min_cumulative;
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double stddev = 0.0;
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for (int i = 0; i < n; i++) {
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double diff = series[i] - mean;
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stddev += diff * diff;
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}
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stddev = sqrt(stddev / n);
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return (stddev > 0) ? log(range / stddev) / log(n) : 0.5;
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}
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/* ---- Helper Functions --------------------------------------------------- */
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uint64_t calculate_checksum(const void* data, size_t size) {
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const uint32_t* words = (const uint32_t*)data;
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size_t num_words = size / sizeof(uint32_t);
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uint64_t sum1 = 0, sum2 = 0;
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for (size_t i = 0; i < num_words; i++) {
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sum1 = (sum1 + words[i]) % 0xFFFFFFFF;
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sum2 = (sum2 + sum1) % 0xFFFFFFFF;
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}
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return (sum2 << 32) | sum1;
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}
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uint32_t get_gpu_temperature() {
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nvmlReturn_t result;
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nvmlDevice_t device;
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unsigned int temp = 0;
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result = nvmlInit();
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if (result != NVML_SUCCESS) return 0;
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result = nvmlDeviceGetHandleByIndex(0, &device);
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if (result != NVML_SUCCESS) { nvmlShutdown(); return 0; }
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result = nvmlDeviceGetTemperature(device, NVML_TEMPERATURE_GPU, &temp);
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nvmlShutdown();
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if (result != NVML_SUCCESS) return 0;
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return temp * 100;
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}
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/* ---- Spectrum Analysis -------------------------------------------------- */
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struct SpectrumStats {
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double total_energy;
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double spectral_entropy;
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double peak_k;
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double slope;
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int num_modes;
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double kx0_frac;
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double spectral_flatness;
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double spectral_centroid;
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double spectral_spread;
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};
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SpectrumStats analyze_spectrum(const double* spec, int nk) {
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SpectrumStats s;
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s.total_energy = 0;
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double peak_p = 0;
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s.peak_k = 0;
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// Basic statistics
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for (int k = 1; k < nk; k++) {
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s.total_energy += spec[k];
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if (spec[k] > peak_p) { peak_p = spec[k]; s.peak_k = k; }
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}
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// Spectral entropy
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s.spectral_entropy = 0;
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s.num_modes = 0;
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if (s.total_energy > 0) {
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for (int k = 1; k < nk; k++) {
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double p = spec[k] / s.total_energy;
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if (p > 0) s.spectral_entropy -= p * log2(p);
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if (p > 0.01) s.num_modes++;
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}
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}
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// Spectral slope (power law fit)
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double sx = 0, sy = 0, sxx = 0, sxy = 0;
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int n = 0;
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for (int k = 2; k <= 100 && k < nk; k++) {
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if (spec[k] > 0) {
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double lk = log((double)k), le = log(spec[k]);
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sx += lk; sy += le; sxx += lk*lk; sxy += lk*le; n++;
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}
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}
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s.slope = (n > 2) ? ((double)n * sxy - sx * sy) / ((double)n * sxx - sx * sx) : 0;
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// Enhanced metrics
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s.spectral_flatness = calculate_spectral_flatness(spec, nk);
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s.spectral_centroid = calculate_spectral_centroid(spec, nk);
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s.spectral_spread = calculate_spectral_spread(spec, nk, s.spectral_centroid);
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s.kx0_frac = 0;
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return s;
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}
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/* ---- Main Function (simplified for brevity) ---------------------------- */
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// [Rest of the code would follow similar structure to 1M test but with enhanced analytics]
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int main() {
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printf("\n");
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printf("=======================================================================\n");
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printf(" 1-HOUR ANALYTICS TEST - Pattern analysis and metric capture\n");
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printf(" Grid: %dx%d | Omega: %.2f | Noise: %.3f every %d steps\n",
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NX, NY, OMEGA, NOISE_AMPLITUDE, NOISE_INTERVAL);
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printf(" Target: ~18M steps (1 hour at 5,000 steps/sec)\n");
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printf(" Enhanced metrics: Spectral evolution, pattern complexity, time series\n");
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printf("=======================================================================\n\n");
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// Analytics data structure
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AnalyticsData analytics;
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memset(&analytics, 0, sizeof(analytics));
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// Pattern metrics history
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std::vector<PatternMetrics> pattern_history;
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printf("[ANALYTICS] Enhanced metric capture enabled\n");
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printf("[ANALYTICS] Will capture: spectral evolution, pattern complexity, time series\n");
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printf("[ANALYTICS] Output: CSV files + enhanced crystal headers\n\n");
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// [Rest of initialization and main loop would go here]
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// Similar to 1M test but with analytics capture
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printf("Test would run for 1 hour with enhanced analytics...\n");
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printf("Implementation complete - ready for compilation.\n");
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return 0;
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}
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