From FIFO to Priority: A User's Correction on Partition Scheduling in a ZK Proving Pipeline

In the middle of a high-stakes coding session building a CUDA-based zero-knowledge proving daemon, a single sentence from the user derails the assistant's sense of completion and redirects the entire project's scheduling architecture. The message is terse, precise, and devastating to the just-committed work:

GPU assignments still seem more or less random, neither gpu assignments nor synthesis assignments should really be fifo, instead they should be 'lowest partition in oldest pipeline'

To understand the weight of this statement, one must appreciate what came immediately before it. The assistant had just spent multiple rounds implementing, deploying, testing, and committing a FIFO-based ordered synthesis dispatch system. The commit message declared the problem solved: "Replace per-partition tokio::spawn with a shared mpsc channel and synthesis worker pool that processes partitions in FIFO arrival order. This ensures earlier jobs' partitions are synthesized and GPU-proved before later jobs, preventing all pipelines from stalling together." The assistant had run live benchmarks on a remote machine, verified that two concurrent PoRep proofs completed successfully, watched SnapDeals jobs process in real time through the status API, and concluded with "No further pending tasks. The system is stable and processing real workloads."

Then the user speaks.

The Context That Makes This Message Necessary

The conversation leading up to this message reveals a long struggle with partition scheduling in the cuzk proving engine. The system processes proof jobs that are divided into partitions — typically 10 partitions for PoRep proofs and 16 for SnapDeals. Each partition must be synthesized (CPU-bound work that generates constraint system artifacts) and then proved on a GPU. The original architecture dispatched each partition as an independent tokio::spawn task, all racing for budget approval via a Notify-based semaphore. This caused "thundering herd" wakeups and random partition selection across pipelines, meaning all pipelines stalled together instead of completing sequentially.

The assistant's FIFO fix replaced this chaos with an mpsc::channel — a multi-producer, single-consumer queue. Partitions were enqueued in arrival order, and a pool of synthesis workers pulled from the channel in FIFO order. The assistant believed this solved the ordering problem. The commit was made. The todos were marked complete.

But the assistant's own testing data told a different story. In [msg 2839], the status output showed Job A (bd3aad5d) with partition 0 on GPU and Job B (c07dcabe) with partitions 2 and 8 on GPU — simultaneously. The assistant rationalized this: "All 20 partitions (10 per job) were dispatched to the channel. The synthesis workers pull FIFO from the channel, but they all started synthesizing ~62s ago (essentially all started simultaneously since there are 28 workers and only 20 partitions). That's actually expected." The assistant concluded that FIFO ordering "only matters when there are more partitions than budget slots."

The user's message rejects this rationalization entirely.

The Core Insight: FIFO Is the Wrong Abstraction

The user's message makes a crucial distinction that the assistant missed. FIFO preserves insertion order, but it does not guarantee completion order when multiple workers consume from the queue concurrently. When 20 partitions are dispatched into a FIFO channel and 28 synthesis workers are available, every partition starts synthesizing immediately. The order in which they finish synthesis — and therefore the order in which they become available for GPU proving — is determined by the variable computational cost of each partition, not by their position in the dispatch queue.

The user's proposed alternative — "lowest partition in oldest pipeline" — replaces the FIFO abstraction with a priority-based scheduling discipline. This is a fundamentally different approach. Instead of saying "process work items in the order they arrived," it says "at any moment, among all pending work, pick the one that belongs to the pipeline that has been waiting the longest, and within that pipeline, pick the lowest partition index." This is a global priority policy that actively selects the most important work at each scheduling decision point, rather than passively accepting whatever order emerges from a queue.

This distinction matters because the system has two separate scheduling points: synthesis assignment (which partition gets CPU time to build the constraint system) and GPU assignment (which synthesized partition gets GPU time to produce the proof). Both need the same priority policy. A partition cannot go to the GPU until its synthesis is complete, so the GPU scheduler must select from a pool of "ready" items using the same priority ordering. The user explicitly calls out both: "neither gpu assignments nor synthesis assignments should really be fifo."

Assumptions, Mistakes, and the Thinking Process

The assistant made several incorrect assumptions that the user's message exposes. First, the assistant assumed that FIFO dispatch order was sufficient to guarantee FIFO completion order, ignoring the effect of concurrent worker consumption. Second, the assistant assumed that the ordering problem only manifested when the number of partitions exceeded the number of worker slots, when in fact the problem is structural — even with fewer partitions than workers, the GPU can end up proving partitions from a newer job before an older job's partitions are finished. Third, the assistant assumed that "all tasks complete" was a valid conclusion, when the user's observation of ongoing random GPU assignments showed the core problem was unsolved.

The user's thinking process, visible in the concise formulation, reveals a sophisticated understanding of scheduling theory. The phrase "lowest partition in oldest pipeline" encodes a lexicographic priority: primary key is pipeline age (oldest first), secondary key is partition index (lowest first). This is a classic two-level priority scheme that ensures both fairness across pipelines and locality within a pipeline. The user does not specify how to implement this — that is left to the assistant — but the policy is unambiguous.

Input and Output Knowledge

To understand this message, one needs several pieces of input knowledge: the architecture of the cuzk proving engine with its partition-based job model; the distinction between synthesis (CPU-bound) and proving (GPU-bound) phases; the budget-based memory manager that gates how many partitions can be in-flight simultaneously; the status API that exposes per-partition state; and the recently deployed FIFO channel implementation. One also needs to have read the assistant's testing output showing interleaved GPU assignments across jobs.

The output knowledge created by this message is a clear design specification for the next phase of work. It establishes that:

  1. FIFO channels are the wrong data structure for both synthesis and GPU dispatch
  2. Both scheduling points must use the same priority policy
  3. The priority policy is lexicographic: (pipeline_age ASC, partition_index ASC)
  4. A mechanism to track pipeline age (a sequence number or timestamp per job) must be introduced
  5. The just-committed FIFO implementation must be replaced

A Pivot Point

This message is a classic example of a user providing essential domain expertise that the assistant lacked. The assistant, focused on the mechanics of channels and workers, implemented a solution that addressed the symptom (random dispatch) but not the root cause (lack of priority-based scheduling). The user, observing the actual behavior of the system, recognized that the abstraction was wrong and supplied the correct one in a single sentence. The message does not criticize the assistant's effort — it simply states the observation and the correct policy, leaving implementation to follow. It is a model of concise, high-signal feedback that redirects a complex engineering effort onto a more productive path.