How We Reduced Pipeline Latency by 70%

When we started working on NeuralFlow 3.0, our pipeline execution engine was our biggest performance bottleneck. A typical 5-node pipeline took 800ms to execute. Today, that same pipeline runs in under 240ms. Here's how we got there.

The Problem

Our original architecture executed pipeline nodes sequentially, even when nodes had no data dependencies. Each node also incurred a ~50ms overhead for serialization and context switching. With enterprise customers running pipelines with 20+ nodes, this added up quickly.

The Solution: Parallel Execution with DAG Scheduling

We rebuilt the execution engine around a directed acyclic graph (DAG) scheduler. The scheduler analyzes node dependencies at pipeline creation time and generates an optimal execution plan that maximizes parallelism.

# Before: Sequential execution
for node in pipeline.nodes:
    result = node.execute(context)
    context.update(result)

# After: DAG-based parallel execution
execution_plan = dag_scheduler.plan(pipeline)
for stage in execution_plan.stages:
    results = await asyncio.gather(*[
        node.execute(context) for node in stage.nodes
    ])
    context.merge(results)
    

Zero-Copy Serialization

We replaced JSON serialization between nodes with a zero-copy protocol based on Apache Arrow. This eliminated the ~50ms per-node overhead and reduced memory usage by 40%.

Results

Lessons Learned

The biggest takeaway: measure before you optimize. Our initial assumption was that the AI model inference was the bottleneck. Profiling revealed that infrastructure overhead was consuming more time than the actual ML computations.