Revolutionizing AI Compute Development Paradigms

In large-scale AI model training, the performance gap between theoretical hardware capabilities and actual implementation often exceeds 30%, primarily due to three technical bottlenecks: operator implementation efficiency, data transfer strategies, and compute-memory coordination. A newly open-sourced infrastructure framework systematically addresses these challenges through a complete toolchain spanning from high-level programming languages to hardware instructions.
The technical stack comprises five core modules:

1. High-Level Compilation Framework: Offers a cleaner programming model than traditional CUDA
2. Matrix Compute Acceleration Library: Optimizes core operators for domestic chip architectures
3. Distributed Communication Protocol Stack: Solves multi-device data synchronization challenges
4. Memory Management Subsystem: Enhances long-sequence processing like KV Cache management
5. Data Preprocessing Engine: Improves sparse data structure handling efficiency
   This modular architecture enables developers to combine optimizations for specific scenarios. For example, in ultra-long text processing, combining KV Cache pooling from the memory subsystem with sparse attention mechanisms from the preprocessing engine achieves multiplicative performance gains.

   Core Component Technical Analysis

   2.1 Compilation Framework Innovation

   Traditional GPU programming requires manual handling of thread warp allocation and shared memory synchronization. The TileLang framework introduces a computational graph abstraction layer that encapsulates hardware operations into high-level semantic units:

   # TileLang matrix multiplication example
   @tile_kernel
   def matmul(A: Tensor[M,K], B: Tensor[K,N]) -> Tensor[M,N]:
    C = allocate(M,N)
    for i in range(M):
        for j in range(N):
            acc = 0
            for k in range(K):
                acc += A[i,k] * B[k,j]
            C[i,j] = acc
    return C

   This declarative model allows developers to focus on algorithm logic while the compiler handles register allocation and loop unrolling automatically. Benchmarks show 60% code reduction with 92% performance of hand-optimized implementations on domestic chips.

   2.2 Compute Library Optimization

   The DeepGEMM library employs three-stage optimization:
6. Algorithm Selection: Dynamically chooses Winograd/Strassen algorithms based on matrix dimensions
7. Memory Access: Maximizes L1 cache utilization through tiling strategies
8. Instruction Scheduling: Leverages unique matrix units in domestic chips for instruction fusion
   In FP16 precision tests, the library achieves 128 TFLOPS peak performance on specific chips - a 3.2x improvement. For Transformer attention computation, fusing Softmax with Scale operations increases compute density by 40%.

   2.3 Communication Protocol Breakthrough

   The DeepEP communication library implements three innovations:

• Topology-Aware Routing: Automatically selects optimal communication paths based on cluster topology
• Overlapped Computation/Communication: Hides gradient synchronization latency through pipelining
• Compressed Transmission Protocol: Quantizes gradient data to reduce network traffic
  In 128-node ResNet-50 training tests, the library achieves 92% end-to-end communication efficiency - an 18% improvement over industry solutions. Under parameter server architectures, All-to-All communication optimizations maintain over 85% scaling efficiency for large models.

  Developer Implementation Guide

  3.1 Rapid Integration

  The open-source components provide standardized APIs with three-step deployment:

1. Install driver and runtime environment
2. Configure build toolchain (CMake/Bazel dual support)
3. Integrate compute libraries and communication stacks
   For PyTorch extension development, optimized operators can be enabled with:

   import torch
   from deepopt import register_optimizations
   register_optimizations(backend='ascend')
   model = MyTransformerModel().to('ascend')

   3.2 Performance Tuning

   Scenario-specific optimization recommendations:

• Compute-Intensive Tasks: Adjust tile sizes to match cache hierarchy
• Communication-Intensive Tasks: Optimize collective operation implementations
• Hybrid Workloads: Use profiling tools to identify and address bottlenecks
  The配套 profiling toolchain automatically generates optimization reports. In BERT training, it identified suboptimal matrix multiplication tiling parameters that, when adjusted, reduced layer computation time by 27%.

  Ecosystem Development Outlook

  This open-source initiative marks a new phase in China’s AI compute ecosystem development by lowering technical barriers through complete infrastructure tooling. Community feedback shows multiple teams have achieved:
• 4096-card cluster training for 100B-parameter models
• Dynamic KV Cache management for long-text models
• Mixed-precision training for sparse models
  Future development will focus on:

1. Heterogeneous Compute Support: Unified scheduling for CPU/NPU/DPU
2. Automated Optimization: Machine learning-based parameter tuning
3. Security Enhancements: Trusted Execution Environment (TEE) integration
   This technical evolution aligns with global industry trends. As domestic chip performance continues improving, software ecosystem maturity becomes the critical competitive factor. The open-source components establish an important foundation for building autonomous AI infrastructure.