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Added documentation on the convgemm routine

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Cedric Nugteren 2019-01-19 15:44:19 +01:00
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README.md
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@ -79,6 +79,7 @@ More detailed documentation is available in separate files:
* [Testing the library for correctness](doc/testing.md)
* [Bindings / wrappers for other languages](doc/bindings.md)
* [More details on the GEMM kernel](doc/details_gemm.md)
* [More details on the convolution implementation](doc/details_conv.md)
* [Glossary with some terms explained](doc/glossary.md)
* [Frequently asked questions (FAQ) and their answers](doc/faq.md)

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ROADMAP.md
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@ -20,6 +20,6 @@ This file gives an overview of the main features planned for addition to CLBlast
| [#228](https://github.com/CNugteren/CLBlast/issues/228) | Mar-Apr '18 | CNugteren | ✔ | Improving performance for Qualcomm Adreno GPUs |
| [#270](https://github.com/CNugteren/CLBlast/issues/270) | Oct '18 | CNugteren | ✔ | Implement col2im |
| - | ?? | CNugteren | | Add support for OpenCL image buffers |
| [#267](https://github.com/CNugteren/CLBlast/issues/267) | ?? | CNugteren | WIP | Merge im2col and GEMM into a direct kernel |
| [#267](https://github.com/CNugteren/CLBlast/issues/267) | Jan '19 | vbkaisetsu| ✔ | Merge im2col and GEMM into a direct kernel |
| [#136](https://github.com/CNugteren/CLBlast/issues/136) | ?? | CNugteren | | Implement xAXPBY and xSET |
| [#169](https://github.com/CNugteren/CLBlast/issues/169) | ?? | dividiti | | Problem-specific tuning parameter selection |

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doc/details_conv.md 100644
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CLBlast: Details on the CONVGEMM routine
================
This document gives a bit more detail on how the CONVGEMM routine is organised and implemented. For other information about CLBlast, see the [main README](../README.md).
CONVGEMM: Two approaches
-------------
CLBlast implements two approaches to batched convolutions using GEMM: through im2col, or stand-alone:
* `ConvGemmMethod::kWithIm2Col`: running first a batched version of im2col to prepare the data into a temporary buffer, and then running a batched version of GEMM. The implementation is just as the regular im2col and GEMM kernels in CLBlast, but it is implemented as a separate kernel so all the non-needed features can be stripped out and some optimizations can be made. It uses the tuning parameters of the regular im2col and GEMM kernels.
* `ConvGemmMethod::kSingleKernel`: this is a single kernel approach: it loads the data in such a way that the im2col kernel is no longer needed, i.e. loading the data as the im2col transformation does it. That way it becomes a single kernel and there will be no need for an intermediate large buffer. It uses a separate set of tuning parameters, and can be tuned using the `clblast_tuner_xconvgemm` binary.
CONVGEMM: Selecting which approach to use
-------------
Since CONVGEMM is a relatively new and experimental feature, selection of the approach is hard-coded in [xconvgemm.hpp on line 32](../src/routines/levelx/xconvgemm.hpp:32), but can be changed there in a single place.
The main drawback of the `ConvGemmMethod::kWithIm2Col` approach is its extra memory usage, but depending on the device and setting, it might be faster compared to the `ConvGemmMethod::kSingleKernel` approach. The latter has as extra advantage that it has its own tuning parameters, so it can be fine-tuned for your specific use-case a bit better than the 2-kernel approach with im2col.