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-rw-r--r--best_counters.py315
-rw-r--r--power_predictor.py26
-rw-r--r--src/benchmark.rs252
3 files changed, 389 insertions, 204 deletions
diff --git a/best_counters.py b/best_counters.py
new file mode 100644
index 0000000..feb5900
--- /dev/null
+++ b/best_counters.py
@@ -0,0 +1,315 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split, cross_val_score
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.feature_selection import RFE, mutual_info_regression
+from sklearn.preprocessing import StandardScaler
+from sklearn.impute import KNNImputer
+from sklearn.metrics import r2_score, mean_squared_error
+from itertools import combinations
+
+def load_and_preprocess_data(csv_path):
+ # Load the CSV file
+ df = pd.read_csv(csv_path)
+
+ # Extract target (power usage)
+ y = df['package_power_j'].values
+
+ # Extract features (CPU frequency and performance counters)
+ X = df.iloc[:, 3:] # Skip timestamp, duration_ms, and power
+
+ # Replace empty strings with NaN
+ X = X.replace('', np.nan)
+
+ # Convert all values to float
+ X = X.astype(float)
+
+ # Impute missing values using KNN
+ imputer = KNNImputer(n_neighbors=5)
+ X_imputed = imputer.fit_transform(X)
+ X = pd.DataFrame(X_imputed, columns=X.columns)
+
+ return X, y
+
+def calculate_mutual_information(X, y):
+ # Calculate mutual information for each feature
+ mi_scores = mutual_info_regression(X, y)
+
+ # Create a DataFrame of features and their importance scores
+ feature_importance = pd.DataFrame({
+ 'Feature': X.columns,
+ 'Importance': mi_scores
+ })
+
+ # Sort by importance
+ feature_importance = feature_importance.sort_values('Importance', ascending=False)
+
+ # Print top features
+ print("\nFeature importance by mutual information:")
+ for i, (_, row) in enumerate(feature_importance.head(10).iterrows(), 1):
+ print(f"{i}. {row['Feature']} - importance: {row['Importance']:.4f}")
+
+ # Visualize feature importance
+ plt.figure(figsize=(10, 6))
+ plt.barh(feature_importance['Feature'], feature_importance['Importance'])
+ plt.xlabel('Mutual Information Score')
+ plt.title('Feature Importance')
+ plt.tight_layout()
+ plt.savefig('feature_importance.png')
+
+ return feature_importance
+
+def find_best_features_rfe(X, y, n_features=5):
+ print("\nRunning Recursive Feature Elimination...")
+ model = RandomForestRegressor(n_estimators=100, random_state=42)
+ selector = RFE(estimator=model, n_features_to_select=n_features, step=1)
+ selector.fit(X, y)
+
+ # Get the selected features
+ selected_features = X.columns[selector.support_].tolist()
+ print(f"Selected features (RFE): {selected_features}")
+ return selected_features
+
+def select_uncorrelated_features(X, feature_importances, top_n=5, correlation_threshold=0.75):
+ print(f"\nSelecting {top_n} uncorrelated features...")
+
+ # Convert feature importances to dictionary for easy lookup
+ importance_dict = {row['Feature']: row['Importance']
+ for _, row in feature_importances.iterrows()}
+
+ # Calculate correlation matrix
+ corr_matrix = X.corr().abs()
+
+ # Start with the most important feature
+ top_feature = feature_importances.iloc[0]['Feature']
+ selected = [top_feature]
+ print(f"Starting with top feature: {top_feature}")
+
+ candidates = X.columns.tolist()
+ candidates.remove(top_feature)
+
+ # Greedy selection of uncorrelated features
+ while len(selected) < top_n and candidates:
+ best_feature = None
+ max_importance = -np.inf
+
+ for feature in candidates:
+ # Check correlation with already selected features
+ correlations = [corr_matrix.loc[feature, sel] for sel in selected]
+ if max(correlations) < correlation_threshold:
+ # Use mutual information score as importance
+ importance = importance_dict[feature]
+ if importance > max_importance:
+ max_importance = importance
+ best_feature = feature
+
+ if best_feature:
+ selected.append(best_feature)
+ candidates.remove(best_feature)
+ print(f"Added {best_feature} (correlation with selected: {max([corr_matrix.loc[best_feature, sel] for sel in selected[:-1]])})")
+ else:
+ # If no feature satisfies correlation threshold, relax the threshold
+ old_threshold = correlation_threshold
+ correlation_threshold += 0.05
+ print(f"No features satisfy threshold {old_threshold}, relaxing to {correlation_threshold}")
+
+ print(f"Selected uncorrelated features: {selected}")
+ return selected
+
+def evaluate_feature_combination(X, y, feature_combo):
+ X_subset = X[list(feature_combo)]
+ model = RandomForestRegressor(n_estimators=100, random_state=42)
+ scores = cross_val_score(model, X_subset, y, cv=5, scoring='r2')
+ return scores.mean(), scores.std()
+
+def find_best_feature_combination(X, y, n_features=5, top_k=10):
+ print(f"\nFinding the best combination of {n_features} features...")
