Add benchmark results

I'm also removing "re-enable optimizations for primitive types" from the todo list because based on the benchmarks I'm not sure it's worth the effort.

README.md

@@ -49,8 +49,7 @@ Progress
 - [x] Support types that do not a have a trivial default constructor
 - [x] Support types that do not have *any* default constructor
 - [x] Support move only types
-- [ ] Re-enable optimizations for primitive types
-- [ ] Update benchmarks
+- [x] Update benchmarks
 
 License
 -------

bench/plot.py

@@ -0,0 +1,121 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from tqdm import tqdm
+import fileinput
+
+TEST_DISPLAY_NAMES = {
+    'ascending order' : 'ascending',
+    'descending order' : 'descending',
+    'random int' : 'random',
+    'random order' : 'random high bits',
+    'random' : 'crumsort (C)',
+    'quadsort' : 'quadsort (C)',
+    'skasort' : 'ska_sort'
+}
+
+EXCLUDE_TESTS = [ 'random double', 'random long' ]
+
+ALGORITHM_DISPLAY_NAMES = {
+    'sort' : 'std::sort',
+    'stablesort' : 'std::stable_sort',
+    'cxcrumsort' : 'crumsort (C++)',
+    'cxquadsort' : 'quadsort (C++)',
+    'crumsort' : 'crumsort (C)',
+    'quadsort' : 'quadsort (C)',
+    'skasort' : 'ska_sort'
+}
+
+EXCLUDE_ALGORITHMS = [ 'blitsort', 'fluxsort', 'gridsort' ]
+
+EXPECTED_X_VALUES = [ 10, 100, 1000, 10000, 100000 ]
+
+benchmark_results = {}
+
+for line in fileinput.input():
+
+    line = line.strip()
+
+    if len(line) > 0 and  line[0] == '|' and line[-1] == '|': # This line actually has a table row
+
+        # Parse the table row
+        cells = line.split('|')[1:-1]      # Throw out empty first and last elements
+        cells = [c.strip() for c in cells]
+        if cells[0] == 'Name' or len(cells[0]) == 0 or cells[0][0] == '-': # Throw out header rows
+            continue
+
+        # Extract the benchmark results
+        algorithm = cells[0]
+        array_len = int(cells[1])
+        time      = float(cells[4])
+        test_name = cells[-1]
+
+        # Store results in the global result table
+        benchmark_results.setdefault(test_name, {})
+        benchmark_results[test_name].setdefault(algorithm, {})
+        benchmark_results[test_name][algorithm][array_len] = time
+
+# Bar graphs
+for test in tqdm(benchmark_results):
+
+    if test in EXCLUDE_TESTS:
+        continue
+
+    test_name = TEST_DISPLAY_NAMES.get(test, test)
+
+    fig, ax = plt.subplots()
+
+    labels = []
+    values = []
+
+    for algorithm in sorted(benchmark_results[test].keys()):
+
+        if algorithm in EXCLUDE_ALGORITHMS:
+            continue
+
+        algorithm_name = ALGORITHM_DISPLAY_NAMES.get(algorithm, algorithm)
+
+        labels.append(algorithm_name)
+        values.append(benchmark_results[test][algorithm][10000])
+
+    y_pos = np.arange(len(labels))
+    plt.barh(y_pos, values, align='center')
+    plt.yticks(y_pos, labels)
+    plt.xlabel('run time (ms)')
+    plt.title(test_name + ' (10,000 elements)')
+    plt.tight_layout()
+
+    fig.savefig(test_name + " 10000.png")
+    plt.close()
+
+# Line/scaling graphs
+for test in tqdm(benchmark_results):
+
+    if test in EXCLUDE_TESTS:
+        continue
+
+    test_name = TEST_DISPLAY_NAMES.get(test, test)
+
+    fig, ax = plt.subplots()
+    ax.set_xscale('log')
+    ax.set_yscale('log')
+
+    ax.set(xlabel='array length', ylabel='run time (ms)', title=test)
+
+    for algorithm in sorted(benchmark_results[test].keys()):
+
+        if algorithm in EXCLUDE_ALGORITHMS:
+            continue
+
+        algorithm_name = ALGORITHM_DISPLAY_NAMES.get(algorithm, algorithm)
+
+        times = benchmark_results[test][algorithm]
+        array_lens = [key for key in times]
+        array_lens.sort()
+        datapoints = [times[array_len] for array_len in array_lens]
+        datapoints = np.array(datapoints)
+
+        ax.plot(array_lens, datapoints, label=algorithm_name)
+
+    ax.legend()
+    fig.savefig(test_name + ".png")
+    plt.close()