#version 430
// Ultra-performance monitoring compute shader
// Real-time performance analytics and adaptive optimization recommendations
layout(local_size_x = 128, local_size_y = 1, local_size_z = 1) in;
// Performance metrics input
layout(std430, binding = 0) buffer TimingBuffer {
float execution_times[]; // Microseconds per operation
};
layout(std430, binding = 1) buffer ThroughputBuffer {
float throughput_data[]; // Operations per second
};
layout(std430, binding = 2) buffer MemoryMetricsBuffer {
float memory_metrics[]; // [allocated, used, bandwidth_util, cache_hit_rate]
};
layout(std430, binding = 3) buffer ComputeMetricsBuffer {
float compute_metrics[]; // [gpu_util, shader_occupancy, warp_efficiency]
};
// Historical performance data
layout(std430, binding = 4) buffer PerformanceHistoryBuffer {
float perf_history[]; // Last 32 measurements per metric
};
// Anomaly detection thresholds
layout(std430, binding = 5) buffer ThresholdsBuffer {
float thresholds[]; // [min_throughput, max_memory, max_latency, min_efficiency]
};
// Output: optimization recommendations
layout(std430, binding = 6) buffer RecommendationsBuffer {
int recommendations[]; // Bitfield of optimization recommendations
};
// Output: performance predictions
layout(std430, binding = 7) buffer PredictionsBuffer {
float predictions[]; // Predicted performance metrics
};
// Output: anomaly flags
layout(std430, binding = 8) buffer AnomalyBuffer {
int anomaly_flags[]; // Detected anomalies
};
uniform int num_systems;
uniform float time_window; // Time window for analysis (seconds)
uniform float anomaly_sensitivity; // Sensitivity for anomaly detection
uniform bool enable_ml_prediction; // Enable ML-based prediction
uniform bool enable_auto_tuning; // Enable automatic optimization
// Shared memory for statistical computations
shared float shared_metrics[128 * 8];
shared float shared_stats[128];
// Optimization recommendation flags
const int RECOMMENDATION_REDUCE_PRECISION = 1;
const int RECOMMENDATION_INCREASE_BATCH_SIZE = 2;
const int RECOMMENDATION_ENABLE_CACHING = 4;
const int RECOMMENDATION_ADJUST_BLOCK_SIZE = 8;
const int RECOMMENDATION_SWITCH_ALGORITHM = 16;
const int RECOMMENDATION_REDUCE_MEMORY_USAGE = 32;
const int RECOMMENDATION_INCREASE_PARALLELISM = 64;
const int RECOMMENDATION_OPTIMIZE_MEMORY_LAYOUT = 128;
// Anomaly detection flags
const int ANOMALY_PERFORMANCE_DEGRADATION = 1;
const int ANOMALY_MEMORY_SPIKE = 2;
const int ANOMALY_COMPUTE_UNDERUTILIZATION = 4;
const int ANOMALY_CACHE_THRASHING = 8;
const int ANOMALY_BANDWIDTH_SATURATION = 16;
// Simple moving average calculation
float moving_average(float data[], int start_idx, int window_size) {
float sum = 0.0;
int count = 0;
for (int i = 0; i < window_size; i++) {
int idx = start_idx + i;
if (data[idx] > 0.0) {
sum += data[idx];
count++;
}
}
return count > 0 ? sum / float(count) : 0.0;
}
// Standard deviation calculation
float standard_deviation(float data[], int start_idx, int window_size, float mean) {
float sum_sq_diff = 0.0;
int count = 0;
for (int i = 0; i < window_size; i++) {
int idx = start_idx + i;
if (data[idx] > 0.0) {
float diff = data[idx] - mean;
sum_sq_diff += diff * diff;
count++;
}
}
return count > 1 ? sqrt(sum_sq_diff / float(count - 1)) : 0.0;
}
// ML-based performance prediction (simplified neural network)
float predict_performance(uint system_id, float features[8]) {
// Simple 3-layer neural network prediction
float hidden1[4];
float hidden2[2];
// First hidden layer (8 -> 4)
float weights1[32] = float[32](
0.5, -0.3, 0.8, 0.2, -0.6, 0.4, 0.7, -0.1, // hidden1[0]
-0.2, 0.6, -0.4, 0.9, 0.1, -0.7, 0.3, 0.5, // hidden1[1]
0.7, 0.1, -0.5, 0.8, -0.2, 0.6, -0.3, 0.4, // hidden1[2]
-0.4, 0.8, 0.2, -0.6, 0.5, 0.1, -0.7, 0.3 // hidden1[3]
);
for (int i = 0; i < 4; i++) {
float sum = 0.0;
for (int j = 0; j < 8; j++) {
sum += features[j] * weights1[i * 8 + j];
}
hidden1[i] = max(0.0, sum); // ReLU activation
}
// Second hidden layer (4 -> 2)
float weights2[8] = float[8](
0.6, -0.4, 0.8, 0.2, // hidden2[0]
-0.3, 0.7, -0.1, 0.5 // hidden2[1]
);
for (int i = 0; i < 2; i++) {