+
+ # Always include frequency if available
+ freq_col = 'cpu_frequency_mhz' if 'cpu_frequency_mhz' in X.columns else None
+
+ # For efficiency, limit the search to the top_k most important features
+ feature_importances = calculate_mutual_information(X, y)
+ top_features = feature_importances.head(top_k)['Feature'].tolist()
+
+ if freq_col and freq_col not in top_features:
+ top_features = [feat for feat in top_features if feat != freq_col]
+ top_features = top_features[:n_features-1] # Make room for frequency
+ else:
+ top_features = top_features[:n_features]
+
+ best_score = -np.inf
+ best_combo = None
+ best_std = 0
+
+ # Try all combinations
+ total_combos = sum(1 for _ in combinations(top_features, n_features if not freq_col else n_features-1))
+ print(f"Testing {total_combos} combinations...")
+
+ for i, combo in enumerate(combinations(top_features, n_features if not freq_col else n_features-1)):
+ if i % 10 == 0:
+ print(f"Evaluating combination {i+1}/{total_combos}")
+
+ features = list(combo)
+ if freq_col and freq_col not in features:
+ features.append(freq_col)
+
+ score, std = evaluate_feature_combination(X, y, features)
+
+ if score > best_score:
+ best_score = score
+ best_std = std
+ best_combo = features
+
+ print(f"Best feature combination (R²={best_score:.4f}±{best_std:.4f}): {best_combo}")
+ return best_combo, best_score, best_std
+
+def compare_feature_subsets(X, y, n_features=5):
+ print("\nComparing different feature selection methods...")
+
+ # Always include frequency if available
+ freq_col = 'cpu_frequency_mhz' if 'cpu_frequency_mhz' in X.columns else None
+
+ # Get feature importances
+ feature_importances = calculate_mutual_information(X, y)
+
+ # Method 1: Top features by mutual information
+ if freq_col:
+ mi_top = feature_importances['Feature'].tolist()
+ if freq_col in mi_top:
+ mi_top.remove(freq_col)
+ mi_top = mi_top[:n_features-1] + [freq_col]
+ else:
+ mi_top = feature_importances['Feature'].tolist()[:n_features]
+
+ # Method 2: Recursive Feature Elimination
+ rfe_selected = find_best_features_rfe(X, y, n_features if not freq_col else n_features-1)
+ if freq_col and freq_col not in rfe_selected:
+ rfe_selected.append(freq_col)
+
+ # Method 3: Uncorrelated features
+ uncorrelated = select_uncorrelated_features(X, feature_importances,
+ n_features if not freq_col else n_features-1)
+ if freq_col and freq_col not in uncorrelated:
+ uncorrelated.append(freq_col)
+
+ # Method 4: Best combination (limited search)
+ best_combo, _, _ = find_best_feature_combination(X, y, n_features, top_k=10)
+
+ # Define subsets to test
+ subsets = {
+ 'mutual_info_top': mi_top,
+ 'rfe_selected': rfe_selected,
+ 'uncorrelated': uncorrelated,
+ 'best_combination': best_combo
+ }
+
+ # Create a visual representation of the correlation matrix
+ plt.figure(figsize=(12, 10))
+ corr_matrix = X.corr()
+ plt.imshow(corr_matrix, cmap='coolwarm', interpolation='none', vmin=-1, vmax=1)
+ plt.colorbar()
+ plt.xticks(range(len(X.columns)), X.columns, rotation=90)
+ plt.yticks(range(len(X.columns)), X.columns)
+ plt.title('Feature Correlation Matrix')
+ plt.tight_layout()
+ plt.savefig('correlation_matrix.png')
+
+ # Evaluate each subset with a RandomForestRegressor
