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 tailored for large-scale AI workloads.
Designed for advanced AI infrastructure and performance engineers, this instructor-led live training (available online or onsite) focuses on optimizing model inference and training workflows across multiple Chinese AI chip ecosystems.
Upon completion of this training, participants will gain the ability to:
- Conduct benchmarking on Ascend, Biren, and Cambricon platforms.
- Pinpoint system bottlenecks and identify inefficiencies in memory and compute resources.
- Implement optimizations at the graph, kernel, and operator levels.
- Refine deployment pipelines to enhance both throughput and latency.
Course Format
- Engaging interactive lectures and discussions.
- Practical application of profiling and optimization tools on each respective platform.
- Guided exercises centered on real-world tuning scenarios.
Customization Options
- For customized training tailored to your specific performance environment or model type, please contact us to arrange a session.
Course Outline
Performance Concepts and Metrics
- Latency, throughput, power consumption, and resource utilization
- Distinguishing between system-level and model-level bottlenecks
- Differences in profiling for inference versus training
Profiling on Huawei Ascend
- Utilizing CANN Profiler and MindInsight
- Diagnostics for kernels and operators
- Analyzing offload patterns and memory mapping
Profiling on Biren GPU
- Leveraging Biren SDK for performance monitoring
- Kernel fusion, memory alignment, and execution queues
- Profiling with awareness of power and temperature constraints
Profiling on Cambricon MLU
- Using BANGPy and Neuware performance tools
- Gaining kernel-level visibility and interpreting logs
- Integrating the MLU profiler with deployment frameworks
Graph and Model-Level Optimization
- Strategies for graph pruning and quantization
- Operator fusion and restructuring of computational graphs
- Standardizing input sizes and tuning batch parameters
Memory and Kernel Optimization
- Optimizing memory layout and reuse strategies
- Managing buffers efficiently across different chipsets
- Platform-specific kernel tuning techniques
Cross-Platform Best Practices
- Achieving performance portability through abstraction strategies
- Establishing shared tuning pipelines for multi-chip environments
- Case study: Tuning an object detection model across Ascend, Biren, and MLU
Summary and Next Steps
Requirements
- Experience with AI model training or deployment pipelines
- Understanding of GPU/MLU compute principles and model optimization techniques
- Familiarity with performance profiling tools and metrics
Target Audience
- Performance engineers
- Machine learning infrastructure teams
- AI system architects
Open Training Courses require 5+ participants.
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