diff --git a/merge_benchmark_results.py b/merge_benchmark_results.py
new file mode 100644
index 0000000..4cd3b1b
--- /dev/null
+++ b/merge_benchmark_results.py
@@ -0,0 +1,58 @@
+import pandas as pd
+import os
+import argparse
+
+def load_csv(file_path):
+    """Load a CSV file into a DataFrame."""
+    return pd.read_csv(file_path)
+
+def extract_prefix_and_column(column_name):
+    """Extract prefix and column name from the format <prefix>_columnname."""
+    if '_' in column_name:
+        prefix, col = column_name.split(':', 1)
+        return prefix, col
+    return None, column_name
+
+def main():
+    parser = argparse.ArgumentParser(description="Select columns based on file prefixes and merge them.")
+    parser.add_argument('files', nargs='+', help='Paths to CSV files')
+    parser.add_argument('-c', '--columns', nargs='+', required=True,
+                        help='List of columns to select with prefixes (e.g., file1_col1, file2_col2)')
+    parser.add_argument('-o', '--output', required=True, help='Output CSV file path')
+    
+    args = parser.parse_args()
+
+    # Load all CSV files into a dictionary with the base filename as the key
+    dataframes = {}
+    for file in args.files:
+        base_filename = os.path.splitext(os.path.basename(file))[0]
+        dataframes[base_filename] = load_csv(file)
+        print(f'Loading {file}')
+    
+    # Prepare an empty DataFrame to store selected columns
+    merged_df = pd.DataFrame()
+
+    # Iterate over the specified columns
+    for col_name in args.columns:
+        prefix, col = extract_prefix_and_column(col_name)
+        
+        print(f'Processing {prefix}:{col}')
+        # Check if the prefix matches any loaded file
+        if prefix in dataframes:
+            df = dataframes[prefix]
+            print(f'Got data frame {prefix} with cols {df.columns.tolist()}')
+            
+            # Ensure the column exists in the corresponding DataFrame
+            if col in df.columns:
+                merged_df[f"{prefix}_{col}"] = df[col]
+            else:
+                print(f"Warning: Column '{col}' not found in file '{prefix}'. Skipping...")
+        else:
+            print(f"Warning: No CSV file found with prefix '{prefix}'. Skipping...")
+
+    # Write the merged DataFrame to the output CSV file
+    merged_df.to_csv(args.output, index=False)
+    print(f"Data successfully written to {args.output}")
+
+if __name__ == "__main__":
+    main()
diff --git a/plot_custom_benchmark_results.py b/plot_custom_benchmark_results.py
new file mode 100644
index 0000000..d9493fa
--- /dev/null
+++ b/plot_custom_benchmark_results.py
@@ -0,0 +1,68 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+import argparse
+
+def showPlot(title, x_label, y_label, labels, samples_sets, show_legend, axis):
+    SAMPLE_SIZE = 60
+
+    x_axis = range(SAMPLE_SIZE)
+
+    for n in range(len(samples_sets)):
+        sample_set = samples_sets[n]
+
+        sample_set_means = []
+        sample_set_std_deviations = []
+        for i in range(SAMPLE_SIZE):
+            mean = 0
+            count = 0
+            for j in range(i, len(sample_set), SAMPLE_SIZE):
+                mean += sample_set[j]
+                count += 1
+            mean /= count
+
+            variance = 0
+            for j in range(i, len(sample_set), SAMPLE_SIZE):
+                variance += (sample_set[j] - mean)*(sample_set[j] - mean)
+            variance /= count
+
+            std_dev = np.sqrt(variance)
+
+            sample_set_means.append(mean)
+            sample_set_std_deviations.append(std_dev)
+        
+        if show_legend:
+            axis.errorbar(x_axis, y=sample_set_means, yerr=sample_set_std_deviations, label=labels[n])
+        else:
+            axis.errorbar(x_axis, y=sample_set_means, yerr=sample_set_std_deviations)
+
+    axis.set_title(title)
+    axis.set(xlabel=x_label, ylabel=y_label)
+    if show_legend:
+        axis.legend()
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='ve001 benchmark plot generator')
+    parser.add_argument('--csvfile', help='columns count')
+    parser.add_argument('--titles', nargs='+', required=True)
+    parser.add_argument('--xlabel', help='columns count')
+    parser.add_argument('--ylabel', help='columns count')
+    parser.add_argument('--divisor', help='columns count')
+    parser.add_argument('--w_space', '-w', help='columns count')
+    parser.add_argument('--sharey', '-y', help='columns count')
+    app_args = parser.parse_args()
+        
+    samples = pd.read_csv(app_args.csvfile)
+
+    _, axes = plt.subplots(1, len(samples.columns.tolist()), sharey=True if \
+            int(app_args.sharey) == 1 else False)
+    
+    for col_name, title, axis in zip(samples.columns.tolist(), app_args.titles, axes):
+        showPlot(title, app_args.xlabel, app_args.ylabel, [], \
+                [samples[col_name]/int(app_args.divisor)], False, axis)
+
+    if float(app_args.w_space) != 0.0:
+        plt.subplots_adjust(wspace=float(app_args.w_space))
+
+    plt.show()
+