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https://github.com/zintix-labs/problab

Problab is a high-performance slot math engine for math designers and engineers. Build once, then simulate, reproduce, and ship to production with the same source of truth.
https://github.com/zintix-labs/problab

go golang math simulation slot slot-machine

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Problab is a high-performance slot math engine for math designers and engineers. Build once, then simulate, reproduce, and ship to production with the same source of truth.

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README 

          
🌐 Language: En | [中文](README_ch.md)



---



# Problab



Maintained by Zintix Labs — @nextso



**Problab** is a high-performance slot math engine for math designers and engineers.



Build once, then **simulate, reproduce, and ship to production**  

with the **same source of truth**.



**Simulation runs the same execution path as production.**



---



## What is Problab?



**If it runs in simulation,**  

**it runs the same way in production.**



Problab is a **slot math execution engine** designed for both

large-scale simulation and real production use.



It is the **same engine** that can be used for:



- large-scale math simulation

- deterministic reproduction (seed-based)

- backend game server execution

- development & debugging



There is **no separate “simulation logic” and “production logic”**.



---



## Why Problab?



Slot math engines usually fail in one of these ways:



- Simulators are fast but **not production-ready**

- Production engines are correct but **too slow to simulate**

- Simulation logic and server logic **diverge over time**

- Reproducing a real production issue is painful or impossible



Problab is designed to solve this exact problem.



---



## Core Design Goals



- **Single Source of Truth**  

  One engine, one logic path, one result.



- **High Performance by Design**  

  Zero-allocation hot paths, cache-friendly data layout.



- **Explicit Dependency Injection**  

  No hidden globals, no magic init side effects.



- **Developer-Friendly**  

  Add a new game by providing:

  - one config file

  - one logic file



- **Same execution path in simulation and production**  

  Simulation is not a mock or a reimplementation.  

  It runs the same deterministic logic path as production.



---



## Performance Snapshot (MacBook Air M3)



All numbers below are **real measurements**, not synthetic benchmarks.



### Single Core



| Game Type | Throughput |

|----------|------------|

| Simple (5x3, 15 lines) | ~7.0M spins/sec |

| Cascade / Cluster | ~1.7M spins/sec |



### 4-Core Parallel Execution



| Game Type | Throughput |

|----------|------------|

| Simple | ~19M spins/sec |

| Cascade / Cluster | ~5.8M spins/sec |



### Real Run Output (Example)



```bash

make run w=4 r=25000000

```



```text

[WORKERS:4] [GAME:demo_normal] [PLAYMODE:0] [SPINS:100,000,000]

used: 5.26 seconds                                                                                                                      

sps : 19,010,181 spins/sec

+--------------------------------+

|          demo_normal           |

+--------------+-----------------+

| Game Name    | demo_normal     |

| Game ID      | 0               |

| Total Rounds | 100,000,000     |

| Total RTP    | 95.56 %         |

| RTP 95% CI   | [95.42%,95.69%] |

| Total Bet    | 4,000,000,000   |

| Total Win    | 3,822,201,660   |

| Base Win     | 2,439,779,100   |

| Free Win     | 1,382,422,560   |

| NoWin Rounds | 71,034,259      |

| Trigger      | 835,933         |

| STD          | 6.797           |

| CV           | 7.113           |

+--------------+-----------------+



```



### Built-in Parameter Tuning (Optimizer)



Problab also includes an early-stage **optimizer toolchain** for math workflows:



- Collects discrete win samples from the same execution path

- Generates candidate shapes with configurable generators and filters

- Outputs reusable **optimal artifacts** for runtime sampling / deterministic replay



The goal is to close the loop of **simulate → verify → tune → reproduce** on a single engine.



> Note: this module is still evolving and APIs/configs may change before v1.0.0.



---



## Typical Use Cases



- Slot math simulation & validation

- RTP / volatility analysis

- Deterministic replay & debugging

- Backend game server execution

- CI regression testing for math changes



---



## Built-in Math Reports (Stats & Verification)



Problab ships with first-class **math verification reports** for validation and regression.



Out of the box you can generate:



- **RTP + 95% CI**

- **STD / CV (volatility)**

- **Hit / No-Win / Trigger rates**

- **Win distribution buckets** (base / free / total)

- **Recorder-style summaries** for audit/debug workflows



This is designed for practical math workflows:  

**validate → compare → regress → explain** with reproducible inputs.



Problab treats **verification output** as a first-class product feature, not an afterthought.



**Example Output**



```

Total RTP: 95.56%

RTP 95% CI: [95.42%, 95.69%]

STD: 6.797

CV:  7.113

...

```



---



## Quick Start 



**Successful execution in 1 minute; production environment ready in 3 minutes.**



This repository focuses on the engine itself.

For building real games, start with the scaffold.



Use **problab-scaffold** — a clean starter template built on top of Problab.



👉 https://github.com/zintix-labs/problab-scaffold



The scaffold provides:

- pre-wired configs / logic / server / simulation

- one-command run (`make run`, `make dev`, `make svr`)

- a structure ready for private commercial development



This is the **recommended way** to build real games with Problab.



---



## Determinism & Reproducibility



- Seed-driven execution

- Replayable results

- Identical behavior between simulation and server



This makes Problab suitable for:



- math audits

- regression testing

- production issue investigation



---



## Status



- APIs may evolve befor v1.0.0

- Focus is on correctness, performance, and core architecture



Documentation, tests, and starter templates will be expanded iteratively.



---



## Roadmap



- Jackpot support (settings + snapshot/delta)

- More shared ops

- (RFC) Trigger methods for state transitions



---



## Contributing



For v0.x.y, we **only accept**:



- Bug reports (with minimal repro / logs if possible)

- Documentation improvements (fixes, examples, translations)



Feature requests are welcome for discussion, but may not be prioritized yet.



---



## License



Apache License 2.0  

See [LICENSE](LICENSE) and [NOTICE](NOTICE).