[R&D_LABORATORY_BENCHMARKS]

R&D Lab & Performance Benchmarks

Research reports verifying CSnakes memory performance, VPS container memory constraints under resource limits, netplay rollback recovery budgets, and Opus voice packet metrics.

// REPOSITORY LANGUAGES

C# (.NET 8)~62%

Rust~24%

Python~8%

Dart / Flutter~6%

// SYSTEM SERVICES & DATABASES

Active Services16 Total

PostgreSQLCore Store

Redis CloudL1-L2 Cache

// EVENT INTERFACES

Message BrokerRabbitMQ

Command BusWolverineFx

WebSocketsSignalR / WS

// INTEGRATION & DEPLOYMENT

CI/CD AutomateGitHub Actions

VirtualizerDocker Compose

Platform targetsLinux / WASM

// CSNAKES_IN_PROCESS_BENCHMARK OPENS_GREEKS_CALCULATION

Evaluating the latency overhead of executing options pricing models via CSnakes (in-process CPython) vs. spawning external Python subprocesses or utilizing HTTP loopback microservices.

Subprocess Spawning (python.exe exec) 142.0 ms

HTTP Loopback Microservice (FastAPI REST) 2.4 ms

CSnakes In-Process C-API FFI 0.038 ms (38μs)

// CALIBRATION_FINDINGS

CSnakes completely bypasses the socket stack and serialization/deserialization cycles. Because the Python interpreter is initialized within the .NET process heap, function arguments are read directly from native memory pointers, reducing Options pricing calculation latency from milliseconds to microseconds.

// CONTAINER_RESOURCE_LIMITS_PROFILE 1024MB_VPS_BUDGET

Memory boundaries allocated to containerized services on budget hosting environments. These boundaries enforce strict garbage collection sweeps and connection limits.

Service Name	Base RAM	Max Limit	Tuning Strategy
rabbitmq:3.13	120 MB	180 MB	vm_memory_high_watermark = 0.2
postgresql:15	25 MB	40 MB	shared_buffers = 32MB, max_conn = 20
redis:7-alpine	22 MB	32 MB	maxmemory = 64MB, allkeys-lru
intelligence-pipeline	145 MB	220 MB	Workstation GC, CSnakes cache reuse
signalr-gateway	40 MB	60 MB	Concurrent socket channels pooled

// MEMORY_TUNING_LEVERAGE

By disabling server-mode Garbage Collection in .NET (ServerGarbageCollection = false), the runtime leaves garbage collection to workstation routines. This prevents the runtime from pre-allocating large segment heaps, keeping the container footprint at ~531 MB and avoiding Out-Of-Memory (OOM) killer terminations.

// ROLLBACK_FRAME_CATCHUP_PERFORMANCE 60HZ_FRAME_BUDGET_16.6MS

Rollback catch-up processing times when a late remote input is received and the engine must restore state and fast-forward to the current clock.

1 Frame Rollback Catch-up 0.45 ms

4 Frames Rollback Catch-up 1.82 ms

8 Frames Rollback Catch-up 3.65 ms

15 Frames Rollback Catch-up (Maximum Window) 6.92 ms

// ROLLBACK_ANALYSIS

Because catching up 15 frames takes only 6.92ms, the rollback calculations complete well within the 16.6ms frame budget (NTSC timing limit). This leaves ~9.6ms for PPU rendering and FFI memory sweeps, maintaining a stable 60 FPS even under simulated 150ms network jitter.

// OPUS_VOIP_COMPRESSION_RATIOS 16KHZ_MONO_VOICE_CAPTURE

Comparison of raw PCM audio data footprint vs. Opus codec compressed payloads streamed over the Iroh P2P netplay channels.

Raw PCM Buffer (20ms chunk at 16kHz) 640 Bytes

Opus Compressed VoIP Packet (Average) 42 Bytes (15.2x Compression)

// CODEC_ADVANTAGE

By compressing 640-byte raw PCM chunks down to 42 bytes using the Opus codec, the VoIP system consumes only ~16.8 Kbps of bandwidth. This low footprint prevents voice traffic from competing with latency-critical rollback input packets, protecting the netplay connection from bottlenecking.