Imported from huaweicloud/huaweicloud-skills (skills/ai/modelarts/huawei-cloud-ascendc-operator-performance-optim/SKILL.md). Install upstream with npx skills add huaweicloud/huaweicloud-skills --skill huawei-cloud-ascendc-operator-performance-optim. Copyright stays with the author.

Huawei Cloud AscendC Operator Performance Optimization

Overview

This skill provides guidance for developing and optimizing custom operators using AscendC programming language.

Architecture: Performance Analysis → Bottleneck Identification → Operator Development → Optimization → Validation

Related Skills:

• huawei-cloud-ascend-profiler-db-explorer - Performance data analysis and bottleneck identification
• huawei-cloud-ascend-small-model-migrate - Migration workflow that may require operator optimization

Architecture Components

This skill involves the following cloud services and components:

• AscendC: Programming language for custom operator development
• CANN: Huawei Cloud AI Computing Platform for NPU
• Ascend 910B: Target NPU hardware for operator deployment
• Ascend Profiler: Performance analysis tool for validation

Architecture Diagram:

┌─────────────────────────────────────────────────────────────┐
│            AscendC Operator Optimization Skill             │
├─────────────────────────────────────────────────────────────┤
│  ┌──────────────┐    ┌──────────────┐    ┌──────────────┐ │
│  │  Performance │───▶│  Bottleneck  │───▶│  Operator    │ │
│  │  Analysis    │    │  Identification│   │  Development │ │
│  └──────────────┘    └──────────────┘    └──────────────┘ │
│         │                   │                   │          │
│         ▼                   ▼                   ▼          │
│  ┌──────────────┐    ┌──────────────┐    ┌──────────────┐ │
│  │  Profiling   │    │  Optimization│    │  Validation │ │
│  │  Data        │    │  Techniques  │    │  & Testing  │ │
│  └──────────────┘    └──────────────┘    └──────────────┘ │
└─────────────────────────────────────────────────────────────┘

Use Cases

Typical Problem Scenarios:

• Optimizing performance-critical operators on Ascend NPU
• Developing custom operators for specific workloads
• Improving model inference performance through operator optimization
• Fixing operator bottlenecks identified during profiling
• Implementing missing operators for NPU deployment

Typical User Phrases:

• "Optimize my custom operator for Ascend"
• "Develop AscendC operator for GEMM"
• "Improve inference performance on NPU"
• "Fix bottleneck operator"
• "Implement custom operator using AscendC"
• "AscendCOperator"
• "OptimizationAscendOperatorPerformance"
• "OperatorPerformance"

Scope

Supported:

• Custom operator development in AscendC
• Performance optimization for existing operators
• Operator validation and testing

Not supported:

• Non-AscendC operator development
• Framework-level optimizations

Core Workflow

1. Performance Analysis

• Use profiling tools to identify performance bottlenecks
• Analyze operator execution time and resource utilization

2. Bottleneck Identification

• Identify operators with high execution time
• Determine optimization opportunities

3. Operator Development

• Implement custom operators using AscendC
• Follow AscendC best practices

4. Optimization Techniques

• Memory optimization
• Compute optimization
• Data layout optimization

5. Validation

• Verify functional correctness
• Validate performance improvement

Reference Documents

Prerequisites

• CANN >= 7.0.0 installed
• AscendC >= 1.0.0 installed
• Ascend NPU driver installed and working properly
• Operator code or performance data to be optimized

Core Commands

# Analyze operator performance bottlenecks
msprof --output=/path/to/output ./my_operator

# Optimize operator using AscendC
# Refer to CANN development guide for operator development

Parameter Confirmation

Output Format

Performance analysis results are saved in the specified output directory:

output/
├── summary.json           # Performance summary
├── operator_stats.csv     # Operator execution statistics
├── timeline.json          # Execution timeline data
└── recommendations.md     # Optimization recommendations

Summary JSON Structure:

{
  "total_time_ms": 1234.56,
  "operator_count": 42,
  "top_operators": [
    {"name": "CustomGEMM", "time_ms": 456.78, "percentage": 37.0},
    {"name": "VectorAdd", "time_ms": 123.45, "percentage": 10.0}
  ],
  "optimization_candidates": ["CustomGEMM", "DataTransfer"]
}

Validation Method

Functional Validation

1. Run operator with test inputs
2. Compare outputs with reference implementation
3. Verify numerical accuracy (tolerance: 1e-5 for FP32, 1e-3 for FP16)

Performance Validation

1. Benchmark operator before optimization
2. Apply optimization changes
3. Benchmark operator after optimization
4. Calculate speedup ratio: speedup = time_before / time_after

Acceptance Criteria

• Functional correctness: Output matches reference within tolerance
• Performance improvement: Speedup >= 1.2x (20% improvement)
• No regression: Other operators not affected

Best Practices

Memory Optimization

• Use GM (Global Memory) for large tensors
• Use L1/L0A/L0B for intermediate results in matrix operations
• Align memory access to 32-byte boundaries
• Reuse memory buffers when possible

Compute Optimization

• Vectorize operations using AscendC intrinsics
• Use MMA (Matrix Multiply Accumulate) for matrix operations
• Parallelize independent operations
• Minimize synchronization points

Data Layout Optimization

• Use NZ format for matrix operations
• Use ND format for vector operations
• Avoid unnecessary format conversions
• Consider memory coalescing for data access

Code Structure

• Separate compute logic from memory operations
• Use template metaprogramming for flexibility
• Document optimization assumptions
• Profile before and after each optimization

Notes

Common Pitfalls

• Memory bank conflicts: Ensure data is distributed across memory banks
• Unaligned access: Check 32-byte alignment for all buffers
• Excessive synchronization: Minimize barrier usage between kernels
• Wrong data format: Match format to operation type (NZ for matmul, ND for vector)

Performance Tips

1. Profile first to identify real bottlenecks
2. Focus on hot paths (operators with >10% total time)
3. Consider algorithmic changes before micro-optimizations
4. Test with realistic input sizes
5. Validate correctness after each optimization

Debugging Tips

• Use ASCENDC_DEBUG=1 for verbose logging
• Check CANN log files in /var/log/npu/
• Compare with CPU reference implementation
• Use msprof for detailed performance breakdown

Limitations

• AscendC operators are hardware-specific (910B)
• Not all PyTorch operators have AscendC equivalents
• Custom operators require CANN recompilation for deployment