diff --git a/big_data_analsis.R b/big_data_analsis.R
deleted file mode 100644
index 2b2ccfa..0000000
--- a/big_data_analsis.R
+++ /dev/null
@@ -1,26 +0,0 @@
-# Plot the data
-
-# Imports
-library(reshape2)
-library(ggplot2)
-
-# Load the data
-# Change path to file
-# data_path <- '../parallel/analysis.csv'  # You can use this instead
-data_path <- '/home/reynaldo/Documents/School/Spring2017/HPC/project/parallel_mcmc/parallel/complete_big_analysis.csv'
-perf_data <- read.csv(file=data_path, header=TRUE, sep=",")
-melted_data <- melt(perf_data, id=c("trial", "block", "thread"))
-five_hundred_trials <- melted_data[which(melted_data$trial == 500), ]
-five_hundred_trials$thread = as.factor(five_hundred_trials$thread)
-
-
-# Find which combination of blocks and threads works best
-ours_five_hundred_trials <- five_hundred_trials[which(five_hundred_trials$variable == "our_time"), ]
-ours_five_hundred_trials <- ours_five_hundred_trials[which(!is.na(ours_five_hundred_trials$thread)), ]
-
-line_base <- ggplot(ours_five_hundred_trials, aes(x=block, y=value, group=thread, shape=thread, color=thread)) +
-  geom_line() +
-  geom_point()
-line_base <- line_base +  xlab("Blocks") + ylab("Time (seconds)") + labs(colour = "Num Threads", shape="Num Threads")
-line_base <- line_base + ggtitle("Blocks vs Time vs Threads") + theme(plot.title = element_text(hjust = 0.5))
-line_base
\ No newline at end of file
diff --git a/generate_plots/big_data_analsis.R b/generate_plots/big_data_analsis.R
new file mode 100644
index 0000000..073f936
--- /dev/null
+++ b/generate_plots/big_data_analsis.R
@@ -0,0 +1,74 @@
+# Plot the data
+
+# Imports
+library(reshape2)
+library(ggplot2)
+
+# Load the data
+# Change path to file
+# data_path <- '../parallel/analysis.csv'  # You can use this instead
+data_path <- '/home/reynaldo/Documents/School/Spring2017/HPC/project/parallel_mcmc/parallel/complete_big_analysis.csv'
+perf_data <- read.csv(file=data_path, header=TRUE, sep=",")
+melted_data <- melt(perf_data, id=c("trial", "block", "thread"))
+five_hundred_trials <- melted_data[which(melted_data$trial == 500), ]
+
+
+# Find which combination of blocks and threads works best
+five_hundred_trials$thread = as.factor(five_hundred_trials$thread)
+
+ours_five_hundred_trials <- five_hundred_trials[which(five_hundred_trials$variable == "our_time"), ]
+ours_five_hundred_trials <- ours_five_hundred_trials[which(!is.na(ours_five_hundred_trials$thread)), ]
+
+line_base <- ggplot(ours_five_hundred_trials, aes(x=block, y=value, group=thread, shape=thread, color=thread)) +
+  geom_line() +
+  geom_point()
+line_base <- line_base +  xlab("Blocks") + ylab("Time (seconds)") + labs(colour = "Num Threads", shape="Num Threads")
+line_base <- line_base + ggtitle("Blocks vs Time vs Threads") + theme(plot.title = element_text(hjust = 0.5))
+line_base
+
+# Best Number of threads vs The other implementations for Blocks vs Time
+five_hundred_trials$thread = as.factor(five_hundred_trials$thread)
