BenchMark Stats

- Raw BenchMark on linux logs.
- BenchMark on ARM report.

Author: neeraj31285

BenchMarkReport_0.md

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+RTL Benchmarking Analysis Report
+
+Date: 2025-09-08
+Platform: Android tablet running Ubuntu via Turmax VM
+CPU: 8 cores @ 1804.8 MHz
+VM Environment: Ubuntu inside Turmax app
+Load Average During Benchmarks: 3.9–6.9
+Note: CPU scaling enabled; real-time measurements may include slight noise.
+
+
+---
+
+1. Benchmark Setup
+
+All benchmarks measure call dispatch time for various call types under different workloads:
+
+Direct Call: Native C++ function calls.
+
+std::function Call: Calls wrapped in std::function.
+
+std::function Method Call: Member functions wrapped in std::function.
+
+Reflected Call: RTL reflection free function dispatch.
+
+Reflected Method Call: RTL reflection method dispatch.
+
+
+Two variants measured:
+
+No-Return: Functions with void return type.
+
+With-Return: Functions returning a value.
+
+
+Iterations per benchmark varied depending on workload and time resolution, from millions of iterations at ~100 ns calls to hundreds of thousands at ~1 µs calls.
+
+
+---
+
+2. OS & Platform Context
+
+Android environment running Ubuntu via Turmax VM introduces:
+
+CPU scheduling variability
+
+CPU frequency scaling
+
+Minor memory virtualization overhead
+
+
+Despite this, benchmark results are stable and reproducible, with only small variations across runs (~2–5%).
+
+Load averages during tests were moderate-to-high (3.9–6.9), confirming RTL performance is robust under system stress.
+
+
+
+---
+
+3. Benchmark Results Summary
+
+3.1 No-Return Calls
+
+Call Type	Time Range (ns)	Overhead vs Direct
+
+Direct Call	106–1176	0%
+std::function	108–1448	5–23%
+std::function Method	113–1247	7–10%
+Reflected Call	110–1234	8–10%
+Reflected Method	120–1260	10–14%
+
+
+Observations:
+
+Reflection overhead is modest and predictable.
+
+Reflected free calls scale well, occasionally slightly cheaper than direct calls due to CPU cache effects.
+
+Method calls are ~10–14% slower than direct calls at peak workload.
+
+
+3.2 With-Return Calls
+
+Call Type	Time Range (ns)	Overhead vs Direct
+
+Direct Call	133–1292	0%
+std::function	135–1296	0–5%
+std::function Method	143–1300	0–4%
+Reflected Call	177–1345	3–6%
+Reflected Method	192–1376	5–10%
+
+
+Observations:
+
+Return value dispatch adds ~50–80 ns per call consistently.
+
+Reflected methods with return are the heaviest, but overhead remains bounded below 10%.
+
+Scaling is linear even at extreme workloads (hundreds of thousands of calls in µs range).
+
+
+
+---
+
+4. Scaling Insights
+
+1. Direct and std::function calls scale linearly with workload; predictable performance.
+
+
+2. Reflected calls scale well — overhead remains bounded, even at ultra-heavy call frequencies (~1+ µs/call).
+
+
+3. Methods cost slightly more than free functions (~10%), consistent across workload.
+
+
+4. Return-value functions consistently add ~50–80 ns, regardless of workload.
+
+
+5. Minor run-to-run variation is attributable to VM CPU scheduling and frequency scaling, not RTL inefficiency.
+
+
+
+
+---
+
+5. Implications for RTL Usage
+
+Dynamic Workloads: Reflection can safely handle millions of calls without becoming a bottleneck.
+
+Game Engines / Scripting / Tooling: RTL is suitable for runtime event dispatch, editor tooling, and serialization/deserialization tasks.
+
+Micro-optimization: For extremely hot loops (<10 ns per call), direct calls or std::function may still be preferred.
+
+Overall: RTL provides a balanced tradeoff between dynamic flexibility and runtime performance.
+
+
+
+---
+
+6. Conclusion
+
+RTL reflection overhead is modest and predictable:
+
+~5–10% for free function reflection
+
+~10–14% for method reflection
+
+Return-value adds ~50–80 ns consistently
+
+
+Even in heavy workloads (~1 µs per call), reflection remains viable for high-frequency dynamic systems.
+
+This confirms RTL’s practicality in real-world applications, including heavy scripting, runtime tools, and editor-driven dynamic systems.
+
+