GPU operation lowering

Overview

Operation lowering preserves the relationship between semantic operations and the command nodes selected by a backend compiler.

At a glance

Question	Answer
Problem	Retain which semantic operation produced each executable command node.
Reads / writes	Records identities and paths without changing declared graph resource uses.
Ownership	The compiler owns the immutable provenance report.
Output contract	Operation paths paired with lowered command-node identifiers and types.
Expected work	One small provenance record per emitted semantic command node.
Chunks	Does not alter logical or physical data partitioning.
Conditions / budgets	Produced during lowering and consumed by inspectors and planners.
Neighborhood	operation tree → command-node emission → lowering report and diagnostics.

CostLinear CPU bookkeeping in the number of lowered nodes.

Common mistakeDo not discard semantic identity once a backend command is selected.

Why preserve lowering provenance?

The operation IR describes computation semantically while GPUCommandGraph describes executable GPU work. One semantic operation may lower into one command node or dozens of nodes, and composite/control-flow operations introduce additional hierarchy.

Jarnevon therefore preserves the relationship instead of discarding it during lowering:

PCG                         semantic operation
└─ iteration                semantic composite
   ├─ SpMV                  semantic operation
   │  └─ spmv-main          compute node
   ├─ dot                   semantic operation
   │  ├─ reduce-stage-0     compute node
   │  ├─ reduce-stage-1     compute node
   │  └─ reduce-final       compute node
   └─ update                semantic operation
      └─ madd               compute node

graph.operationLowering returns both the semantic operation tree and an immutable list of lowered command nodes with their root-to-leaf operation paths.

Why this is compiler infrastructure

Without provenance, diagnostics can say that reduce-stage-1 took time or consumed memory, but cannot explain which algorithm requested it. With provenance the compiler/inspector can aggregate execution information back to semantic intent:

PCG
  SpMV       0.42 ms
  reductions 0.19 ms
  updates    0.08 ms

The same mapping is needed by future compiler passes for:

• semantic timing and memory attribution;
• selected-strategy explanations;
• generated WGSL navigation;
• control-flow lowering;
• fusion diagnostics;
• Ploor planning/execution boundaries.

Dynamic control-flow boundary

A semantic GPU predicate is not itself a WebGPU command. A dynamic conditional or loop body can contain operations that lower into dispatches with different true workgroup dimensions.

semantic predicate: active
        │
        ├─ SpMV      true dispatch = [4096,1,1]
        ├─ reduction true dispatch = [ 256,1,1]
        └─ scalar    true dispatch = [   1,1,1]

WebGPU conditional compute is realized with GPU-written indirect dispatch arguments. A false predicate writes a zero workgroup dimension; a true predicate must preserve the concrete dispatch dimensions of that particular command node.

Operation-aware lowering therefore needs both pieces of information:

1. which semantic predicate applies to a lowered node;
2. what concrete dispatch geometry that node requires.

This PR establishes (1) through semantic provenance. Capturing/rewriting concrete dispatch geometry for dynamic GPU control is a subsequent lowering capability rather than pretending one generic gate can condition an arbitrary composite.

Compatibility

Existing contributors and direct addComputePass(), addRenderPass(), and addCopyPass() calls continue to work. Nodes emitted outside graph.add(operation) have no semantic operation ancestry and therefore do not appear in the operation-lowering node map.

The initial implementation instruments pass addition additively while the operation API remains experimental. Once the design stabilizes, provenance recording should move into the native GPUCommandGraph node-insertion path without changing the public operation model.