add build script

benchmarks/benchmark.sh

@@ -12,21 +12,7 @@ cd .. || exit
 
 # === Build project ===
 echo "Building mandelbrot benchmark"
-
-source /opt/intel/oneapi/setvars.sh
-
-if [ ! -d "build" ]; then
-    mkdir build
-fi
-
-# Change to build directory
-cd build || exit
-
-# Run cmake
-cmake ..
-
-# Compile files
-make -j
+bash build.sh # run build script
 
 # Change back to root directory
 cd .. || exit

benchmarks/generate_plots.py

@@ -11,9 +11,8 @@
 def read_metrics(filename):
     return pd.read_csv(filename)
 
-def scatter_algos(data, output_file):
-
 
+def scatter_algos(data, output_file):
     # Create a figure for subplots
     fig, axs = plt.subplots(1, len(data["algo"].unique()), figsize=(15, 15))
     fig.tight_layout(pad=2.0)
@@ -22,13 +21,13 @@ def scatter_algos(data, output_file):
     for i, category in enumerate(data["algo"].unique()):
         ax = axs[i]
         for type_ in data["options"].unique():
-        
+
             # Filter the data based on category and type
             filtered_data = data[(data["options"] == type_) & (data["algo"] == category)]
-        
+
             # Plot the scatter plot for this category and type
             ax.scatter(filtered_data['threads'], filtered_data['time'], label=f'{type_[1:]}')
-        
+
             # Set labels and title
             ax.set_xlabel('Threads')
             ax.set_ylabel('Time')
@@ -39,16 +38,17 @@ def scatter_algos(data, output_file):
     plt.savefig(output_file)
     plt.close(fig)
 
+
 def scatter_options(data, output_file):
     fig, axs = plt.subplots(1, len(data["options"].unique()), figsize=(15, 15))
     fig.tight_layout(pad=2.0)
     for i, type_ in enumerate(data["options"].unique()):
         ax = axs[i]
         for category in data["algo"].unique():
-        
+
             # Filter the data based on category and type
             filtered_data = data[(data["options"] == type_) & (data["algo"] == category)]
-        
+
             # Plot the scatter plot for this category and type
             ax.scatter(filtered_data['threads'], filtered_data['time'], label=f'{category}')
 
@@ -61,7 +61,7 @@ def scatter_options(data, output_file):
 
     plt.savefig(output_file)
     plt.close(fig)
-    
+
 
 def boxplots(data, output_file):
     num_threads = data["threads"].unique()
@@ -71,7 +71,7 @@ def boxplots(data, output_file):
     fig.tight_layout(pad=5.0)
     for x, algo in enumerate(algos):
         for y, option in enumerate(options):
-            ax = axs[x,y]
+            ax = axs[x, y]
             filtered_data = []
             for num_thread in num_threads:
                 filtered_data.append(data[(data["algo"] == algo) & (data["options"] == option) & (data["threads"] == num_thread)]["time"])
@@ -86,7 +86,6 @@ def boxplots(data, output_file):
     plt.close(fig)
 
 
-
 # Plot performance vs threads
 def plot_performance(data, output_file):
     plt.figure(figsize=(10, 6))
@@ -179,8 +178,7 @@ def plot_efficiency(data, output_file):
     if not os.path.exists(args.output_dir):
         os.makedirs(args.output_dir)
 
-
-    data[['algo', 'options']] =  data['file'].str.rsplit('_', n=1, expand=True)
+    data[['algo', 'options']] = data['file'].str.rsplit('_', n=1, expand=True)
 
     # Create graphs
     plot_performance(

build.sh

@@ -0,0 +1,28 @@
+#!/bin/bash
+set -e
+
+# Script to build the project
+
+source /opt/intel/oneapi/setvars.sh
+
+# Check if script is run from root directory
+if [ ! -f "main.cpp" ]; then
+    echo "Please run this script from the root directory"
+    exit 1
+fi
+
+# Check if build directory exists
+if [ -d "build" ]; then
+    rm -r build
+fi
+
+mkdir build
+
+# Change to build directory
+cd build || exit
+
+# Run cmake
+cmake ..
+
+# Compile files
+make -j