Parallelization
Parallelization runs multiple LLM calls at the same time and aggregates the results. Two flavors: sectioning (split a task into independent subtasks run in parallel) and voting (run the same task several times to improve reliability or coverage). It cuts latency and can raise quality.
Problem
Running independent subtasks one after another wastes time, and a single sample of a hard task can be unreliable.
When to use it
Use parallelization when subtasks are independent (sectioning), or when multiple attempts at the same task improve confidence or coverage (voting).
Solution
Sectioning: split the work into independent pieces, run them concurrently, and combine the outputs. Voting: run the same prompt multiple times (or with variations) and aggregate by majority, union or a judge.
Both reduce wall-clock time versus sequential execution; voting additionally trades extra cost for higher reliability on tasks where a single sample is risky.
Components
Benefits
- Lower latency by running calls concurrently.
- Voting improves reliability and coverage.
- Each parallel call stays simple and focused.
Risks
- Voting multiplies token cost.
- Aggregation logic can be tricky to get right.
- Subtasks assumed independent may actually interact.
When not to use it
- When subtasks depend on each other's output — chain them.
- When cost is tight and a single call suffices.
- When results cannot be aggregated meaningfully.
Technologies
Examples
- Summarizing many documents at once, then merging the summaries.
- Running a safety check in parallel with the main response.
- Sampling an answer several times and taking the majority.
KPIs
- Latency reduction vs. sequential
- Wall-clock saved by running calls concurrently; the whole point of the pattern.
- Aggregation quality
- Whether merging the parallel outputs preserves correctness — the hard part is the join, not the fan-out.
- Concurrency cost
- Total tokens across all parallel branches; you trade money for speed, so watch the multiplier.
- Rate-limit / throttle rate
- How often parallel calls hit provider rate limits, which silently serializes or fails them.
Observed failure modes
- Aggregation errors: parallel results are correct individually but combined wrongly (double-counting, contradictions).
- Rate limiting turns intended parallelism back into slow, serialized calls.
- Cost surprise: N parallel branches cost N× even when only one result is used.
- Partial failure handling: one branch fails and the aggregator either blocks or silently drops it.
Lessons learned
- Design the aggregation step first — combining results well is harder than splitting the work.
- Respect provider rate limits with batching or backoff, or parallelism evaporates.
- Only parallelize independent sub-tasks; dependencies force a sequence anyway.
- Decide explicitly how partial failures are handled before they happen in production.
FAQs
- What is the difference between sectioning and voting?
- Sectioning splits one task into different independent subtasks; voting runs the same task multiple times to aggregate for reliability.
- Does voting always improve quality?
- Often, on tasks where samples vary, but it multiplies cost. Reserve it for high-stakes steps where a single sample is risky.
- How do I combine parallel results?
- Depending on the case: concatenate sections, take a majority vote, union the findings, or use a judge model to synthesize.