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huawei-cloud-ascend-op-mfu-calculator
by
huaweicloud-skills-team
· GitHub ↗
· v0.0.2
· MIT-0
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Install in OpenClaw
/install huawei-cloud-ascend-op-mfu-calculator
Description
Calculate MFU (Machine FLOP Utilization) for operators like matmul/GEMM/FlashAttention on Ascend NPU, providing clear formulas and derivation process Use thi...
README (SKILL.md)
Huawei Cloud Ascend Operator MFU Calculator
Overview
This skill calculates MFU (Machine FLOP Utilization) for operators like matmul/GEMM/FlashAttention on Ascend NPU, providing clear formulas and derivation process.
Architecture: Input Validation → FLOPs Calculation → Achieved TFLOPs/s → MFU Calculation → Result Analysis
Related Skills:
huawei-cloud-ascend-profiler-db-explorer- Profiling data analysis for operator performance data
Prerequisites
- Python 3.8+ installed
- Basic understanding of FLOPs calculation concepts
Usage Scenarios
Typical Problem Scenarios:
- Evaluating how well an operator utilizes Ascend NPU compute power
- Comparing performance of different operator implementations
- Identifying optimization opportunities for matrix operations
Typical User Utterances:
- "Calculate MFU for my GEMM operator"
- "What's the machine FLOP utilization for FlashAttention?"
- "Analyze my matmul operator performance efficiency"
Workflow
- Input Collection: Gather operator parameters (matrix dimensions, data types, execution time)
- FLOPs Calculation: Compute theoretical FLOPs for the operation
- Achieved Performance: Calculate achieved TFLOPs/s from execution time
- MFU Calculation: Apply formula MFU = Achieved FLOPs / Peak FLOPs
- Result Analysis: Provide interpretation and optimization suggestions
MFU Calculation Formula
MFU = (Achieved FLOPs / Peak FLOPs) × 100%
Where:
- Achieved FLOPs = Operation FLOPs / Execution Time
- Peak FLOPs = Hardware-specific peak performance (e.g., Ascend 910B: 256 TFLOPs for FP16)
Reference Documents
| Document | Description |
|---|---|
| Ascend 910B Series Technical Specifications | Official Ascend 910B series product specifications |
| MFU Calculation Methodology | Detailed MFU calculation formulas and examples |
| FlashAttention Technical Paper | Original FlashAttention research paper |
Enhanced Features
Intelligent Bottleneck Diagnoser
- AI-powered bottleneck diagnosis that analyzes profiling data to identify root causes automatically
- Classifies bottlenecks into categories: memory-bound, compute-bound, communication-bound, or operator-fallback
- Provides actionable optimization recommendations with priority ranking
- Includes pattern matching for known performance anti-patterns
Parameter Confirmation
| Parameter | Description | Required |
|---|---|---|
| operator | Operator type (matmul/flash_attention/gemm, etc.) | Yes |
| flops | Theoretical FLOPs of the operator | Yes |
| time_ms | Operator execution time (milliseconds) | Yes |
| peak_tflops | Hardware peak computing power (TFLOPS) | Yes |
| device | NPU device type (910B/910, etc.) | No |
Usage Guidance
Install this if you need Ascend MFU calculation guidance. Treat hardware peak numbers and formulas as analysis aids to verify against current official documentation, and only provide profiler CSV files you intend the assistant to analyze.
Capability Assessment
Purpose & Capability
The skill purpose, references, and examples all align around calculating MFU for matmul, GEMM, and FlashAttention performance analysis on Ascend NPUs.
Instruction Scope
The skill declares python3 as an allowed tool and includes examples that may read a user-provided profiling CSV, which is purpose-aligned but should be used only with intended performance data.
Install Mechanism
The artifact contains only markdown files and references; there are no executable scripts, package install hooks, dependencies, or setup commands.
Credentials
Requested capability is proportionate for arithmetic and CSV-based performance calculations; there is no request for credentials, account access, network calls, broad filesystem access, or data mutation.
Persistence & Privilege
No persistence, background workers, privilege escalation, credential/session handling, or long-running execution is present in the artifacts.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install huawei-cloud-ascend-op-mfu-calculator - After installation, invoke the skill by name or use
/huawei-cloud-ascend-op-mfu-calculator - Provide required inputs per the skill's parameter spec and get structured output
Version History
v0.0.2
No changes detected in this version.
- No file changes or updates were made compared to the previous version.
v0.0.1
- Initial release of the Huawei Cloud Ascend Operator MFU Calculator skill.
- Calculates MFU (Machine FLOP Utilization) for operators such as MatMul, GEMM, and FlashAttention on Ascend NPUs.
- Provides step-by-step calculation: input parameters → FLOPs calculation → achieved performance → MFU computation → result analysis.
- Includes intelligent bottleneck diagnosis with optimization suggestions and pattern recognition.
- Offers clear usage scenarios, formula documentation, and hardware-specific references for Ascend devices.
Metadata
Frequently Asked Questions
What is huawei-cloud-ascend-op-mfu-calculator?
Calculate MFU (Machine FLOP Utilization) for operators like matmul/GEMM/FlashAttention on Ascend NPU, providing clear formulas and derivation process Use thi... It is an AI Agent Skill for Claude Code / OpenClaw, with 38 downloads so far.
How do I install huawei-cloud-ascend-op-mfu-calculator?
Run "/install huawei-cloud-ascend-op-mfu-calculator" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is huawei-cloud-ascend-op-mfu-calculator free?
Yes, huawei-cloud-ascend-op-mfu-calculator is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does huawei-cloud-ascend-op-mfu-calculator support?
huawei-cloud-ascend-op-mfu-calculator is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created huawei-cloud-ascend-op-mfu-calculator?
It is built and maintained by huaweicloud-skills-team (@huaweiclouddev); the current version is v0.0.2.
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