intel-xpu-cute-accuracy-debug
Debug numerical mismatches in Intel XPU CuTe/CUTLASS kernels, especially Xe DPAS/BDPAS fragment layouts, block-scale payloads, and fused SDPA/GEMM accumulators.
Skill by yiliu30
Chalk Labs
Need help deploying intel-xpu-cute-accuracy-debug?
Chalk Labs sets up agents like this inside real teams: connected to your tools, tested on your data, with a human in the loop where it matters.
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/yiliu30-intel-xpu-cute-accuracy-debug
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
Get the weekly diff
The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.
About
Reviews
Write a reviewNo reviews yet. Installed it? Reviews come from verified installers only. How reviews work
Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/intel-xpu-cute-accuracy-debug
What's inside
- Skill name
intel-xpu-cute-accuracy-debug- Description
- Debug numerical mismatches in Intel XPU CuTe/CUTLASS kernels, especially Xe DPAS/BDPAS fragment layouts, block-scale payloads, and fused SDPA/GEMM accumulators.
- License
- MIT
- Instructions
- 66 lines in SKILL.md · view on GitHub
Files (4)
- SKILL.md
- openai.yaml · agents/
- accuracy-oracles.md · references/
- fragment-and-collective-debugging.md · references/
SKILL.md (first lines)
# Intel XPU CuTe accuracy debugging Use this skill when an Intel XPU CuTe/CUTLASS kernel compiles and launches but fails verification, has unexpectedly poor numerical accuracy, or needs a credible accuracy measurement. It is for layout, fragment-transfer, scale, and accumulator diagnosis; it is not a generic performance-tuning workflow. ## Start with the right comparison Determine whether the observed gap can be caused by intended low-precision arithmetic before changing the kernel: - Compare the kernel with a host/device oracle that mirrors its actual input payload formats, scale rounding, and intermediate quantization. - Compare separately with the original higher-precision inputs to measure end-user quality. - Do not use a loose high-precision tolerance to conclude a register-layout or accumulator path is correct. Report cosine similarity, maximum and mean absolute error, and maximum and mean relative error. Define and print a nonzero denominator floor for relative error. ## Investigation order 1. Reduce to a deterministic, small, single-tile case where possible. 2. Localize the failing boundary: global-memory copy, register reorder, payload/scale construction, MMA/BDPAS call, accumulator update, or epilogue. 3. Inspect the authoritative CuTe MMA traits and relevant reorder atom before inferring register ownership from tensor shape or index order. 4. Prove operand and scale mappings with coordinate fingerprints before using random values. 5. Test accumulation separately with a reduction-oriented operand pattern. 6. Return to random/representative inputs only after the exact-model oracle proves the suspect boundary. Read [fragment-and-collective-debugging.md](references/fragment-and-collective-debugging.md) whenever a fragment reorder, cross-lane transfer, or Xe DPAS/BDPAS operand is involved. Read [accuracy-oracles.md](references/accuracy-oracles.md) when building a host reference or reporting quality metrics. ## Non-obvious invariants - A CuTe register fragment is an ABI-defined mapping, not a contiguous logical matrix. Source/destination element count compatibility is required for a `reorder()` atom even when types compile. - A subgroup collective must be executed by all lanes in the same order with the same logical source operation. Do not select a lane-dependent source register index for `group_broadcast`; condition only the local destination store after the collective. - For block-scaled BDPAS, validate data payload placement and scale/offset payload placement independently. A correct data fingerprint does not prove scale-byte mapping. - Uniform test data can hide K-stripe permutations, out-of-fragment reads, and wrong scale slots. Prefer coordinate-dependent, exactly representable patterns. ## Scope and safety Preserve the kernel's intended precision format and existing user changes. Use temporary compile-time diagnostics for focused experiments, then restore a clean normal build. Start a separate simulator port only when needed and avoid concurrent clients on a shared simulator.
From yiliu30/z2h, MIT.
