diff --git a/lab07/aaron/e1/jacobiWaveSplitWork.cpp b/lab07/aaron/e1/jacobiWaveSplitWork.cpp
index 4f5db92..00d22b6 100644
--- a/lab07/aaron/e1/jacobiWaveSplitWork.cpp
+++ b/lab07/aaron/e1/jacobiWaveSplitWork.cpp
@@ -45,6 +45,7 @@
  * -------------
  */
 
+#include <benchmark/benchmark.h>
 #include "matrix.h"
 #include <omp.h>
 #include <atomic>
@@ -168,24 +169,49 @@ void print_matrix(const Matrix &matrix)
   }
 }
 
-int main()
-{
-  Matrix first = Matrix(8, 8);
-  Matrix sec = Matrix(8, 8);
-
-  fill_matrix(first, 10);
+void benchmarkComputeResult(benchmark::State& state) {
+  int iterations = state.range(0);
+  Matrix sec = Matrix(1000, 1000);
   fill_matrix(sec, 10);
 
-  print_matrix(first);
-  print_matrix(sec);
+  for (auto _ : state) {
+    gauss_seidel(sec, iterations);
+    benchmark::DoNotOptimize(sec);
+  }
+}
 
-  gauss_seidel_lin(first, 1000);
-  gauss_seidel(sec, 1000);
+int main(int argc, char** argv) {
+  ::benchmark::Initialize(&argc, argv);
 
-  check(first, sec);
+  for (int iterations = 0; iterations < 10000; iterations++) {
+    benchmark::RegisterBenchmark("idk", benchmarkComputeResult)
+        ->Arg(iterations)
+        ->Unit(benchmark::kMillisecond);
+  }
 
-  print_matrix(first);
-  print_matrix(sec);
+  ::benchmark::RunSpecifiedBenchmarks();
 
   return 0;
-}
\ No newline at end of file
+}
+
+// int main()
+// {
+//   Matrix first = Matrix(8, 8);
+//   Matrix sec = Matrix(8, 8);
+
+//   fill_matrix(first, 10);
+//   fill_matrix(sec, 10);
+
+//   print_matrix(first);
+//   print_matrix(sec);
+
+//   gauss_seidel_lin(first, 1000);
+//   gauss_seidel(sec, 1000);
+
+//   check(first, sec);
+
+//   print_matrix(first);
+//   print_matrix(sec);
+
+//   return 0;
+// }
\ No newline at end of file
diff --git a/lab07/aaron/e2/upload/dbscan_parallel.cpp b/lab07/aaron/e2/upload/dbscan_parallel.cpp
index 16a3ae6..2cf7f56 100644
--- a/lab07/aaron/e2/upload/dbscan_parallel.cpp
+++ b/lab07/aaron/e2/upload/dbscan_parallel.cpp
@@ -13,6 +13,7 @@ void DBSCAN::run(const std::vector<Point>& points) {
   const int n = dataset_.size();
 
   int clusterIndex = 0;
+  #pragma omp parallel for
   for (int i = 0; i < n; ++i) {
     Point& point = dataset_[i];
     if (point.clusterID < 0) {
@@ -56,7 +57,7 @@ std::set<int> DBSCAN::initializeNeighbors() {
         Point& pointToCheckNeighborsFor = dataset_[i];
         for (int j = 0; j < dataset_.size(); ++j) {
             if (pointToCheckNeighborsFor.distance(dataset_[j]) <= epsilon_) {
-                pointToCheckNeighborsFor.neighbors.insert(j);
+              pointToCheckNeighborsFor.neighbors.insert(j);
             }
         }
     }
diff --git a/lab07/aaron/e2/upload/dbscan_serial.cpp b/lab07/aaron/e2/upload/dbscan_serial.cpp
index b940748..bbb43ea 100644
--- a/lab07/aaron/e2/upload/dbscan_serial.cpp
+++ b/lab07/aaron/e2/upload/dbscan_serial.cpp
@@ -1,4 +1,4 @@
-#include "dbscan.h"
+#include "dbscan_serial.h"
 #include <cmath>
 #include <iostream>
 
@@ -11,6 +11,7 @@ void DBSCAN::run(const std::vector<Point>& points) {
   const int n = dataset_.size();
 
   int clusterIndex = 0;
+
   for (int i = 0; i < n; ++i) {
     Point& point = dataset_[i];
     if (point.clusterID < 0) {
@@ -23,6 +24,10 @@ void DBSCAN::run(const std::vector<Point>& points) {
       }
     }
   }
+
+  for (Point point : dataset_) {
+    std::cout << point << std::endl;
+  }
 }
 
 bool DBSCAN::expandCluster(Point& p, std::set<int>& neighbours, int clusterID) {
diff --git a/lab07/aaron/e2/upload/makefile b/lab07/aaron/e2/upload/makefile
index e1863e0..17c1d54 100644
--- a/lab07/aaron/e2/upload/makefile
+++ b/lab07/aaron/e2/upload/makefile
@@ -6,11 +6,11 @@
 
 # SOURCE_FILES: The source files of the algorithm, used for each build.
 # You can add more source files here if needed.
-SOURCE_FILES = dbscan.cpp point.cpp
+SOURCE_FILES = dbscan_serial.cpp point.cpp
 
 # Main rogram, used to cluster the data and save the result.
 # PROGRAM_NAME: The name of the program that will be generated after compilation.
-PROGRAM_NAME = dbscan
+PROGRAM_NAME = dbscan_serial
 RUN_MAIN = run.cpp
 
 # Benchmark program: This program is used to benchmark the performance of the algorithm.