[llvm-dev] Static Analysis for GPU Program Performance in LLVM

Nimit Singhania via llvm-dev llvm-dev at lists.llvm.org
Mon Oct 25 12:50:06 PDT 2021

Hi there,

I would like to propose addition of two static analyses to LLVM framework
that can help detect performance issues in GPU programs: The first analysis
directly detects the issue with memory congestion across GPU threads; the
second analysis checks independence for block-size for synchronization-free
programs that allows performance tuning of block-size without impacting
correctness. Both these static analyses were developed as part of my PhD
thesis and are available on github. Please see the link here to see more


We would like to upstream these analyses to LLVM. There are many advantages
of the work. These are ground-breaking analyses that allow light-weight
compile-time detection of performance and correctness issues in GPU
programs that concern *inter-thread *behavior. Being light-weight allows
them to operate efficiently at compile-time. Inter-thread behavior of the
program concerns the behaviors of the program that are observed due to the
interaction between threads and are not local to individual threads. Such
analysis is difficult to perform in a generic multi-threaded program,
however due to the regularity of GPU parallelism, the analyses are feasible
at compile-time.

These analyses can be the basis for optimizations that can improve the
performance of GPU programs multifold. Given the complexity of GPU
programming and the lack of support for tools in this space, the analyses
provide the first steps towards robust tools for analysis and optimization
of GPU programs. There are two publications that have been published for
this work, which can be found in the references below. I would be happy to
answer any questions or concerns regarding this work.


1. FMSD 2021: Static analysis for detecting uncoalesced accesses in GPU
programs, Rajeev Alur, Joseph Devietti, Omar Navarro Leija, and Nimit
2. SAS 2018: Block-Size Independence of GPU Programs, Rajeev Alur, Joseph
Devietti, and Nimit Singhania.
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