float sum = 0.0;
for (int j = 0; j < 4; j++) {
sum += hidden1[j] * weights2[i * 4 + j];
}
hidden2[i] = max(0.0, sum); // ReLU activation
}
// Output layer (2 -> 1)
float output_weights[2] = float[2](0.8, -0.3);
float output = 0.0;
for (int i = 0; i < 2; i++) {
output += hidden2[i] * output_weights[i];
}
return 1.0 / (1.0 + exp(-output)); // Sigmoid activation
}
void main() {
uint index = gl_GlobalInvocationID.x;
uint tid = gl_LocalInvocationID.x;
if (index >= num_systems) return;
// Load current performance metrics
float current_exec_time = execution_times[index];
float current_throughput = throughput_data[index];
// Memory metrics: [allocated, used, bandwidth_util, cache_hit_rate]
float mem_allocated = memory_metrics[index * 4 + 0];
float mem_used = memory_metrics[index * 4 + 1];
float bandwidth_util = memory_metrics[index * 4 + 2];
float cache_hit_rate = memory_metrics[index * 4 + 3];
// Compute metrics: [gpu_util, shader_occupancy, warp_efficiency]
float gpu_util = compute_metrics[index * 3 + 0];
float shader_occupancy = compute_metrics[index * 3 + 1];
float warp_efficiency = compute_metrics[index * 3 + 2];
// Calculate historical statistics
int hist_start = index * 32;
float avg_throughput = moving_average(perf_history, hist_start, 16);
float std_throughput = standard_deviation(perf_history, hist_start, 16, avg_throughput);
// Anomaly detection
int anomalies = 0;
// Performance degradation
if (current_throughput < avg_throughput - 2.0 * std_throughput * anomaly_sensitivity) {
anomalies |= ANOMALY_PERFORMANCE_DEGRADATION;
}
// Memory spike
float memory_usage_ratio = mem_used / max(mem_allocated, 1.0);
if (memory_usage_ratio > thresholds[1]) {
anomalies |= ANOMALY_MEMORY_SPIKE;
}
// Compute underutilization
if (gpu_util < thresholds[3]) {
anomalies |= ANOMALY_COMPUTE_UNDERUTILIZATION;
}
// Cache thrashing
if (cache_hit_rate < 0.5) {
anomalies |= ANOMALY_CACHE_THRASHING;
}
// Bandwidth saturation
if (bandwidth_util > 0.95) {
anomalies |= ANOMALY_BANDWIDTH_SATURATION;
}
anomaly_flags[index] = anomalies;
// Generate optimization recommendations
int recommendations = 0;
if (enable_auto_tuning) {
// Reduce precision if performance is poor and accuracy allows
if ((anomalies & ANOMALY_PERFORMANCE_DEGRADATION) && current_exec_time > thresholds[2]) {
recommendations |= RECOMMENDATION_REDUCE_PRECISION;
}
// Increase batch size if underutilizing compute
if (anomalies & ANOMALY_COMPUTE_UNDERUTILIZATION) {
recommendations |= RECOMMENDATION_INCREASE_BATCH_SIZE;
}
// Enable caching if cache hit rate is low
if (anomalies & ANOMALY_CACHE_THRASHING) {
recommendations |= RECOMMENDATION_ENABLE_CACHING;
recommendations |= RECOMMENDATION_OPTIMIZE_MEMORY_LAYOUT;
}
// Reduce memory usage if memory spike detected
if (anomalies & ANOMALY_MEMORY_SPIKE) {
recommendations |= RECOMMENDATION_REDUCE_MEMORY_USAGE;
}
// Adjust block size if bandwidth is saturated
if (anomalies & ANOMALY_BANDWIDTH_SATURATION) {
recommendations |= RECOMMENDATION_ADJUST_BLOCK_SIZE;
}
// Switch algorithm if performance consistently poor
if (current_throughput < avg_throughput * 0.5) {
recommendations |= RECOMMENDATION_SWITCH_ALGORITHM;
}
}
recommendations_buffer[index] = recommendations;
// ML-based performance prediction
if (enable_ml_prediction) {
float features[8] = float[8](
current_throughput / 1000.0, // Normalized throughput
current_exec_time / 1000.0, // Normalized execution time
memory_usage_ratio, // Memory usage ratio
bandwidth_util, // Bandwidth utilization
cache_hit_rate, // Cache hit rate
gpu_util, // GPU utilization
shader_occupancy, // Shader occupancy
warp_efficiency // Warp efficiency
);
float predicted_perf = predict_performance(index, features);
predictions[index] = predicted_perf;
} else {
// Simple linear prediction based on trend
float trend = (current_throughput - avg_throughput) / max(avg_throughput, 1.0);
predictions[index] = current_throughput * (1.0 + trend * 0.1);
}
// Update performance history (circular buffer)
int hist_idx = hist_start + (int(gl_GlobalInvocationID.x) % 32);
perf_history[hist_idx] = current_throughput;
}