+one_threads_500_trials <- five_hundred_trials[which(five_hundred_trials$thread == 1), ]
+
+line_base <- ggplot(one_threads_500_trials, aes(x=block, y=value, group=variable, shape=variable, color=variable)) +
+  geom_line() +
+  geom_point()
+line_base <- line_base +  xlab("Blocks") + ylab("Time (seconds)") + labs(colour = "Implementations", shape="Implementations")
+line_base <- line_base + ggtitle("Blocks vs Time vs Implementation") + theme(plot.title = element_text(hjust = 0.5))
+line_base
+
+
+# Find which combination of blocks and threads works best
+five_hundred_trials$thread = as.factor(five_hundred_trials$thread)
+ours_five_hundred_trials <- five_hundred_trials[which(five_hundred_trials$variable == "our_time"), ]
+ours_five_hundred_trials <- ours_five_hundred_trials[which(!is.na(ours_five_hundred_trials$thread)), ]
+
+line_base <- ggplot(ours_five_hundred_trials, aes(x=block, y=value, group=thread, shape=thread, color=thread)) +
+  geom_line() +
+  geom_point()
+line_base <- line_base +  xlab("Blocks") + ylab("Time (seconds)") + labs(colour = "Num Threads", shape="Num Threads")
+line_base <- line_base + ggtitle("Blocks vs Time vs Threads") + theme(plot.title = element_text(hjust = 0.5))
+line_base
+
+
+# Create Scatter plot (Blocks vs Threads)
+five_hundred_trials$block = as.factor(five_hundred_trials$block)
+
+base_plot <- ggplot(five_hundred_trials, aes(x=block, y=time, color=variable))
+base_plot <- base_plot  +  geom_point(aes(x=block, y=thread, size=value, color=variable)) 
+base_plot <- base_plot + scale_size(range = c(0, 10))
+base_plot <- base_plot + xlab("Blocks") + ylab("Time (seconds)") + labs(colour = "Implementation", size="Time")
+base_plot <- base_plot + ggtitle("Blocks vs Time vs Implementation") + theme(plot.title = element_text(hjust = 0.5))
+base_plot
+
+
+# Find which combination of blocks and threads works best
+ours_five_hundred_trials <- five_hundred_trials[which(five_hundred_trials$variable == "our_time" & five_hundred_trials$block ==1 & five_hundred_trials$thread < 64 ), ]
+ours_five_hundred_trials$thread <- as.factor(ours_five_hundred_trials$thread)
+ours_five_hundred_trials <- ours_five_hundred_trials[which(!is.na(ours_five_hundred_trials$thread)), ]
+
+bar_base <- ggplot(ours_five_hundred_trials, aes(x=thread, y=value, fill=thread)) + geom_col(width=0.5)
+bar_base <- bar_base +  xlab("Threads") + ylab("Time (seconds)")
+bar_base <- bar_base + ggtitle("Big Data Threads vs Time (1 Block)") + theme(plot.title = element_text(hjust = 0.5))
+bar_base <- bar_base +   theme(legend.position="none")
+bar_base
diff --git a/data_analysis.R b/generate_plots/data_analysis.R
similarity index 71%
rename from data_analysis.R
rename to generate_plots/data_analysis.R
index 390e2b6..37bef7d 100644
--- a/data_analysis.R
+++ b/generate_plots/data_analysis.R
@@ -6,7 +6,8 @@ library(ggplot2)
 
 # Load the data
 # Change path to file
-# data_path <- '../parallel/analysis.csv'  # You can use this instead
+# data_path <- '../parallel/complete_analysis.csv'  # You can use this instead