+ results = {}
+ for name, features in subsets.items():
+ X_subset = X[features]
+ scores = cross_val_score(RandomForestRegressor(n_estimators=100, random_state=42),
+ X_subset, y, cv=5, scoring='r2')
+
+ results[name] = {
+ 'r2_mean': scores.mean(),
+ 'r2_std': scores.std(),
+ 'features': features
+ }
+
+ # Print results
+ print("\nResults for different feature subsets:")
+ for name, res in results.items():
+ print(f"{name}: R²={res['r2_mean']:.4f}±{res['r2_std']:.4f}, Features: {res['features']}")
+
+ # Create bar chart of results
+ plt.figure(figsize=(10, 6))
+ methods = list(results.keys())
+ scores = [results[m]['r2_mean'] for m in methods]
+ errors = [results[m]['r2_std'] for m in methods]
+
+ plt.bar(methods, scores, yerr=errors, capsize=5)
+ plt.ylabel('R² Score')
+ plt.title('Performance of Different Feature Selection Methods')
+ plt.ylim(0.90, 1.0) # Adjust as needed
+ plt.tight_layout()
+ plt.savefig('feature_selection_comparison.png')
+
+ # Find the best method
+ best_method = max(results.items(), key=lambda x: x[1]['r2_mean'])
+ print(f"\nBest method: {best_method[0]} with R²={best_method[1]['r2_mean']:.4f}")
+ print(f"Final recommended feature set: {best_method[1]['features']}")
+
+ return results, best_method[1]['features']
+
+def final_model_evaluation(X, y, selected_features):
+ print("\nEvaluating final model with selected features...")
+
+ # Prepare data
+ X_selected = X[selected_features]
+ X_train, X_test, y_train, y_test = train_test_split(
+ X_selected, y, test_size=0.2, random_state=42)
+
+ # Train model
+ model = RandomForestRegressor(n_estimators=100, random_state=42)
+ model.fit(X_train, y_train)
+
+ # Evaluate
+ y_pred = model.predict(X_test)
+ r2 = r2_score(y_test, y_pred)
+ rmse = np.sqrt(mean_squared_error(y_test, y_pred))
+
+ print(f"Final model performance: R²={r2:.4f}, RMSE={rmse:.6f}")
+
+ # Plot actual vs predicted
+ plt.figure(figsize=(8, 8))
+ plt.scatter(y_test, y_pred, alpha=0.5)
+ plt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)], 'r--')
+ plt.xlabel('Actual Power Usage (J)')
+ plt.ylabel('Predicted Power Usage (J)')
+ plt.title(f'Actual vs Predicted Power Usage with Selected Features\nR²={r2:.4f}, RMSE={rmse:.6f}')
+ plt.tight_layout()
+ plt.savefig('final_model_performance.png')
+
+ # Feature importances in the final model
+ importances = model.feature_importances_
+ indices = np.argsort(importances)[::-1]
+
+ plt.figure(figsize=(10, 6))
+ plt.title('Feature Importances in Final Model')
+ plt.bar(range(X_selected.shape[1]), importances[indices])
+ plt.xticks(range(X_selected.shape[1]), np.array(selected_features)[indices], rotation=90)
+ plt.tight_layout()
+ plt.savefig('final_model_feature_importances.png')
+
+ return model, r2, rmse
+
+def main():
+ # Load and preprocess data
+ print("Loading and preprocessing data...")
+ X, y = load_and_preprocess_data('logs.csv')
+
+ # Compare different feature selection methods
+ results, best_features = compare_feature_subsets(X, y, n_features=5)
+
+ # Evaluate final model with selected features
+ model, r2, rmse = final_model_evaluation(X, y, best_features)
+
+ print("\nAnalysis complete! The optimal set of 5 performance counters has been identified.")
