1. Ecosystem Breakthrough in Domestic AI Computing

An open-source community has released a full-stack toolchain for domestic AI chip platforms, comprising five core components: matrix computation acceleration libraries, distributed communication frameworks, and a high-level programming language. This technical solution matches functionality of leading international platforms, marking China’s AI computing ecosystem’s transition to mature commercial application.

1.1 Core Value of Technical Release

The open-source component matrix includes:

• Computation Acceleration: DeepGEMM (matrix operations), TileKernels (vector processing)
• Communication Optimization: DeepEP (cross-device communication), FlashMLA (sparse attention acceleration)
• Development Tools: TileLang high-level programming language
• Data Processing: DeepSelect (intelligent data filtering)

These components cover the complete model training pipeline from low-level computation to high-level orchestration. Benchmark tests show 30% improved training throughput and 60% shorter operator development cycles when using this toolchain.

1.2 Ecosystem Collaboration Milestone

A domestic chip manufacturer simultaneously open-sourced its CANN-based model deployment framework featuring:

• KV Cache pooling for ultra-long text processing
• Gradient compression algorithms for distributed training
• Asynchronous communication protocols for reinforcement learning

Joint validation demonstrates 92% cluster communication efficiency for trillion-parameter model training - an 18% improvement over traditional approaches.

2. Technical Architecture Analysis

2.1 Computation Acceleration Layer

DeepGEMM employs three optimization strategies:

1. Tiling Optimization: 64×64 matrix blocking for register-level caching
2. Memory Access Pattern: 50% reduced latency through loop unrolling
3. Instruction Parallelism: 8-way SIMD data parallelism

// DeepGEMM optimization example: Blocked matrix multiplication
void deep_gemm(float* A, float* B, float* C, int M, int N, int K) {
    const int BLOCK_SIZE = 64;
    for (int i = 0; i < M; i += BLOCK_SIZE) {
        for (int j = 0; j < N; j += BLOCK_SIZE) {
            for (int k = 0; k < K; k += BLOCK_SIZE) {
                float c_block[BLOCK_SIZE][BLOCK_SIZE] = {0};
                // Block computation implementation
            }
        }
    }
}

TileKernels achieves 128 TFLOPS peak performance on domestic chips through register reuse techniques that boost FMA instruction density to 85%.

2.2 Communication Optimization Layer

DeepEP implements a three-tier hybrid architecture:

1. Intra-node: NUMA-aware scheduling over PCIe 4.0
2. Intra-rack: RDMA over Converged Ethernet (RoCE)
3. Cross-rack: Adaptive gradient compression

This reduces AllReduce communication time from 120ms to 35ms while achieving 88% bandwidth utilization in trillion-parameter training scenarios. FlashMLA decreases memory usage by 60% for long-sequence processing through dynamic masking.

2.3 Development Tools Layer

TileLang features three core capabilities:

1. Domain-Specific Syntax: Built-in matrix/tensor operations
2. Automatic Parallelization: Dependency analysis for parallel graph generation
3. Hardware-Aware Optimization: Automatic prefetching and pipeline insertion

# TileLang example: Attention mechanism implementation
def attention(q, k, v):
    scores = tile.matmul(q, k.transpose()) / sqrt(q.shape[-1])
    weights = tile.softmax(scores)
    return tile.matmul(weights, v)

Compared to CUDA implementations, TileLang reduces operator code volume by 70% while maintaining over 95% hardware utilization. The language has been validated in training multiple trillion-parameter models, handling over 80% of core operator development.

3. Developer Implementation Guide

3.1 Containerized Deployment

FROM ascend-base-image:latest
RUN apt-get update && apt-get install -y \
    tilelang-compiler \
    deepgemm-runtime \
    deepep-sdk
WORKDIR /workspace
COPY ./model /workspace/model

3.2 Performance Tuning Methodology

Computation-Intensive Operators:

• Enable auto-vectorization (-O3 -ftile-vectorize)
• Set optimal block size (BLOCK_SIZE=128)

Communication-Intensive Scenarios:

• Adjust gradient aggregation interval (-batch-size=1024)
• Enable compression (-enable-compression)

Memory Optimization:

• Use memory pooling (-use-memory-pool)
• Activate zero-redundancy mode (-zero-redundancy)

3.3 Typical Use Cases

1. Large-Model Pretraining:

   • Supports 2,048-card distributed training
   • Achieves 98% linear scaling efficiency

2. Long-Sequence Processing:

   • Handles sequences >16K tokens via FlashMLA
   • Reduces memory usage by 40%

3. Sparse Model Training:

   • Dynamic data filtering through DeepSelect
   • Triples effective computation density

4. Ecosystem Development Outlook

This open-source release signifies China’s AI computing ecosystem reaching maturity, offering developers three core benefits:

1. Development Efficiency: High-level abstractions reduce low-level complexity
2. Performance Assurance: Validated optimizations ensure efficient hardware utilization
3. Ecosystem Compatibility: Seamless integration with mainstream frameworks

Industry analysts predict domestic computing platforms will power over 35% of large model development by 2025. This full-stack toolchain serves as critical infrastructure for building autonomous AI technology foundations. Developers can access complete technical documentation and practical examples through official channels to accelerate adoption of domestic AI computing solutions.