Performance Optimization on Ascend, Biren, and Cambricon Training Course
Ascend, Biren, and Cambricon stand out as premier AI hardware platforms in China, each providing distinct acceleration and profiling capabilities designed for large-scale AI workloads in production environments.
This instructor-led training session, available either online or onsite, targets advanced AI infrastructure and performance engineers looking to enhance model inference and training workflows across various Chinese AI chip architectures.
Upon completing this course, participants will gain the ability to:
- Evaluate model performance on Ascend, Biren, and Cambricon systems.
- Diagnose system bottlenecks and identify inefficiencies in memory or compute resources.
- Implement optimizations at the graph, kernel, and operator levels.
- Configure deployment pipelines to maximize throughput and minimize latency.
Course Format
- Engaging lectures combined with interactive discussions.
- Practical application of profiling and optimization tools across different platforms.
- Structured exercises centered on real-world tuning scenarios.
Customization Options
- If you require a tailored training program aligned with your specific performance environment or model type, please contact us to make arrangements.
Course Outline
Fundamentals of Performance and Key Metrics
- Analyzing latency, throughput, power consumption, and resource utilization
- Distinguishing between system-level and model-level bottlenecks
- Approaches to profiling for inference versus training tasks
Profiling on Huawei Ascend
- Leveraging CANN Profiler and MindInsight
- Conducting diagnostics at the kernel and operator levels
- Analyzing offload patterns and memory mapping strategies
Profiling on Biren GPUs
- Utilizing performance monitoring features within the Biren SDK
- Managing kernel fusion, memory alignment, and execution queues
- Implementing power and temperature-aware profiling techniques
Profiling on Cambricon MLU
- Employing BANGPy and Neuware performance utilities
- Gaining visibility into kernel operations and interpreting logs
- Integrating the MLU profiler with deployment frameworks
Optimization at the Graph and Model Levels
- Strategies for graph pruning and quantization
- Techniques for operator fusion and computational graph restructuring
- Standardizing input sizes and fine-tuning batch configurations
Memory and Kernel Optimization Techniques
- Enhancing memory layout efficiency and reuse strategies
- Managing buffers effectively across different chipsets
- Applying platform-specific tuning techniques for kernels
Cross-Platform Best Practices
- Achieving performance portability through abstraction strategies
- Developing shared tuning pipelines suitable for multi-chip environments
- Case Study: Optimizing an object detection model across Ascend, Biren, and MLU architectures
Summary and Future Steps
Requirements
- Prior experience in AI model training or deployment pipelines
- Solid understanding of GPU/MLU compute principles and model optimization techniques
- Familiarity with basic performance profiling tools and metrics
Target Audience
- Performance engineers
- Teams responsible for machine learning infrastructure
- AI system architects
Open Training Courses require 5+ participants.
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