blackccpie / fastconv

fast 2D convolution implementation benchmark

fastconv

Reading this post from Pete Warden, which in short describes a way to convert a 3D tensor convolution operation into a 2D GEneral Matrix to Matrix Multiplication (GEMM), I wondered if this could apply to 2D matrix convolution.

This little project aims at verifying this hypothesis, using two different matrix implementations : one static allocation matrix container and one dynamic allocation matrix container, respectively based on std::array and std::vector private inheritance.

Static allocation matrix container

Int this case the compiler is aware of loop sizes and allocation sizes at compile time. Therefore in this case it seems hard to beat the standard implementation.

Testing this scenario required to increase the allowed stack size for the application, in order to have measures on big enough matrix (250Mb stack size to test on 1500x1500 matrix).

Dynamic allocation matrix container

Here the time spent to build the im2col/Toeplitz matrix is counterbalanced with the reduced time to operate the mulitply/add operations related to GEMM. The latter process can be efficiently vectorized using appropriate SIMD intrinsics.

First preliminary results show an average 20% gain in computation time compared to the standard implementation.

fastconv's Issues

Question on the project

What's the difference between the function 'convolve' and 'fast_convolve'? And where to use the function 'multiply'?
I thought the project is to show the efficiency of three convolution implementation 'convolve', 'fast_convolve' and im2col matrix 'multiply'.
Thank you!