+ print(f"Final selected features: {best_features}")
+ print(f"Model performance: R²={r2:.4f}, RMSE={rmse:.6f}")
+
+if __name__ == "__main__":
+ main()
diff --git a/power_predictor.py b/power_predictor.py
index 67c73ae..49e49fe 100644
--- a/power_predictor.py
+++ b/power_predictor.py
@@ -72,7 +72,7 @@ def analyze_feature_importance(X, y):
plt.savefig('feature_importance.png')
print("\nTop 5 most important features:")
- for i, (_, row) in enumerate(feature_importance.head(5).iterrows(), 1):
+ for i, (_, row) in enumerate(feature_importance.head(15).iterrows(), 1):
print(f"{i}. {row['Feature']} - importance: {row['Importance']:.4f}")
return feature_importance
@@ -205,6 +205,26 @@ def evaluate_model(model, X, y, scaler):
return mse, rmse, mae, r2
+def remove_outliers(X, y, threshold=3):
+ # Scale features for distance calculations
+ scaler = StandardScaler()
+ X_scaled = scaler.fit_transform(X)
+
+ # Calculate z-scores for target values
+ z_scores = np.abs((y - np.mean(y)) / np.std(y))
+
+ # Identify inliers
+ inliers = z_scores < threshold
+
+ # Remove outliers
+ X_clean = X.iloc[inliers]
+ y_clean = y[inliers]
+
+ removed = len(y) - len(y_clean)
+ print(f"Removed {removed} outliers ({removed/len(y)*100:.2f}% of data)")
+
+ return X_clean, y_clean
+
# Step 7: Main function
def main():
csv_path = 'logs.csv'
@@ -212,6 +232,8 @@ def main():
# Load and prepare data
print("Loading data...")
X, y = load_data(csv_path)
+
+ X, y = remove_outliers(X, y)
# Impute missing values
print("\nImputing missing values...")
@@ -223,7 +245,7 @@ def main():
# Train the model
print("\nTraining model...")
- model, scaler = train_model(X_imputed, y, batch_size=8, epochs=200)
+ model, scaler = train_model(X_imputed, y, batch_size=8, epochs=100)
# Evaluate the model
print("\nEvaluating model...")
diff --git a/src/benchmark.rs b/src/benchmark.rs
index 606f29b..6368fa8 100644
--- a/src/benchmark.rs
+++ b/src/benchmark.rs
@@ -17,8 +17,8 @@ use std::thread;
use std::time::{Duration, Instant};
const SLICE_US: u64 = 50000;
-const LOG_INTERVAL_MS: u64 = 10; // Log every 1 second
- // const RESHUFFLE_ROUNDS: usize = 5; // Number of rounds before reshuffling counters
+const LOG_INTERVAL_MS: u64 = 5; // Log every 1 second
+ // const RESHUFFLE_ROUNDS: usize = 5; // Number of rounds before reshuffling counters
const RESHUFFLE_ROUNDS: usize = 1; // Number of rounds before reshuffling counters
const MAX_COUNTERS_AT_ONCE: usize = 5;
@@ -379,284 +379,132 @@ fn define_available_events() -> Vec<(String, Event)> {
"cpu_cycles".to_string(),
Event::Hardware(Hardware::CPU_CYCLES),
),
- (
- "instructions".to_string(),
- Event::Hardware(Hardware::INSTRUCTIONS),
- ),
+ // (
+ // "instructions".to_string(),
+ // Event::Hardware(Hardware::INSTRUCTIONS),
+ // ),
(
"cache_references".to_string(),
Event::Hardware(Hardware::CACHE_REFERENCES),
),
- (
- "cache_misses".to_string(),
- Event::Hardware(Hardware::CACHE_MISSES),
- ),
- (
- "branch_instructions".to_string(),
- Event::Hardware(Hardware::BRANCH_INSTRUCTIONS),
- ),
- (
- "branch_misses".to_string(),
- Event::Hardware(Hardware::BRANCH_MISSES),
- ),
// (
- // "bus_cycles".to_string(),
- // Event::Hardware(Hardware::BUS_CYCLES),
+ // "cache_misses".to_string(),
+ // Event::Hardware(Hardware::CACHE_MISSES),
