TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters

https://arxiv.org/abs/2607.22432v1

Core Idea

TileSight addresses the problem that existing GPU performance tooling relies on coarse roofline bounds, opaque ML predictors, or post-hoc profilers, failing to support tile-centric programming now dominant in AI workloads.

For this daily profile, it is worth opening because it links Triton, Cache, and Hierarchy to a concrete method, not just a broad trend.

What Is New

The novelty signal is concentrated around Triton, Cache, Hierarchy, and CUDA. For this profile, the important question is whether the paper changes how architecture ideas are generated, evaluated, or connected to software and hardware constraints.

Methodology

Read this as a loop: define the target system, apply the proposed mechanism, measure against a baseline, then use the measured signal to justify the next design choice. Mechanism: Recent GPU programming frameworks such as Triton, TileLang, and CUDA Tile adopt tiles as first-class primitives, making tile-centric programming the prevailing approach for high-performance GPU kernels. Evidence: At up to 32 GPUs, TileSight achieves 16.18% weighted MAPE (wMAPE) on fused distributed kernels and 13.52% wMAPE on end-to-end vLLM serving.

score(design) = quality_metric(design) - cost_to_evaluate(design) + feedback_gain(design)

Figure To Read First

Read this visual first: focus on the first architecture, workflow, or pipeline figure before the experiments. It should show what is optimized, what feedback signal is used, and where the system boundary sits.

Minimal Mental Model

research artifact
  question      -> what design, runtime, or system boundary changes?
  mechanism     -> model, agent, compiler, simulator, or hardware feedback
  evaluation    -> baseline comparison plus cost / latency / accuracy signal
  reusable idea -> what should carry into the next architecture experiment?

Why It Matters

Paper recommendations matter when they sharpen the research map: what problem is now easier to study, what methodology becomes reusable, and which architecture assumptions should be questioned next.