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Comment by disdi | Hacker News Reader

Amazing work. What changes are needed in this library to make it benchmark GPUs ?

No changes are required in the library -- you just need to have some way of generating the code for the forward pass using e.g. cuDNN (which already has a heuristic selector!)

In the compiler we need to add backends to cuDNN and probably some OpenCL DNN framework -- this is already fairly high on our list of priorities.

I am really not able to connect the dots here. Bear with me for my ignorance since I am from OS/drivers background and not a compiler guy.

Can I compile code at below link with nvcc(Nvidia compiler) - https://bitbucket.org/STG-TCD/trinnity/src/b33c94e1fabd18f83...

If I write gemm.cl like above program implementing a bunch of CNN primitives, why I cannot compile it with gcc.

For compiling any Opencl program, I need to write kernel in suppose sample.cl file and compile it with gcc –Wall sample.cl –o sample -lOpenCL

This -lOpenCL picks up libopencl.so file from my Hardware vendor(Qcom, Intel) and generates the binary which runs it on GPU(or whereever Opencl is available)

Why do I need anything trinity compiler and optimizer.

The short answer is that there are dozens of ways to use that GEMM to do convolution (convolution algorithms), and there are NUM_ALGORITHMS * NUM_LAYERS way to implement the network.

Our toolkit figures out which of those arrangements are the fast ones!

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