Performance Optimization on Ascend, Biren, and Cambricon Training Course
Ascend, Biren, and Cambricon represent the forefront of AI hardware platforms in China, each providing distinct acceleration and profiling capabilities tailored for large-scale production AI workloads.
This instructor-led training, available either online or onsite, targets advanced AI infrastructure and performance engineers seeking to enhance model inference and training workflows across these diverse Chinese AI chip ecosystems.
Upon completion of this course, participants will be equipped to:
- Evaluate model performance on Ascend, Biren, and Cambricon platforms through benchmarking.
- Pinpoint system bottlenecks and inefficiencies related to memory and computation.
- Implement optimizations at the graph, kernel, and operator levels.
- Refine deployment pipelines to achieve superior throughput and reduced latency.
Course Format
- Engaging interactive lectures and discussions.
- Practical application of profiling and optimization tools on each specific platform.
- Guided exercises centered on real-world tuning scenarios.
Customization Options
- For customized training tailored to your specific performance environment or model architecture, please contact us to arrange a session.
Course Outline
Performance Concepts and Metrics
- Analyzing latency, throughput, power consumption, and resource utilization
- Distinguishing between system-level and model-level bottlenecks
- Profiling strategies for inference versus training workloads
Profiling on Huawei Ascend
- Utilizing CANN Profiler and MindInsight
- Conducting kernel and operator diagnostics
- Analyzing offload patterns and memory mapping
Profiling on Biren GPU
- Leveraging Biren SDK for performance monitoring
- Implementing kernel fusion, memory alignment, and execution queues
- Performing power and temperature-aware profiling
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
- Employing graph pruning and quantization strategies
- Executing operator fusion and computational graph restructuring
- Standardizing input sizes and tuning batch parameters
Memory and Kernel Optimization
- Optimizing memory layout and data reuse
- Managing buffers efficiently across different chipsets
- Applying platform-specific kernel tuning techniques
Cross-Platform Best Practices
- Achieving performance portability through abstraction strategies
- Developing shared tuning pipelines for multi-chip environments
- Case Study: Tuning an object detection model across Ascend, Biren, and MLU
Summary and Next Steps
Requirements
- Practical experience with AI model training or deployment pipelines
- Understanding of GPU/MLU compute principles and model optimization techniques
- Familiarity with basic 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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