[PATCH] D99683: [HIP] Support ThinLTO

Tue Apr 6 14:48:47 PDT 2021

yaxunl added a comment.

In D99683#2672554 <https://reviews.llvm.org/D99683#2672554>, @tejohnson wrote:

> This raises some higher level questions for me:
>
> First, how will you deal with other corner cases that won't or cannot be imported right now? While enabling importing of noinline functions and cranking up the threshold will get the majority of functions imported, there are cases that we still won't import (functions/vars that are interposable, certain funcs/vars that cannot be renamed, most non-const variables with non-trivial initializers).

We will document the limitation of thinLTO support of HIP toolchain and recommend users not to use thinLTO in those corner cases.

> Second, force importing of everything transitively referenced defeats the purpose of ThinLTO and would probably make it worse than regular LTO. The main entry module will need to import everything transitively referenced from there, so everything not dead in the binary, which should make that module post importing equivalent to a regular LTO module. In addition, every other module needs to transitively import everything referenced from those modules, making them very large depending on how many leaf vs non-leaf functions and variables they contain. What is the goal of doing ThinLTO in this case?

The objective is to improve optimization/codegen time by using multi-threads of thinLTO. For example, I have 10 modules each containing a kernel. In full LTO linking, I get one big module containing 10 kernels with all functions inlined, and I have one thread for optimization/codegen. With thinLTO, I get one kernel in each module, with all functions inlined. AMDGPU internalization and global DCE will remove functions not used by that kernel in each module. I will get 10 threads, each doing optimization/codegen for one kernel. Theoretically, there could be 10 times speed up.

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