// ),
// (
- // "stalled_cycles_frontend".to_string(),
- // Event::Hardware(Hardware::STALLED_CYCLES_FRONTEND),
- // ),
- // (
- // "stalled_cycles_backend".to_string(),
- // Event::Hardware(Hardware::STALLED_CYCLES_BACKEND),
+ // "branch_instructions".to_string(),
+ // Event::Hardware(Hardware::BRANCH_INSTRUCTIONS),
// ),
(
- "ref_cpu_cycles".to_string(),
- Event::Hardware(Hardware::REF_CPU_CYCLES),
- ),
- ]);
-
- // Software events
- events.extend([
- (
- "sw_cpu_clock".to_string(),
- Event::Software(Software::CPU_CLOCK),
- ),
- (
- "sw_task_clock".to_string(),
- Event::Software(Software::TASK_CLOCK),
- ),
- (
- "sw_page_faults".to_string(),
- Event::Software(Software::PAGE_FAULTS),
- ),
- (
- "sw_context_switches".to_string(),
- Event::Software(Software::CONTEXT_SWITCHES),
- ),
- (
- "sw_cpu_migrations".to_string(),
- Event::Software(Software::CPU_MIGRATIONS),
- ),
- (
- "sw_page_faults_min".to_string(),
- Event::Software(Software::PAGE_FAULTS_MIN),
- ),
- (
- "sw_page_faults_maj".to_string(),
- Event::Software(Software::PAGE_FAULTS_MAJ),
- ),
- (
- "sw_alignment_faults".to_string(),
- Event::Software(Software::ALIGNMENT_FAULTS),
+ "branch_misses".to_string(),
+ Event::Hardware(Hardware::BRANCH_MISSES),
),
(
- "sw_emulation_faults".to_string(),
- Event::Software(Software::EMULATION_FAULTS),
+ "ref_cpu_cycles".to_string(),
+ Event::Hardware(Hardware::REF_CPU_CYCLES),
),
]);
// L1 Data Cache events
events.extend([
- (
- "l1d_read_access".to_string(),
- Event::Cache(Cache {
- which: WhichCache::L1D,
- operation: CacheOp::READ,
- result: CacheResult::ACCESS,
- }),
- ),
- (
- "l1d_read_miss".to_string(),
- Event::Cache(Cache {
- which: WhichCache::L1D,
- operation: CacheOp::READ,
- result: CacheResult::MISS,
- }),
- ),
- // (
- // "l1d_write_access".to_string(),
- // Event::Cache(Cache {
- // which: WhichCache::L1D,
- // operation: CacheOp::WRITE,
- // result: CacheResult::ACCESS,
- // }),
- // ),
- // (
- // "l1d_write_miss".to_string(),
- // Event::Cache(Cache {
- // which: WhichCache::L1D,
- // operation: CacheOp::WRITE,
- // result: CacheResult::MISS,
- // }),
- // ),
// (
- // "l1d_prefetch_access".to_string(),
+ // "l1d_read_access".to_string(),
// Event::Cache(Cache {
// which: WhichCache::L1D,
- // operation: CacheOp::PREFETCH,
+ // operation: CacheOp::READ,
// result: CacheResult::ACCESS,
// }),
// ),
- // (
- // "l1d_prefetch_miss".to_string(),
- // Event::Cache(Cache {
- // which: WhichCache::L1D,
- // operation: CacheOp::PREFETCH,
- // result: CacheResult::MISS,
- // }),
- // ),
- ]);
-
- // L1 Instruction Cache events
- events.extend([
(
- "l1i_read_access".to_string(),
- Event::Cache(Cache {
- which: WhichCache::L1I,
- operation: CacheOp::READ,
- result: CacheResult::ACCESS,
- }),
- ),
- (
- "l1i_read_miss".to_string(),
+ "l1d_read_miss".to_string(),
Event::Cache(Cache {
- which: WhichCache::L1I,
+ which: WhichCache::L1D,
operation: CacheOp::READ,
result: CacheResult::MISS,
}),
),
]);
- // Last Level Cache events
+ // L1 Instruction Cache events
events.extend([
// (
- // "llc_read_access".to_string(),
+ // "l1i_read_access".to_string(),
// Event::Cache(Cache {
- // which: WhichCache::LL,
+ // which: WhichCache::L1I,
// operation: CacheOp::READ,
// result: CacheResult::ACCESS,