More from yiliu30/z2h
- 0.563score
profile-cute-xpu
yiliu30
Profile and analyze CuTe/SYCL*TLA kernels on Intel XPU/GPU, especially Intel Xe/BMG/Xe20/Xe35 GEMM-style kernels using CuTe primitives such as XE_DPAS_TT…
SkillBFreshness: B★ 2Claude Code +9 - 0.507score
remote-debug
yiliu30
Drive Claude Code to debug an issue on a remote node over SSH — connect, set up an environment, copy scripts/probes across, run repros, and capture findings.
SkillCFreshness: C★ 2Claude Code +9 - 0.507score
confluence-html
yiliu30
Create rich, interactive HTML that survives Confluence's save sanitizer by using only inline styles, no external resources, and JavaScript for dynamic content.
SkillCFreshness: C★ 2Claude Code +9 - 0.501score
read-arxiv-paper
yiliu30
Given an arxiv URL, download the TeX source, read the paper, and produce a summary markdown file
SkillCFreshness: C★ 2Claude Code +9 - 0.448score
reduce-model
yiliu30
Create a reduced (fewer-layer) version of a large HuggingFace model for fast testing and debugging.
SkillDFreshness: D★ 2Claude Code +9 - 0.443score
find-sth
yiliu30
Investigate a technical question by searching across multiple sources (source code, documentation, academic papers, existing findings) and produce a sourced…
SkillDFreshness: D★ 2Claude Code +9 - 0.443score
observe
yiliu30
Memory observer agent for recording structured observations from primary Claude Code sessions.
SkillDFreshness: D★ 2Claude Code +9 - 0.443score
real-review
yiliu30
Fact-check written content (GitHub issues, docs, reports, posts) by verifying every claim, number, link, code reference, and conclusion against actual evidence.
SkillDFreshness: D★ 2Claude Code +9 - 0.443score
vllm-kernel-trace
yiliu30
Trace which GPU kernels vLLM dispatches for specific model layers (linear, attention, MoE, norms).
SkillDFreshness: D★ 2Claude Code +9
Signals
- Stars
- 2
- Forks
- 0
- Open issues
- 1
- Last commit (this folder)
- Aug 20, 2026 (2 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/yiliu30/z2h
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
More from yiliu30
- 0.563score
profile-cute-xpu
yiliu30
Profile and analyze CuTe/SYCL*TLA kernels on Intel XPU/GPU, especially Intel Xe/BMG/Xe20/Xe35 GEMM-style kernels using CuTe primitives such as XE_DPAS_TT…
SkillBFreshness: B★ 2Claude Code +9 - 0.507score
confluence-html
yiliu30
Create rich, interactive HTML that survives Confluence's save sanitizer by using only inline styles, no external resources, and JavaScript for dynamic content.
SkillCFreshness: C★ 2Claude Code +9 - 0.507score
remote-debug
yiliu30
Drive Claude Code to debug an issue on a remote node over SSH — connect, set up an environment, copy scripts/probes across, run repros, and capture findings.
SkillCFreshness: C★ 2Claude Code +9 - 0.501score
read-arxiv-paper
yiliu30
Given an arxiv URL, download the TeX source, read the paper, and produce a summary markdown file
SkillCFreshness: C★ 2Claude Code +9 - 0.448score
reduce-model
yiliu30
Create a reduced (fewer-layer) version of a large HuggingFace model for fast testing and debugging.
SkillDFreshness: D★ 2Claude Code +9 - 0.443score
observe
yiliu30
Memory observer agent for recording structured observations from primary Claude Code sessions.
SkillDFreshness: D★ 2Claude Code +9
Done for you
Rather not set it up yourself?
Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.
See how we build AI agents →Read the case studies
Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.
- launches shipped
- 50+
- launches shipped
- media placements
- 500+
- media placements
- founders on every account
- 2
- founders on every account
Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block
Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.
Get intel-xpu-cute-accuracy-debug running in your business
Tell us what you want to automate. A founder replies within one working day with a plan, not a pitch deck.
Cite this page
intel-xpu-cute-accuracy-debug skill. Agent Mart. https://agentmart.dev/skills/yiliu30-intel-xpu-cute-accuracy-debug (last updated 2026-10-24)