+# data_path <- '/home/reynaldo/Documents/School/Spring2017/HPC/project/parallel_mcmc/parallel/complete_analysisSmallFast.csv'
 data_path <- '/home/reynaldo/Documents/School/Spring2017/HPC/project/parallel_mcmc/parallel/complete_analysis.csv'
 perf_data <- read.csv(file=data_path, header=TRUE, sep=",")
 melted_data <- melt(perf_data, id=c("trial", "block", "thread"))
@@ -20,7 +21,7 @@ five_hundred_trials$thread = as.factor(five_hundred_trials$thread)
 base_plot <- ggplot(five_hundred_trials, aes(x=block, y=time, color=variable))
 base_plot <- base_plot  +  geom_point(aes(x=block, y=thread, size=value, color=variable)) 
 base_plot <- base_plot + scale_size(range = c(0, 10))
-base_plot <- base_plot + xlab("Threads") + ylab("Time (seconds)")
+base_plot <- base_plot + xlab("Blocks") + ylab("Threads") + labs(colour = "Implementation", size="Time")
 base_plot <- base_plot + ggtitle("Blocks vs Time") + theme(plot.title = element_text(hjust = 0.5))
 base_plot
 
@@ -47,4 +48,12 @@ line_base <- line_base + ggtitle("Blocks vs Time vs Implementation") + theme(plo
 line_base
 
 
+# Trials vs Time vs Implementation
+one_thread_one_block <- melted_data[which(melted_data$thread == 1 & melted_data$block == 1), ]
 
+line_base <- ggplot(one_thread_one_block, aes(x=trial, y=value, group=variable, shape=variable, color=variable)) +
+  geom_line() +
+  geom_point()
+line_base <- line_base +  xlab("Trials") + ylab("Time (seconds)") + labs(colour = "Implementations", shape="Implementations")
+line_base <- line_base + ggtitle("Small Data Trials vs Time 1 Block 1 Thread") + theme(plot.title = element_text(hjust = 0.5))
+line_base
diff --git a/generate_plots/fast_data_analysis.R b/generate_plots/fast_data_analysis.R
new file mode 100644
index 0000000..c05e3e3
--- /dev/null
+++ b/generate_plots/fast_data_analysis.R
@@ -0,0 +1,71 @@
+# Plot the data
+
+# Imports
+library(reshape2)
+library(ggplot2)
+
+# Load the data
+# Change path to file
+# data_path <- '../parallel/analysis.csv'  # You can use this instead
+data_path <- '/home/reynaldo/Documents/School/Spring2017/HPC/project/parallel_mcmc/parallel/complete_analysis.csv'
+perf_data <- read.csv(file=data_path, header=TRUE, sep=",")
+melted_data <- melt(perf_data, id=c("trial", "block", "thread"))
+five_hundred_trials <- melted_data[which(melted_data$trial == 500), ]
+five_hundred_trials$thread = as.factor(five_hundred_trials$thread)
+
+data_path <- '/home/reynaldo/Documents/School/Spring2017/HPC/project/parallel_mcmc/parallel/complete_analysisSmallFast.csv'
+fast_perf_data <- read.csv(file=data_path, header=TRUE, sep=",")
+fast_melted_data <- melt(fast_perf_data, id=c("trial", "block", "thread"))
+fast_five_hundred_trials <- fast_melted_data[which(fast_melted_data$trial == 500), ]
+fast_five_hundred_trials$thread = as.factor(fast_five_hundred_trials$thread)
+
+
+
+# Generate Plots
+
+# Create Scatter plot (Blocks vs Threads)
+base_plot <- ggplot(fast_five_hundred_trials, aes(x=block, y=time, color=variable))