// }),
// ),
// (
- // "llc_read_miss".to_string(),
+ // "l1i_read_miss".to_string(),
// Event::Cache(Cache {
- // which: WhichCache::LL,
+ // which: WhichCache::L1I,
// operation: CacheOp::READ,
// result: CacheResult::MISS,
// }),
// ),
+ ]);
+
+ // Data TLB events
+ events.extend([
// (
- // "llc_write_access".to_string(),
+ // "dtlb_read_access".to_string(),
// Event::Cache(Cache {
- // which: WhichCache::LL,
- // operation: CacheOp::WRITE,
+ // which: WhichCache::DTLB,
+ // operation: CacheOp::READ,
// result: CacheResult::ACCESS,
// }),
// ),
// (
- // "llc_write_miss".to_string(),
+ // "dtlb_read_miss".to_string(),
// Event::Cache(Cache {
- // which: WhichCache::LL,
- // operation: CacheOp::WRITE,
+ // which: WhichCache::DTLB,
+ // operation: CacheOp::READ,
// result: CacheResult::MISS,
// }),
// ),
+ ]);
+
+ // Instruction TLB events
+ events.extend([
// (
- // "llc_prefetch_access".to_string(),
+ // "itlb_read_access".to_string(),
// Event::Cache(Cache {
- // which: WhichCache::LL,
- // operation: CacheOp::PREFETCH,
+ // which: WhichCache::ITLB,
+ // operation: CacheOp::READ,
// result: CacheResult::ACCESS,
// }),
// ),
// (
- // "llc_prefetch_miss".to_string(),
+ // "itlb_read_miss".to_string(),
// Event::Cache(Cache {
- // which: WhichCache::LL,
- // operation: CacheOp::PREFETCH,
+ // which: WhichCache::ITLB,
+ // operation: CacheOp::READ,
// result: CacheResult::MISS,
// }),
// ),
]);
- // Data TLB events
+ // Branch Prediction Unit events
events.extend([
- (
- "dtlb_read_access".to_string(),
- Event::Cache(Cache {
- which: WhichCache::DTLB,
- operation: CacheOp::READ,
- result: CacheResult::ACCESS,
- }),
- ),
- (
- "dtlb_read_miss".to_string(),
- Event::Cache(Cache {
- which: WhichCache::DTLB,
- operation: CacheOp::READ,
- result: CacheResult::MISS,
- }),
- ),
// (
- // "dtlb_write_access".to_string(),
+ // "bpu_read_access".to_string(),
// Event::Cache(Cache {
- // which: WhichCache::DTLB,
- // operation: CacheOp::WRITE,
+ // which: WhichCache::BPU,
+ // operation: CacheOp::READ,
// result: CacheResult::ACCESS,
// }),
// ),
// (
- // "dtlb_write_miss".to_string(),
+ // "bpu_read_miss".to_string(),
// Event::Cache(Cache {
- // which: WhichCache::DTLB,
- // operation: CacheOp::WRITE,
+ // which: WhichCache::BPU,
+ // operation: CacheOp::READ,
// result: CacheResult::MISS,
// }),
// ),
]);
- // Instruction TLB events
- events.extend([
- (
- "itlb_read_access".to_string(),
- Event::Cache(Cache {
- which: WhichCache::ITLB,
- operation: CacheOp::READ,
- result: CacheResult::ACCESS,
- }),
- ),
- (
- "itlb_read_miss".to_string(),
- Event::Cache(Cache {
- which: WhichCache::ITLB,
- operation: CacheOp::READ,
- result: CacheResult::MISS,
- }),
- ),
- ]);
-
- // Branch Prediction Unit events
- events.extend([
- (
- "bpu_read_access".to_string(),
- Event::Cache(Cache {
- which: WhichCache::BPU,
- operation: CacheOp::READ,
- result: CacheResult::ACCESS,
- }),
- ),
- (
- "bpu_read_miss".to_string(),
- Event::Cache(Cache {
- which: WhichCache::BPU,
- operation: CacheOp::READ,
- result: CacheResult::MISS,
- }),
- ),
- ]);
-
// Sort events by name for consistent ordering
events.sort_unstable_by_key(|(name, _)| name.clone());
generated by cgit v1.2.3-70-g09d2 (git 2.48.1) at 2025-09-22 21:27:58 +0000