Performance Optimization on Ascend, Biren, and Cambricon Training Course
Ascend, Biren, and Cambricon stand as premier AI hardware platforms in China, providing distinct acceleration and profiling solutions tailored for large-scale AI workloads in production.
This instructor-led live training (available online or onsite) targets advanced AI infrastructure and performance engineers who aim to optimize model inference and training processes across various Chinese AI chip ecosystems.
Upon completion of this training, participants will be equipped to:
- Evaluate models on Ascend, Biren, and Cambricon platforms through benchmarking.
- Diagnose 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 reduce latency.
Course Format
- Interactive lectures and discussions.
- Practical application of profiling and optimization tools specific to each platform.
- Guided exercises designed around real-world tuning scenarios.
Customization Options
- For customized training tailored to your specific performance environment or model requirements, please contact us to arrange.
Course Outline
Performance Concepts and Metrics
- Latency, throughput, power consumption, and resource utilization.
- Distinction between system-level and model-level bottlenecks.
- Profiling techniques for inference versus training workloads.
Profiling on Huawei Ascend
- Utilizing CANN Profiler and MindInsight.
- Kernel and operator diagnostics.
- Offload patterns and memory mapping strategies.
Profiling on Biren GPU
- Performance monitoring features within the Biren SDK.
- Kernel fusion, memory alignment, and execution queues.
- Power and temperature-aware profiling.
Profiling on Cambricon MLU
- BANGPy and Neuware performance tools.
- Kernel-level visibility and log interpretation.
- Integration of the MLU profiler with deployment frameworks.
Graph and Model-Level Optimization
- Graph pruning and quantization strategies.
- Operator fusion and computational graph restructuring.
- Input size standardization and batch tuning.
Memory and Kernel Optimization
- Optimizing memory layout and reuse.
- Efficient buffer management across different chipsets.
- Platform-specific kernel-level tuning techniques.
Cross-Platform Best Practices
- Performance portability: 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 computing principles and model optimization techniques.
- Basic familiarity with performance profiling tools and associated metrics.
Target Audience
- Performance engineers.
- Machine learning infrastructure teams.
- AI system architects.
Open Training Courses require 5+ participants.
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