Index: doc/theses/colby_parsons_MMAth/benchmarks/mutex_stmt/data/nasus.txt
===================================================================
--- doc/theses/colby_parsons_MMAth/benchmarks/mutex_stmt/data/nasus.txt	(revision 14e105369536401baf2229cef802a60315acf849)
+++ doc/theses/colby_parsons_MMAth/benchmarks/mutex_stmt/data/nasus.txt	(revision 14e105369536401baf2229cef802a60315acf849)
@@ -0,0 +1,826 @@
+11
+2 4 8 16 24 32
+2 4 8 
+CFA-baseline CPP-baseline CFA-rand CPP-rand 
+locks: 2
+CFA-baseline:
+cores	throughput (entries)
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+CPP-baseline:
+cores	throughput (entries)
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+CFA-rand:
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+CPP-rand:
+cores	throughput (entries)
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+locks: 4
+CFA-baseline:
+cores	throughput (entries)
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+CPP-baseline:
+cores	throughput (entries)
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+
+CFA-rand:
+cores	throughput (entries)
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+CPP-rand:
+cores	throughput (entries)
+2	19493422
+2	19579521
+2	19743496
+2	19741567
+2	20354480
+2	20145079
+2	18961059
+2	17895926
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+8	120210
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+8	139413
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+16	2382
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+24	9370
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+24	1187
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+32	5204
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+32	5340
+32	5391
+32	6772
+32	4656
+32	3163
+32	4529
+32	5691
+locks: 8
+CFA-baseline:
+cores	throughput (entries)
+2	44460716
+2	44393108
+2	44156750
+2	44189538
+2	44524595
+2	43979623
+2	44434622
+2	44258555
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+CPP-baseline:
+cores	throughput (entries)
+2	122140484
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+
+CFA-rand:
+cores	throughput (entries)
+2	90701593
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+CPP-rand:
+cores	throughput (entries)
+2	1273726
+2	1266301
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+16	1103
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+32	1048
+32  917
Index: doc/theses/colby_parsons_MMAth/benchmarks/mutex_stmt/data/pyke.txt
===================================================================
--- doc/theses/colby_parsons_MMAth/benchmarks/mutex_stmt/data/pyke.txt	(revision 14e105369536401baf2229cef802a60315acf849)
+++ doc/theses/colby_parsons_MMAth/benchmarks/mutex_stmt/data/pyke.txt	(revision 14e105369536401baf2229cef802a60315acf849)
@@ -0,0 +1,826 @@
+11
+2 4 8 16 24 32
+2 4 8 
+CFA-baseline CPP-baseline CFA-rand CPP-rand 
+locks: 2
+CFA-baseline:
+cores	throughput (entries)
+2	156366158
+2	148111668
+2	159629122
+2	134037579
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+2	160092915
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+CPP-baseline:
+cores	throughput (entries)
+2	151210672
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+locks: 4
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+CFA-rand:
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+CPP-rand:
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+locks: 8
+CFA-baseline:
+cores	throughput (entries)
+2	22433593
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+2	27636603
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+CPP-baseline:
+cores	throughput (entries)
+2	36207876
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+2	42357750
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+
+CFA-rand:
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+CPP-rand:
+cores	throughput (entries)
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Index: doc/theses/colby_parsons_MMAth/benchmarks/mutex_stmt/genPlots
===================================================================
--- doc/theses/colby_parsons_MMAth/benchmarks/mutex_stmt/genPlots	(revision 14e105369536401baf2229cef802a60315acf849)
+++ doc/theses/colby_parsons_MMAth/benchmarks/mutex_stmt/genPlots	(revision 14e105369536401baf2229cef802a60315acf849)
@@ -0,0 +1,3 @@
+#!/bin/bash -
+python3 plotData.py data/nasus.txt nasus_
+python3 plotData.py data/pyke.txt pyke_
Index: doc/theses/colby_parsons_MMAth/benchmarks/mutex_stmt/plotData.py
===================================================================
--- doc/theses/colby_parsons_MMAth/benchmarks/mutex_stmt/plotData.py	(revision 917e1fda57f8a41eccff6a6e0ab336b41d4b6e98)
+++ doc/theses/colby_parsons_MMAth/benchmarks/mutex_stmt/plotData.py	(revision 14e105369536401baf2229cef802a60315acf849)
@@ -60,5 +60,5 @@
 name = "Aggregate Lock"
 var_name = ""
-experiment_duration = 10
+experiment_duration = 10.0
 sendData = [0.0 for j in range(numVariants)]
 data = [[0.0 for i in range(len(procs))] for j in range(numVariants)]
@@ -87,6 +87,6 @@
         data[currVariant][procCount] = currMedian
         lower, upper = st.t.interval(0.95, numTimes - 1, loc=np.mean(tempData), scale=st.sem(tempData))
-        bars[currVariant][0][procCount] = currMedian - lower
-        bars[currVariant][1][procCount] = upper - currMedian
+        bars[currVariant][0][procCount] = max( 0, currMedian - lower )
+        bars[currVariant][1][procCount] = max( 0, upper - currMedian )
         count = 0
         procCount += 1
@@ -100,5 +100,5 @@
                 fig, ax = plt.subplots(layout='constrained')
                 plt.title(name + " Benchmark: " + str(currLocks) + " Locks")
-                plt.ylabel("Throughput (entries)")
+                plt.ylabel("Throughput (critical section entries per second)")
                 plt.xlabel("Cores")
                 for idx, arr in enumerate(data):
