finished Website Fingerprinting Lab

part1/warmup.js

@@ -1,7 +1,7 @@
 const runs = 10;
-
+const N= 393216;
 function measureOneLine() {
-  const LINE_SIZE = 16; // 128/sizeof(double) Note that js treats all numbers as double
+  const LINE_SIZE = 8; // 64/sizeof(double) Note that js treats all numbers as double
   let result = [];
 
   // Fill with -1 to ensure allocation
@@ -12,16 +12,27 @@ function measureOneLine() {
     let val = M[i * LINE_SIZE];
     const end = performance.now();
 
-    result.push(end - start);
+    result.push((end - start).toFixed(2));
   }
 
   return result;
 }
 
 function measureNLines() {
+  const LINE_SIZE = 8; // 64/sizeof(double) Note that js treats all numbers as double
   let result = [];
 
-  // TODO: Exercise 1-1
+  // Fill with -1 to ensure allocation
+  const M = new Array(runs * N * LINE_SIZE).fill(-1);
+  for (let i = 0; i < runs; i++) {
+    const start = performance.now();
+    for (let j = 0; j < N; j++) {
+      let val = M[i * N * LINE_SIZE + j * LINE_SIZE];
+    }
+    const end = performance.now();
+
+    result.push((end - start).toFixed(2));
+  }
 
   return result;
 }

part2/eval.py

@@ -7,7 +7,9 @@ from sklearn.model_selection import train_test_split
 
 def eval():
     y_pred_full, y_test_full = [], []
-
+    traces=json.load(open('../traces_nomem.out'))["traces"]
+    labels=json.load(open('../traces_nomem.out'))["labels"]
+    print("Number of traces: ", len(traces))
     # Re-train 10 times in order to reduce effects of randomness
     for i in range(10):
         ### TODO: Exercise 2-5
@@ -15,6 +17,10 @@ def eval():
         ### 2. Split data into X_train, X_test, y_train, y_test with train_test_split
         ### 3. Train classifier with X_train and y_train
         ### 4. Use classifier to make predictions on X_test. Save the result to a variable called y_pred
+        X_train, X_test, y_train, y_test = train_test_split(traces, labels, test_size=0.2)
+        clf = RandomForestClassifier(n_estimators=100)
+        clf.fit(X_train, y_train)
+        y_pred = clf.predict(X_test)
 
         # Do not modify the next two lines
         y_test_full.extend(y_test)
@@ -22,6 +28,8 @@ def eval():
 
     ### TODO: Exercise 2-5 (continued)
     ### 5. Print classification report using y_test_full and y_pred_full
+    print(classification_report(y_test_full, y_pred_full))
 
 if __name__ == "__main__":
+    
     eval()

part2/worker.js

@@ -1,6 +1,7 @@
+const N= 393216;  // Number of cache lines 24MB/64B
 // Number of sweep counts
 // TODO (Exercise 2-1): Choose an appropriate value!
-let P = 1000;
+let P = 10;
 
 // Number of elements in your trace
 let K = 5 * 1000 / P; 
@@ -12,19 +13,33 @@ let T;
 let start;
 
 function record() {
+  
   // Create empty array for saving trace values
   T = new Array(K);
 
   // Fill array with -1 so we can be sure memory is allocated
   T.fill(-1, 0, T.length);
-
+  const LINE_SIZE = 8; // 64/sizeof(double) Note that js treats all numbers as double
+  const M = new Array(N * LINE_SIZE).fill(-1);
   // Save start timestamp
   start = performance.now();
+  for (let i = 0; i < K; i++) {
+    let count=0;
+    while (performance.now() - start < P*(i+1)) {
+      // sweep the cache
+      for (let j = 0; j < N; j++) {
+        let val = M[j * LINE_SIZE];
+      }
+      count++;
+    }
+    T[i]=count;
+  }
 
   // TODO (Exercise 2-1): Record data for 5 seconds and save values to T.
 
   // Once done recording, send result to main thread
   postMessage(JSON.stringify(T));
+  console.log(T);
 }
 
 // DO NOT MODIFY BELOW THIS LINE -- PROVIDED BY COURSE STAFF

part3/worker.js

@@ -1,6 +1,7 @@
+const N= 393216;  // Number of cache lines 24MB/64B
 // Number of sweep counts
 // TODO (Exercise 3-1): Choose an appropriate value!
-let P = 1000;
+let P = 10;
 
 // Number of elements in your trace
 let K = 5 * 1000 / P; 
@@ -12,19 +13,33 @@ let T;
 let start;
 
 function record() {
+  
   // Create empty array for saving trace values
   T = new Array(K);
 
   // Fill array with -1 so we can be sure memory is allocated
   T.fill(-1, 0, T.length);
-
+  const LINE_SIZE = 8; // 64/sizeof(double) Note that js treats all numbers as double
+  const M = new Array(N * LINE_SIZE).fill(-1);
   // Save start timestamp
   start = performance.now();
+  for (let i = 0; i < K; i++) {
+    let count=0;
+    while (performance.now() - start < P*(i+1)) {
+      // sweep the cache
+      for (let j = 0; j < N; j++) {
+        // let val = M[j * LINE_SIZE];
+      }
+      count++;
+    }
+    T[i]=count;
+  }
 
-  // TODO (Exercise 3-1): Record data for 5 seconds and save values to T.
+  // TODO (Exercise 2-1): Record data for 5 seconds and save values to T.
 
   // Once done recording, send result to main thread
   postMessage(JSON.stringify(T));
+  console.log(T);
 }
 
 // DO NOT MODIFY BELOW THIS LINE -- PROVIDED BY COURSE STAFF