+base_plot <- base_plot  +  geom_point(aes(x=block, y=thread, size=value, color=variable)) 
+base_plot <- base_plot + scale_size(range = c(0, 10))
+base_plot <- base_plot + xlab("Blocks") + ylab("Time (seconds)") + labs(colour = "Implementation", size="Time")
+base_plot <- base_plot + ggtitle("Faster Blocks vs Time") + theme(plot.title = element_text(hjust = 0.5))
+base_plot
+
+# Find which combination of blocks and threads works best
+fast_ours_five_hundred_trials <- fast_five_hundred_trials[which(fast_five_hundred_trials$variable == "our_time"), ]
+fast_ours_five_hundred_trials <- fast_ours_five_hundred_trials[which(!is.na(fast_ours_five_hundred_trials$thread)), ]
+
+line_base <- ggplot(fast_ours_five_hundred_trials, aes(x=block, y=value, group=thread, shape=thread, color=thread)) +
+  geom_line() +
+  geom_point()
+line_base <- line_base +  xlab("Blocks") + ylab("Time (seconds)") + labs(colour = "Num Threads", shape="Num Threads")
+line_base <- line_base + ggtitle("Faster Blocks vs Time vs Threads") + theme(plot.title = element_text(hjust = 0.5))
+line_base
+
+
+# Merge two datasets
+our_fast_time_melted = fast_melted_data[(which(fast_melted_data$variable == "our_fast_time")), ]
+all_melted_data <- rbind(melted_data, our_fast_time_melted)
+all_five_hundred_trials <- all_melted_data[which(all_melted_data$trial == 500), ]
+
+
+# Best Number of threads vs The other implementations for Blocks vs Time
+one_threads_500_trials <- all_five_hundred_trials[which(all_five_hundred_trials$thread == 1), ]
+
+line_base <- ggplot(one_threads_500_trials, aes(x=block, y=value, group=variable, shape=variable, color=variable)) +
+  geom_line() +
+  geom_point()
+line_base <- line_base +  xlab("Blocks") + ylab("Time (seconds)") + labs(colour = "Implementations", shape="Implementations")
+line_base <- line_base + ggtitle("Blocks vs Time vs Implementation") + theme(plot.title = element_text(hjust = 0.5))
+line_base
+
+
+# Trials vs Time vs Implementation
+one_thread_one_block <- all_melted_data[which(all_melted_data$thread == 1 & all_melted_data$block == 1), ]
+
+line_base <- ggplot(one_thread_one_block, aes(x=trial, y=value, group=variable, shape=variable, color=variable)) +
+  geom_line() +
+  geom_point()
+line_base <- line_base +  xlab("Trials") + ylab("Time (seconds)") + labs(colour = "Implementations", shape="Implementations")
+line_base <- line_base + ggtitle("Small Data Trials vs Time 1 Block 1 Thread") + theme(plot.title = element_text(hjust = 0.5))
+line_base
diff --git a/parallel/complete_analysisSmallFast.csv b/parallel/complete_analysisSmallFast.csv
new file mode 100644
index 0000000..538ece3
--- /dev/null
+++ b/parallel/complete_analysisSmallFast.csv
@@ -0,0 +1,125 @@
+trial,block,thread,seq_time,our_fast_time,their_time
+500,1,1,1.30,11.36,5.02
+500,1,2,1.25,7.97,5.03
+500,1,4,1.25,6.46,5.06
+500,1,8,1.27,5.72,4.93
+500,1,16,1.25,5.82,5.17
+500,1,32,1.26,7.64,4.96
+500,2,1,1.25,7.84,5.30
+500,2,2,1.25,6.21,4.96
+500,2,4,1.35,5.79,5.19
+500,2,8,1.25,5.98,4.95
+500,2,16,1.25,7.60,5.02
