Add comprehensive performance scaling data

- Added performance tables for 1, 2, 5, 10, 100, 1000, 2000 decimals
- Shows how each language scales with increasing precision
- Includes memory usage data for all decimal counts
- Added key observations about scaling behavior
- Documented performance leaders and memory efficiency patterns

parse_data.py

@@ -0,0 +1,33 @@
+#!/usr/bin/env python3
+import csv
+
+# Read the performance data
+data = {}
+with open('performance_data.csv', 'r') as f:
+    reader = csv.reader(f)
+    next(reader)  # Skip header
+    for row in reader:
+        if len(row) >= 5:
+            decimals = row[0]
+            # Skip rows where language name is missing or is just "SUCCESS"
+            if row[1] and row[1] not in ['SUCCESS', 'Brainfuck', 'Ruby'] and not row[1].startswith('3.'):
+                language = row[1]
+                time = row[2]
+                memory = row[3]
+                status = row[4]
+                
+                if decimals not in data:
+                    data[decimals] = []
+                data[decimals].append({
+                    'language': language,
+                    'time': time,
+                    'memory': memory,
+                    'status': status
+                })
+
+# Print summary
+for dec in ['1', '2', '5', '10', '100', '1000', '2000']:
+    if dec in data:
+        print(f"\n=== {dec} decimals ===")
+        for entry in data[dec][:10]:  # Show first 10
+            print(f"{entry['language']}: {entry['time']}ms, {entry['memory']}")