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## Simple Benchmark

### Network Benchmark without batchnorm (F32/F16) in RTX 3080 Laptop GPU 150W

Network Code: test/benchmark.py

| F32/F16 | Spconv 1.x F32 (1080Ti) | Native| Implicit Gemm | Implicit Gemm Split Mask  |
| -------------- |:---------------------:|---------------------:|---------------------:| ---------------------:|
| Forward | 43ms     | 21.7ms/13.7ms    | 23.5ms/11.2ms      | 22ms/12.2ms      |
| Backward | 80ms    | 41.9ms/25.2ms    | 51.0ms/13.8ms      | 41.1ms/12.2ms      |

| F16 Forward | Native| Implicit Gemm | Implicit Gemm Split Mask  |
| -------------- |:---------------------:|---------------------:| ---------------------:|
| RTX 3080 Laptop 150W | 13.7ms     | 11.2ms    | 12.2ms      |
| RTX A6000 | 19.1ms    |  11.7ms   | 14.0ms      |
| TESLA V100 | 17.9ms    |  11.4ms   | 13.4ms      |

| F16 Backward | Native| Implicit Gemm | Implicit Gemm Split Mask  |
| -------------- |:---------------------:|---------------------:| ---------------------:|
| RTX 3080 Laptop 150W | 25.2ms     | 13.8ms    | 12.2ms      |
| RTX A6000       | 28.1ms     | 9.2ms     | 8.9ms      |
| TESLA V100 | 33.9ms    |  12.2ms   | 12.9ms      |

### Network Gemm Kernel Benchmark FP16 in RTX 3080 Laptop GPU

Network Code: test/benchmark.py

The network/input/profile code is same as above table.

This table only profile **fp16 gemm kernels** without output tensor create/clear overhead. this table show the performance upper bound of our algorithm.

| F16 |  Native| Implicit Gemm | Implicit Gemm Split Mask  |
| -------------- |:---------------------:|---------------------:| ---------------------:|
| Forward | 8.0ms    | 4.3ms      | 4.0ms      |

We can see that the implicit gemm is very fast, gemm only use 4.3ms/11.2ms in network forward. we can achieve better performance in TensorRT + Pure C++.

**NOTE** 
When you want to benchmark network in your laptop, don't forget to close all apps except terminals! Other apps will consume GPU resource and make kernels run slower.


## Comparsion with [MinkowskiEngine](https://github.com/NVIDIA/MinkowskiEngine) and [torchsparse](https://github.com/mit-han-lab/torchsparse)

TODO