+500,2,32,1.25,15.66,5.10
+500,3,1,1.26,6.67,4.97
+500,3,2,1.31,5.84,5.29
+500,3,4,1.25,5.60,5.02
+500,3,8,1.30,6.67,5.23
+500,3,16,1.27,10.95,4.96
+500,3,32,1.27,29.33,5.11
+500,4,1,1.27,6.13,4.98
+500,4,2,1.30,6.03,5.44
+500,4,4,1.25,6.38,5.00
+500,4,8,1.29,7.56,4.97
+500,4,16,1.25,15.77,4.99
+500,4,32,1.35,48.40,5.03
+500,5,1,1.24,5.93,4.90
+500,5,2,1.29,5.66,5.27
+500,5,4,1.24,6.10,5.13
+500,5,8,1.25,9.07,4.99
+500,5,16,1.24,21.89,5.27
+500,5,32,1.25,73.22,4.97
+500,6,1,1.30,5.89,5.02
+500,6,2,1.25,5.46,4.97
+500,6,4,1.31,6.46,5.05
+500,6,8,1.25,10.91,5.06
+500,6,16,1.25,29.22,5.03
+500,6,32,1.25,102.84,5.09
+500,7,1,1.24,5.65,5.10
+500,7,2,1.25,5.62,5.19
+500,7,4,1.24,7.00,4.93
+500,7,8,1.23,13.18,5.05
+500,7,16,1.31,38.55,4.92
+500,7,32,1.23,138.31,4.98
+500,8,1,1.24,5.48,5.67
+500,8,2,1.25,5.71,5.06
+500,8,4,1.25,7.63,5.03
+500,8,8,1.28,15.58,4.99
+500,8,16,1.23,48.45,5.15
+500,8,32,1.25,179.02,4.92
+500,9,1,1.25,5.56,5.30
+500,9,2,1.25,6.51,5.07
+500,9,4,1.26,8.76,4.94
+500,9,8,1.25,18.86,5.06
+500,9,16,1.25,60.05,5.05
+500,9,32,1.24,225.00,5.07
+500,10,1,1.23,5.53,4.94
+500,10,2,1.23,6.13,4.99
+500,10,4,1.25,9.13,5.00
+500,10,8,1.27,21.88,4.93
+500,10,16,1.25,72.91,5.01
+500,10,32,1.24,276.43,4.95
+1000,1,1,2.49,17.57,5.42
+1000,1,2,2.49,11.41,5.60
+1000,1,4,2.48,8.28,5.42
+1000,1,8,2.48,7.02,5.88
+1000,1,16,2.48,7.12,5.40
+1000,1,32,2.62,10.85,5.71
+1000,2,1,2.54,11.04,5.48
+1000,2,2,2.49,8.01,5.65
+1000,2,4,2.48,6.69,5.39
+1000,2,8,2.56,7.11,5.71
+1000,2,16,2.49,10.78,5.47
+1000,2,32,2.48,27.08,5.70
+1000,3,1,2.48,9.22,5.50
+1000,3,2,2.49,7.04,5.41
+1000,3,4,2.49,6.66,5.51
+1000,3,8,2.51,8.34,5.48
+1000,3,16,2.51,17.51,5.66
+1000,3,32,2.51,54.22,5.48
+1000,4,1,2.50,8.07,5.63
+1000,4,2,2.51,6.73,5.42
+1000,4,4,2.51,7.13,5.53
+1000,4,8,2.49,11.51,5.52
+1000,4,16,2.52,30.24,5.72
+1000,4,32,2.52,92.38,5.75
+1000,5,1,2.55,7.37,5.42
+1000,5,2,2.52,6.84,6.10
+1000,5,4,2.52,7.76,5.88
+1000,5,8,2.51,13.70,5.40
+1000,5,16,2.51,39.26,5.60
+1000,5,32,2.48,140.90,5.45
+1000,6,1,2.49,6.93,5.43
+1000,6,2,2.49,6.59,5.58
+1000,6,4,2.49,8.45,5.45
+1000,6,8,2.53,17.31,5.49
+1000,6,16,2.52,54.23,5.57
+1000,6,32,2.51,201.19,5.43
+1000,7,1,2.52,6.90,5.48
+1000,7,2,2.51,6.74,5.66
+1000,7,4,2.51,9.92,5.49
+1000,7,8,2.51,21.70,5.67
+1000,7,16,2.51,71.72,5.80
+1000,7,32,2.51,272.19,5.45
+1000,8,1,2.50,6.61,5.49
+1000,8,2,2.53,6.99,5.54
+1000,8,4,2.54,10.56,5.54
+1000,8,8,2.53,26.87,5.46
+1000,8,16,2.52,92.36,5.53
+1000,8,32,2.51,353.73,5.53
+1000,9,1,2.53,6.82,5.61
+1000,9,2,2.50,7.87,5.52
+1000,9,4,2.51,12.98,5.47
+1000,9,8,2.48,32.81,6.09
+1000,9,16,2.51,115.26,5.48
+1000,9,32,2.51,445.90,5.41
+1000,10,1,2.51,6.44,5.38
+1000,10,2,2.48,7.71,5.67
+1000,10,4,2.49,14.83,5.46
+1000,10,8,2.49,39.36,5.42
+1000,10,16,2.51,141.47,5.54
+1000,10,32,2.52,549.02,3.46
+1500,1,1,3.76,22.15,4.00
+1500,1,2,3.74,12.74,3.98
+1500,1,4,3.73,8.01,4.00
+1500,1,8,3.73,6.14,3.96
diff --git a/parallel/empty_file b/parallel/empty_file
deleted file mode 100644
index e69de29..0000000