diff --git a/A2/graph/performance.py b/A2/graph/performance.py
index 6a098b3..c62817d 100755
--- a/A2/graph/performance.py
+++ b/A2/graph/performance.py
@@ -1,40 +1,46 @@
 #!/usr/bin/python
 
 import os
 import numpy as np
 from numpy import array
 
 testdir = "tests"
 os.system("mkdir -p " + testdir)
 
 Ntr = [1, 2, 4, 8, 16]
 #sample = "Running the algorithm with 4 threads on 10000 by 10000 array for 5 iterations took 1.547 seconds"
 
 def loadtimes(fstream):
     t = [] 
     for line in fstream:
         # skip if no information is contained
         if not line.startswith("Running the algorithm"):
             continue
 
         values = line.split(' ') # split into string set
         values = [i for i in values if i != ''] # fliter null strings
         t.append(float(values[15])) # 16-th value should contain the elapsed time
     return t
 
 T = [] # times
+S = [] # speedups
 for filename in os.listdir(os.getcwd() + '/' + testdir):
     with open(os.path.join(os.getcwd() + '/' + testdir, filename), 'r') as f:
         t = np.array(loadtimes(f))
         T.append(t)
+        t0 = t[0]
+        S.append(t0 / t)
 
 Tmean = np.mean(T, axis=0).tolist()
 Tsq = np.sqrt(np.var(T, axis=0)).tolist() # compute mean and variance of arrays
 
+Smean = np.mean(S, axis=0).tolist()
+Ssq = np.sqrt(np.var(S, axis=0)).tolist() # compute mean and variance of arrays
+
 # output values
 out = open("times.txt", 'w')
-print("ntr  t  dt", file=out) 
-for (ntr, t, dt) in zip(Ntr, Tmean, Tsq):
-    print("%d  %.3g  %.3g" % (ntr, t, dt), file=out) 
+print("ntr  t  dt  s  ds", file=out) 
+for (ntr, t, dt, s, ds) in zip(Ntr, Tmean, Tsq, Smean, Ssq):
+    print("%d  %.3g  %.3g  %.3g  %.3g" % (ntr, t, dt, s, ds), file=out) 
 out.close()
 
diff --git a/A2/graph/times.txt b/A2/graph/times.txt
index b5b9d59..7dc2b0c 100644
--- a/A2/graph/times.txt
+++ b/A2/graph/times.txt
@@ -1,6 +1,6 @@
-ntr  t  dt
-1  1.55  0
-2  1.55  0
-4  1.55  0
-8  1.55  0
-16  1.55  0
+ntr  t  dt  s  ds
+1  49.2  7.31  1  0
+2  25  3.5  1.97  0.0408
+4  12.6  1.6  3.88  0.108
+8  6.72  1.01  7.38  0.818
+16  5.06  1.4  10.3  2.53