IdeaWell

Project ideas from Hacker News discussions.

Async/Await on the GPU

Original Article

Hacker News Discussion

📝 Discussion Summary (Click to expand)

Three prevailing themes in the discussion

# Theme Representative quotes
1 Ergonomic promise vs. performance uncertainty • LegNeato: “The anticipated benefits are similar to the benefits of async/await on CPU: better ergonomics for the developer writing concurrent code, better utilization of shared/limited resources, fewer concurrency bugs.”
• GZGavinZhao: “One concern I have is that this async/await approach is not ‘AOT’-enough like the Triton approach… Do you anticipate that there will be measurable performance difference?”
2 Architecture‑specific constraints (warps, memory, SIMD) • zozbot234: “I’m not quite seeing the real benefit… requires keeping the async function’s state in GPU‑wide shared memory, which is generally a scarce resource.”
• LegNeato: “GPU‑wide memory is not quite as scarce on datacenter cards… local executors with local futures that are !Send can be placed in a faster address space.”
• firefly2000: “Is this Nvidia‑only or does it work on other architectures?”
• LegNeato: “Currently NVIDIA‑only, we’re cooking up some Vulkan stuff in rust‑gpu though.”
3 Ecosystem impact and adoption concerns • the__alchemist: “I am, bluntly, sick of Async taking over rust ecosystems… I see it as the biggest threat to Rust staying a useful tool.”
• xiphias2: “Training pipelines are full of data preparation… async‑await is needed for serving inference requests directly on the GPU for example.”

These three threads capture the main points of debate: the potential developer‑friendly gains versus the lack of proven speedups, the technical hurdles tied to GPU hardware, and the broader worry that async/await could dominate Rust’s GPU ecosystem.

🚀 Project Ideas

Generating project ideas…

WarpAsync

Summary

  • A Rust async runtime that abstracts warp size, SIMD width, and supports heterogenous workloads across NVIDIA and AMD GPUs.
  • Provides ergonomic async/await syntax for GPU kernels, cross‑architecture compatibility, and a warp‑aware scheduler.

Details

Key Value
Target Audience Rust GPU developers, ML engineers, HPC researchers
Core Feature Async runtime + warp‑aware scheduler + cross‑arch abstraction
Tech Stack Rust, rust‑gpu, wgpu, CUDA, ROCm, Vulkan, LLVM
Difficulty High
Monetization Hobby

Notes

  • HN commenters ask “Is async/await on GPU useful?” and “How to handle warp size differences?” – WarpAsync answers both.
  • Sparks discussion on performance trade‑offs, AOT vs JIT, and cross‑architecture portability.

GPU Memory Advisor

Summary

  • A static‑analysis + runtime‑profiling tool that recommends tiling, GPU residency, or CPU residency for tensors in Rust ML pipelines.
  • Reduces unnecessary data transfers and optimizes memory usage.

Details

Key Value
Target Audience ML engineers, data scientists using Rust (Burn, Candle, etc.)
Core Feature Analysis engine + recommendation API + CLI integration
Tech Stack Rust, LLVM, MLIR, tracing, profiling crates, CLI framework
Difficulty Medium
Monetization Revenue‑ready: freemium (basic free, premium analytics)

Notes

  • Directly addresses “when to keep data on GPU” and “tiling vs. keeping tensors resident” concerns.
  • Encourages best‑practice sharing and could become a standard part of Rust ML workflows.

AsyncGPU Debugger

Summary

  • A visual debugger for GPU async code that displays warp execution, task scheduling, and memory usage in real time.
  • Enables stepping through async tasks and inspecting state machines.

Details

Key Value
Target Audience GPU developers, Rust developers, HPC programmers
Core Feature UI with warp timeline, breakpoints, state machine inspection
Tech Stack Rust, WebGPU, Electron, wgpu-profiler, wgpu debug layers
Difficulty High
Monetization Hobby

Notes

  • Addresses frustration with “debugging async GPU code” and “performance visibility”.
  • Provides a platform for community contributions and plugin extensions.

Read Later