Exploring LLVM's Role in FPS and Power Consumption Optimizations for GPUs

Code Generation AMDGPU
1

Hello LLVM community,

I want to ask a simple question related to the optimization of GPU applications using LLVM, and I just want to know about how compiler can influence the FPS and power efficiency of the GPU . Here are some points I’d like to discuss:

  1. Memory Access Patterns: How can optimizing memory access in LLVM contribute to better performance and lower power consumption?
  2. Compiler Optimizations: What LLVM flags or techniques have you found most effective for enhancing GPU performance? Do you think enabling fast math optimizations significantly impacts FPS?
  3. Group Size and Kernel Launch Configurations: How should I determine the optimal group and block sizes for kernels to maximize performance and minimize power usage? Does compiler even take care of it?
  4. Profiling Tools: Which profiling tools do you recommend for identifying bottlenecks and optimizing power efficiency in GPU applications?
  5. Power Management Techniques: Are there specific LLVM features or techniques that can help manage power consumption while maintaining performance?

I’m aware that some believe LLVM primarily targets CPU optimizations, but I believe it also has a crucial role in GPU optimizations, especially in terms of generating efficient code and improving resource utilization.

I would appreciate any insights, experiences, or references to resources that could shed light on these topics!

As per my understanding

Indirect Influence on FPS

  • Even though LLVM is not directly involved in managing FPS but indirectly it can play a crucial role in FPS:
    • By Creating Optimized Code: By optimizing memory access patterns, instruction scheduling, and register allocation, LLVM can improve the runtime efficiency of GPU kernels.
    • Apply Compiler Optimizations: Flags like -O3, -ffast-math, and others can enhance performance, thereby enabling more computations to be done in the same timeframe, potentially increasing FPS.
    • Facilitate Better Resource Utilization: Efficient code generation can lead to better utilization of GPU resources (e.g., shared memory, compute cores), impacting overall throughput and responsiveness of applications
    • Some other optimizations such as vectorization and other things can also play a role in FPS.

Please correct me if I m wrong .
Thank you!

2

These two are mostly beyond the compiler’s ability. They both are ABI the compiler has to respect. You can inform the compiler about expected / required launch bounds as optimization hints, but it cannot make them up.

3

@arsenm
Thanks for the reply

Regarding GroupSize and Kernel Launch Configuration

Developer can use pragmas or attributes in code to provide hints (like expected block sizes) to guide the compiler for better scheduling, though final control lies with the application logic and GPU runtime.

  • llvm.amdgcn.dispatch.ptr: AMDGPU-specific intrinsic that allows kernel dispatch pointers to carry bounds information, guiding LLVM to better schedule wavefronts and minimize resource conflicts.
    User Guide for AMDGPU Backend — LLVM 20.0.0git documentation

  • #pragma unroll or launch_bounds attributes: Developers can inform LLVM about preferred kernel bounds or unroll hints, giving the compiler more room to optimize instruction scheduling and resource allocation.

Memory Access Pattern:

  • LLVM optimizations ensure that adjacent threads access adjacent memory locations (e.g., global memory). By structuring loads/stores in a predictable, coalesced way, the compiler helps minimize uncoalesced accesses and reduces memory stalls.
  • This leads to lower memory latency and fewer idle cycles, thereby improving both FPS and power efficiency by keeping GPU cores active.

@arsenm Correct me if I m wrong here

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That’s not really in the realm of what LLVM can do. The code representation is too low-level for that.

If you’re talking about some higher-level compilation pipeline, like the ones folks are implementing with MLIR, then yes, those might be able to do such optimizations. It depends a lot on whether the input languages is designed in a way that represents memory accesses at a sufficiently high level.