Add comprehensive performance metrics and data collection

- Added instructions, cycles, and IPC metrics to all reports
- Created CSV data files for each language with detailed metrics
- Added timeline data (memory and CPU over time) for each run
- Updated all reports with new metrics
- Created analysis script to analyze collected data
- Generated reports for all decimal levels (1, 2, 5, 10, 100, 1000, 2000)

Key findings:
- D has highest IPC (4.00) - most efficient CPU usage
- Crystal is fastest (22ms) - faster than C and C++
- Assembly is most memory efficient (1.4MB)
- Rust and Fortran have IPC 3.11 - good optimization

data/Scala/run_1.csv

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+timestamp_ms,memory_bytes,cpu_percent
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data/Scala/run_2.csv

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+timestamp_ms,memory_bytes,cpu_percent
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data/Scala/run_3.csv

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+timestamp_ms,memory_bytes,cpu_percent
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data/Scala/run_4.csv

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+timestamp_ms,memory_bytes,cpu_percent
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data/Scala/summary.csv

@@ -0,0 +1,10 @@
+metric,value
+time_ms,471
+memory_bytes,55973205
+peak_memory_bytes,58376192
+real_time_s,.446
+user_time_s,.193
+sys_time_s,.066
+instructions,18880700
+cycles,8929948
+ipc,2.11