IdeaWell

Project ideas from Hacker News discussions.

Compiling models to megakernels

Original Article

Hacker News Discussion

📝 Discussion Summary (Click to expand)

3 Most Prevalent Themes

1. Debate on the Scope of AI Optimizations

Users debated whether AI optimization techniques fall under universal computer science principles or are a distinct category. One user claimed AI researchers have only discovered inlining and caching, while others argued for unique, performance-focused optimizations specific to AI.

  • "There are only 4 optimizations in computer science: inlining, partial evaluation, dead code elimination, & caching. It looks like AI researchers just discovered inlining & they already knew about caching so eventually they'll get to partial evaluation & dead code elimination." — measurablefunc
  • "AI actually has some optimizations unique to the field. You can in fact optimize a model to make it work; not a lot of other disciplines put as much emphasis on this as AI" — mxkopy
  • "RLHF is one that comes to mind" — mxkopy

2. Classification of Algorithmic and Mathematical Optimizations

Discussion centered on where algorithmic optimizations (e.g., FFT, Strassen algorithm) fit within the proposed universal categories, with some arguing they are instances of caching or common subexpression evaluation.

  • "Which categories do algorithmic optimizations fall under? For example: Strassen algorithm for matrix multiplication, FFT convolution, Winograd convolution..." — johndough
  • "FFT is the classic case of common subexpression evaluation (its mathematically equivalent), which I think by OPs definition would fall under caching." — torginus

3. Practical Feasibility of Applying Compiler Optimizations to AI

Several users questioned the practical utility of applying classical optimizations like partial evaluation and dead code elimination to neural networks, citing technical hurdles like activation functions and fixed architectures.

  • "You can't do any partial evaluation of a neural network because the activation functions are interrupting the multiplication of tensors." — imtringued
  • "Dead code elimination is even more useless since most kernels are special purpose to begin with and you can't remove tensors without altering the architecture." — imtringued
  • "I think you can. If you have a neuron whose input weights are 100,-1,2, with threshold 0, you can know the output of the neuron if the first input is enabled... so you can skip evaluating those." — torginus

🚀 Project Ideas

AutoModelOpt

Summary

  • A web‑based tool that automatically applies model‑level optimizations (pruning, quantization, low‑rank factorization, dead‑code elimination) to any trained neural network and outputs an inference‑ready graph for CUDA, TensorRT, or ONNX Runtime.
  • Gives practitioners instant, high‑quality inference speedups without manual tuning.

Details

Key Value
Target Audience ML engineers, researchers, and hobbyists who need fast inference on GPUs or edge devices
Core Feature Automatic analysis & transformation of model graphs, visual diff of removed/merged ops, export to popular runtimes
Tech Stack Python, ONNX, PyTorch, TensorRT, CUDA, WebAssembly, React for UI
Difficulty Medium
Monetization Revenue‑ready: tiered subscription for enterprise features

Notes

  • HN commenters lament the lack of “low‑overhead” GPU optimizations: “implementing them on GPUs in a low‑overhead manner that maintains the model's fidelity is challenging.” AutoModelOpt removes that friction.
  • Enables quick experimentation with pruning, quantization, and dead‑code elimination, directly addressing the frustration that “dead code elimination is even more useless” for many models.

GPU Kernel Fusion Compiler

Summary

  • A search‑based compiler that fuses pre‑defined and code‑generated GPU kernels, performs register allocation, vectorization, and scheduling, and outputs highly optimized CUDA kernels for inference.
  • Empowers developers to achieve state‑of‑the‑art performance without writing low‑level CUDA code.

Details

Key Value
Target Audience GPU‑centric ML practitioners, compiler researchers, and performance engineers
Core Feature Unified search space for block/warp/thread ops, automatic kernel fusion, profiling‑guided specialization
Tech Stack LLVM, CUDA Toolkit, Python API, Rust for core, Web UI for visualizing fusion plans
Difficulty High
Monetization Revenue‑ready: per‑job licensing for large models

Notes

  • Addresses the pain point raised by “implementing them on GPUs in a low‑overhead manner that maintains the model's fidelity is challenging.” The compiler automates the complex fusion and scheduling decisions.
  • Provides a playground for researchers to test ideas like “fused kernels (goes beyond inlining)” and “speculative decoding” in a production‑ready setting.

Optimization Benchmark Hub

Summary

  • A curated, open‑source library of algorithmic optimizations (Strassen, FFT, Winograd, low‑rank adapters, sparsity, etc.) with a benchmarking suite that evaluates trade‑offs across architectures and hardware.
  • Gives researchers a quick way to compare and adopt optimizations without reinventing the wheel.

Details

Key Value
Target Audience ML researchers, algorithm designers, and performance analysts
Core Feature Modular optimization modules, automated benchmarking pipelines, leaderboard of speed/accuracy trade‑offs
Tech Stack Python, PyTorch, JAX, Docker, CI/CD, GitHub Actions, Grafana dashboards
Difficulty Medium
Monetization Hobby (open source)

Notes

  • Responds to “If somebody likes broad categories here is good one: '1s and 0s' …” by providing concrete, reusable modules.
  • Enables quick experimentation with “vectorization, register allocation, scheduling, lock elision, better algorithms, compression, quantization, fused kernels, low‑rank adapters, sparsity, speculative decoding, parallel/multi‑token decoding, better sampling, prefill/decode separation” as listed by mirekrusin.
  • Sparks discussion on which optimizations truly deliver gains in practice.

Read Later