Strategic Context and Core Challenges of Technical Migration

During the critical phase of domestic computing ecosystem development, an open-source model team achieved a milestone technical migration: systematically transitioning their model training framework from mature hardware platforms to domestic chip architectures. This initiative validated cross-architecture migration feasibility while establishing a reusable engineering methodology.

Traditional migration approaches faced three major challenges:

1. Tight coupling between algorithm and hardware layers driving up migration costs
2. Inefficient operator rewriting due to lack of unified abstraction layers
3. Systemic risks from ecosystem component compatibility issues

Through innovative technical design, the team reduced core operator migration cycles by 60% while achieving 93% of hardware theoretical peak performance for critical computing kernels.

Domain-Specific Language Abstraction Layer Design

TileLang Architecture Philosophy

As the migration infrastructure’s cornerstone, TileLang employs a “tiled computation + data flow” programming paradigm with balanced expressiveness and hardware adaptability:

# Matrix multiplication example
@tile_op
def matmul(A, B, C):
    tile_size = 32
    for i in range(0, A.shape[0], tile_size):
        for j in range(0, B.shape[1], tile_size):
            for k in range(0, A.shape[1], tile_size):
                C[i:i+tile_size, j:j+tile_size] += 
                    A[i:i+tile_size, k:k+tile_size] * 
                    B[k:k+tile_size, j:j+tile_size]

This design achieves three key breakthroughs:

• Decoupling computation description from memory management
• Automatic generation of hardware-optimized data layouts
• Support for operator fusion and pipeline optimization

Compiler Intelligence Optimization

The compiler backend performs critical optimizations:

• Memory Planning: Automatic hierarchical storage strategy selection across registers, shared memory, and global memory
• Parallel Scheduling: Optimal thread block configuration generation based on hardware topology
• Synchronization Optimization: Redundant barrier elimination with essential data dependency control

Benchmark data shows 72% reduced memory access latency and 41% improved compute unit utilization for typical operators at FP16 precision.

Operator Migration Engineering Practice

Verification-Migration Dual-Phase Methodology

The team adopted an iterative pattern combining mature platform verification with new architecture migration:

1. Complete algorithm verification and performance tuning on mature platforms
2. Establish operator performance baselines (e.g., 410 TFLOPS for sparse attention kernels)
3. Implement equivalent functionality on target architecture
4. Ensure computational correctness through numerical comparison

This approach decomposes migration risks into manageable technical units, enabling focused hardware-specific optimization rather than algorithm correctness validation.

Critical Operator Optimization Example

For the FlashMLA sparse attention kernel:

• Data Structure Optimization: Compressed Sparse Row (CSR) format reduced memory footprint
• Computational Pattern Innovation: Fused Load-Compute-Store pipeline
• Hardware Feature Utilization: Specialized instruction sequences for domestic chip tensor cores

Performance testing showed 58% reduced latency compared to initial implementation at batch size=8192, achieving 93% of hardware theoretical peak.

Ecosystem Component Decoupling Design

Core Component Correspondence

The migration involves six key component layers:
| Component Type | Mature Platform Implementation | Domestic Architecture Implementation |
|————————-|———————————————-|——————————————————|
| Compute Kernels | CUDA Kernel Library | Ascend C Native Implementation |
| Communication Primitives | NCCL Collective Communication | Huawei CCAF Communication Library Adaptation |
| Memory Management | Custom Allocator | Ascend Dedicated Memory Pool |
| Debugging Tools | Nsight Systems | Huawei MindInsight |
| Performance Analysis | Nvprof | Huawei Profiling Toolchain |
| Model Formats | Custom Checkpoints | Universal ONNX Compatible Format |

This layered decoupling ensures independent component evolution while preventing ecosystem lock-in.

Hybrid Programming Model Support

To maintain development efficiency, the team implemented progressive migration:

# Hybrid programming example
def hybrid_training():
    if platform == 'ascend':
        use_ascend_c_backend()
        enable_ccaf_communication()
    else:
        use_cuda_backend()
        enable_nccl_communication()
    # Unified model definition
    model = build_model_with_tilelang()
    train_loop(model)

This design enables business continuity during migration while allowing gradual component replacement.

Performance Optimization Methodology

Cross-Architecture Tuning Framework

The team established a five-dimensional optimization system:

1. Computational Graph Optimization: Operator fusion, constant folding, dead code elimination
2. Memory Optimization: Shared memory reuse, zero-copy techniques, memory pre-allocation
3. Parallel Optimization: Thread block configuration, pipeline design, asynchronous execution
4. Precision Tuning: Mixed precision training, quantization-aware training
5. Communication Optimization: Overlapping computation/communication, gradient compression, hierarchical synchronization

Automated Tuning Toolchain

To lower optimization barriers, the team developed supporting tools:

• Performance Profiler: Automatic hotspot operator identification
• Configuration Generator: Optimal parameter generation based on hardware topology
• Correctness Validator: Cross-platform numerical comparison system

For a typical model migration, these tools reduced tuning cycles from 2 weeks to 3 days while maintaining performance within 3% of optimal.

Industrial Value of Technical Migration

This practice provides three demonstration aspects for domestic computing ecosystem development:

1. Methodological Level: Validates feasibility of the verification-migration dual-phase approach
2. Technical Level: Constructs reusable abstraction layers and toolchains
3. Ecosystem Level: Demonstrates collaborative innovation with domestic hardware/software stacks

Industry analysis suggests similar approaches can reduce enterprise migration costs by 40-60% while shortening time-to-market by 30-50%. This migration paradigm is becoming an important reference model for domestic computing ecosystem development.

The team is now packaging migration experience into standardized toolkits containing operator template libraries, automated migration tools, and performance tuning manuals. These assets not only serve their model training needs but also provide industry-reusable technical resources to accelerate domestic computing ecosystem maturation.