Outputs¶

Every entry point returns a MappingEstimate and writes a set of files under its output directory, one group_<index>/ folder per fused group. This page covers both.

The result¶

A MappingEstimate holds cycles, the fused groups' estimates plus the reconfiguration the hardware declares, the per-group group_cycles, and the solved context. Read the rest off the context with ctx.get(...):

Key	What it is
`group_latencies`	Per-fusion-group latency breakdown.
`scheduler`	The `SteadyStateScheduler` - the full schedule and timing.
`workload`	The parsed computation graph.
`accelerator`	The parsed hardware model.

estimate = evaluate_mapping(...)
print(estimate.cycles)
ctx = estimate.context
scheduler = ctx.get("scheduler")

Files written to disk¶

Visualizations (PNG) - the tiling and the schedule of each fused group, written into its group_<index>/ folder.

Schedule trace (Perfetto)¶

The schedule can be exported as a Perfetto JSON trace and opened at https://ui.perfetto.dev to inspect each core's timeline and the inter-core transfers. See stream/visualization/ for the trace and plotting helpers.

Typed IR (for tools and agents)¶

For structured, JSON-serializable output, convert the context's objects into the typed IR models. These are the same models the MCP server returns:

from stream.ir import WorkloadIR, AcceleratorIR, AllocationIR

workload_ir    = WorkloadIR.from_internal(ctx.get("workload"))
accelerator_ir = AcceleratorIR.from_internal(ctx.get("accelerator"))
allocation_ir  = AllocationIR.from_internal(ctx.get("scheduler"))

allocation_data = allocation_ir.model_dump()      # JSON-compatible dict

AllocationIR exposes persona views - .algorithmic_view(), .hardware_view(), .compiler_view() - each shaping the same result for a different consumer. The performance view surfaces bottleneck (compute- vs transfer-bound) cycles and per-node utilization. See Using Stream with an AI agent for details.