ML Infrastructure Programming

DSL Design Principles#

1. Abstraction Level

   • High enough for ML practitioners to use effectively
   • Low enough to enable hardware-specific optimizations
   • Familiar syntax and semantics for target audience
   • Composable and modular design

2. Performance Optimization

   • Automatic kernel fusion and optimization
   • Memory access pattern optimization
   • Hardware-specific code generation
   • Runtime adaptation and tuning

3. Developer Experience

   • Debugging and profiling tools
   • Integration with existing ML workflows
   • Clear error messages and documentation
   • Gradual learning curve

Helion DSL Deep Dive#

Architecture Overview:#

• Python-embedded DSL for ML kernel authoring
• Compiles to Triton for GPU execution
• PyTorch-like syntax for familiarity
• Ahead-of-time autotuning engine

Key Features:#

# Helion DSL example
import helion

@helion.kernel
def matmul_kernel(a, b, c, M, N, K):

# PyTorch-like syntax
    for i in helion.grid(M):
        for j in helion.grid(N):
            acc = 0.0
            for k in range(K):
                acc += a[i, k] * b[k, j]
            c[i, j] = acc

Autotuning Engine:#

• Automatic search space exploration
• Performance model-guided optimization
• Hardware-specific parameter tuning
• Caching of optimal configurations