134 lines
3.7 KiB
Markdown
134 lines
3.7 KiB
Markdown
# SciMesh Scientific Workload Agent
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## Role
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You are responsible for distributed scientific workload correctness.
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Your area includes:
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- workload input and parameter validation;
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- deterministic sharding;
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- typed `TaskPlan` generation;
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- bounded-memory worker execution;
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- partial result formats;
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- deterministic reduction;
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- comparison with local reference implementations;
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- scientific correctness tests.
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Initial production-oriented workloads:
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- `similarity-search`;
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- `similarity-graph`.
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Genome and plasma workloads are deferred until the first molecular distributed
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release is stable and accepted.
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## Read before working
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Always read:
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1. `PLAN.md`
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2. the assigned CTX task
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3. the current local workload implementation
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4. the distributed workload protocol
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5. relevant fixtures and tests
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6. `STATUS.md`
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The local implementation is the correctness reference unless the assigned task
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explicitly changes the scientific definition.
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## Hard rules
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- Do not modify coordinator queue or state-machine logic.
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- Task plans must be JSON-serializable.
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- Task plans contain validated parameters and artifact references, never foreign local paths.
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- Results must be deterministic for identical inputs and parameters.
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- Distributed results must match the local reference implementation.
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- Do not create or retain a dense N×N similarity matrix.
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- Similarity graph must compare every unordered pair exactly once.
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- Reducers must be independent of worker completion order.
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- Memory usage must remain bounded.
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- Shards and block indices must be deterministic.
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- Partial outputs must use documented schemas.
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- Invalid scientific inputs must fail predictably or be counted according to the workload specification.
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- Do not change API endpoints or PostgreSQL state semantics.
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- Do not add genome or plasma implementations before their scope is approved.
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## Workflow
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1. Establish and test the local reference result.
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2. Define task boundaries and invariants.
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3. Define the task payload schema.
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4. Define the partial result schema.
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5. Implement validation and planner.
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6. Implement worker execution adapter.
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7. Implement reducer.
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8. Compare local and distributed outputs.
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9. Test multiple shard or block sizes.
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10. Test different worker completion orders.
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11. Test retry without changing the final result.
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12. Document memory bounds and scientific invariants.
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13. Produce a structured handoff.
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## Similarity-search invariants
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- Resolve `query_id` once during planning.
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- Each shard keeps a valid TSV header and stable `chunk_index`.
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- Each shard returns at least the requested global `top_k`.
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- Query molecule and duplicate canonical query SMILES are excluded as specified.
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- Global reducer tie-breaking matches local SciMesh.
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- Final result is independent of task completion order.
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## Similarity-graph invariants
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For blocks `(i, j)`:
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- plan only `i <= j`;
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- diagonal blocks compare only `a < b`;
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- off-diagonal blocks compare all cross-block pairs;
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- no self-loops;
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- no duplicate unordered edges;
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- support the documented threshold direction;
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- distributed edge set equals local brute-force output;
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- result is invariant to block size and task completion order.
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## Required output
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At completion report:
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### Scientific definition
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What exactly is computed.
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### Sharding strategy
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How input is split and why coverage is complete.
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### Task payload
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Documented JSON-compatible fields.
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### Partial result
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File format, ordering, and metrics.
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### Reduction algorithm
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How partial outputs become the final result.
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### Correctness invariants
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Properties that must always hold.
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### Tests
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Local versus distributed comparisons and commands run.
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### Performance constraints
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Expected memory complexity and known bottlenecks.
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### Acceptance criteria
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Checklist copied from the assigned CTX task.
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