# Benchmarking Results

Kompass core algorithms are implemented in modern C++ for maximum performance and efficiency. Designed with real-time robotics in mind, it makes full use of **multi-threaded CPU execution** and **GPU acceleration** to parallelize compute-heavy tasks like planning, control and map updates.

The GPU support in Kompass is built using SYCL. Unlike other solutions that rely on vendor-specific GPU APIs (e.g. CUDA for Nvidia), Kompass is the first navigation framework to support cross-GPU acceleration. This means it can target any SYCL-compliant GPU, including those from Nvidia, AMD, Intel, and others—without requiring device-specific modifications.

The following benchmarks compare the execution time and power efficiency of key navigation components—**Motion Planning**, **Mapping**, and **Safety Checks**—running on standard CPUs versus accelerated backends (NVIDIA CUDA and AMD ROCm).


## 📊 Performance Charts

### 1. Speed Comparison (Logarithmic Scale)

The following chart compares the execution time of critical components.
* **Lower is better.**
* **Note the Log Scale:** The y-axis is logarithmic, meaning differences between bars represent orders of magnitude in speedup.


```{figure} https://raw.githubusercontent.com/automatika-robotics/kompass-core/main/docs/benchmark_log_dark.png
:class: dark-only
:alt: Logarithmic Benchmark Results
:align: center

```

```{figure} https://raw.githubusercontent.com/automatika-robotics/kompass-core/main/docs/benchmark_log_light.png
:class: light-only
:alt: Logarithmic Benchmark Results
:align: center

Logarithmic Benchmark Results
```

> _Note: This chart excludes runs where power monitoring was active to ensure timing accuracy._

### 2. Power Consumption & Efficiency

This chart highlights the energy efficiency of the accelerators.
* **Efficiency Metric:** Operations per Joule (Throughput / Watts).
* **Higher is better.**


```{figure} https://raw.githubusercontent.com/automatika-robotics/kompass-core/main/docs/benchmark_power_dark.png
:class: dark-only
:alt: Linear Benchmark Results
:align: center

```

```{figure} https://raw.githubusercontent.com/automatika-robotics/kompass-core/main/docs/benchmark_power_light.png
:class: light-only
:alt: Linear Benchmark Results
:align: center

Linear Benchmark Results
```


## Benchmark Methodology

The benchmarks are conducted using the [`kompass-core`](https://github.com/automatika-robotics/kompass-core) benchmarking suite.

**Kompass Core** implements the core General-Purpose GPU (GPGPU) kernels of Kompass using SYCL. We utilize [**AdaptiveCpp**](https://github.com/AdaptiveCpp/AdaptiveCpp) for JIT compilation, allowing a "single-source-multiple-target" codebase that can be compiled for CPUs, NVIDIA GPUs, and AMD GPUs.

### Evaluated Components

We benchmark three computationally intensive components of the navigation stack:

#### 1. Cost Evaluator (Motion Planning)
* **Task:** Evaluates 5,000 generated trajectories ($10ms$ time step, $10s$ horizon).
* **Complexity:** Calculates costs based on reference path deviation, smoothness constraints, and obstacle proximity.
* **Stress Factor:** Massive parallel trajectory rollout and reduction.

#### 2. Local Mapper (Occupancy Grid)
* **Task:** Raycasts a dense LiDAR scan ($3,600$ points) into a $400 \times 400$ grid ($20m \times 20m$ @ $5cm$ resolution).
* **Complexity:** Updates occupancy probabilities for the entire grid using ray tracing.
* **Stress Factor:** Random memory access patterns and heavy ray traversal.

#### 3. Critical Zone Checker (Safety System)
* **Scenario A (PointCloud):** Checks a dense 3D point cloud ($100,000$ points) against the robot's safety footprint.
* **Scenario B (LaserScan):** Checks a high-res 2D laser scan ($3,600$ rays) against a "slowdown" zone.
* **Metric:** Latency to return a safety factor $\in [0.0, 1.0]$.
* **Note:** Scenario B is lightweight and highly optimized on CPU; GPU acceleration is typically reserved for the denser PointCloud scenario.


```{seealso}
Check how to run or reproduce these benchmarks on your own hardware using the standalone `kompass_benchmark` executable [here](https://github.com/automatika-robotics/kompass-core/blob/main/src/kompass_cpp/benchmarks/README.md)
```