CLBlast/doc/details_conv.md

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.

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, 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.