The TODO List as a Window into Engineering Methodology: Planning Phase 7 Validation

Introduction

In the middle of a high-stakes optimization campaign for Filecoin's Groth16 proof generation pipeline, a seemingly mundane message appears. Message [msg 2095] contains nothing more than a todowrite tool call — a structured TODO list being updated by the AI assistant. On the surface, it is a simple planning artifact: five items tracking the test campaign for Phase 7 of the cuzk SNARK proving engine. But this message is far more significant than its modest format suggests. It marks the precise inflection point where implementation ends and validation begins, where code becomes data, and where architectural theory meets the unforgiving reality of benchmark numbers.

To understand why this message matters, we must understand what came before it. The preceding messages document the culmination of weeks of work: the Phase 7 per-partition dispatch architecture, committed as f5bfb669 on the feat/cuzk branch ([msg 2086]). This was a fundamental restructuring of how the proving engine handles PoRep C2 proofs. Instead of treating a proof as a monolithic unit — synthesizing all 10 partitions at once, then proving them sequentially — Phase 7 treats each of the 10 partitions as an independent work unit flowing through the engine pipeline. Each partition is synthesized individually, proved with num_circuits=1 (reducing b_g2_msm from ~25s to ~0.4s), and assembled into the final 1920-byte proof. The expected payoff was dramatic: ~100% GPU utilization, zero cross-sector GPU idle gaps, and a throughput improvement from ~42.8s/proof toward ~30s/proof.

But these were predictions. The implementation had compiled cleanly (578 insertions across 4 files), the commit message had been written with confident projections, but no one had actually run it yet. The user's prompt at [msg 2089] — "Do some test runs!" — was both a command and a reality check.

The Message: A TODO List as a Theory of Validation

The subject message reads:

[assistant] [todowrite] {"todos":[{"content":"Create Phase 7 test config (partition_workers=20)","priority":"high","status":"in_progress"},{"content":"Start daemon with Phase 7 config","priority":"high","status":"pending"},{"content":"Run single-proof latency test","priority":"high","status":"pending"},{"content":"Run multi-proof throughput test (5 proofs, j=2)","priority":"high","status":"pending"},{"content":"Run compariso...

The todowrite tool call writes structured TODO items to a file that persists across the conversation, serving as both a plan and a progress tracker. The items are ordered deliberately, revealing the assistant's theory of how to validate a complex system:

Create Phase 7 test config — The first step is to create a configuration file that enables the new partition_workers setting. This is infrastructure: without the right config, the daemon won't use the new code path. The fact that this item is already "in_progress" while the rest are "pending" shows that the assistant has already begun acting on the plan before finishing writing it down.
Start daemon with Phase 7 config — The daemon must be launched with the new configuration. This is a non-trivial step: it requires killing any existing daemon instance, waiting for it to terminate, launching the new one, and verifying it starts correctly (SRS preloading, GPU initialization, etc.).
Run single-proof latency test — The first real test. A single proof measures end-to-end latency including cold-start effects (first-time SRS loading, cache warming). This validates basic correctness: does the pipeline actually produce a valid proof? It also establishes the baseline for the "first proof penalty" that subsequent tests will try to amortize.
Run multi-proof throughput test (5 proofs, j=2) — The throughput test with 5 proofs at concurrency 2 is where Phase 7's cross-sector pipelining should shine. By dispatching multiple proofs' partitions through the same synthesis→GPU pipeline, the engine can overlap one proof's GPU work with another's synthesis. The choice of j=2 (concurrency 2) and 5 proofs is strategic: enough to reach steady-state and measure sustained throughput, but not so many as to overwhelm the system or take excessive wall-clock time.
Run comparison... — The truncated fifth item almost certainly involves comparison against a baseline (Phase 6 slotted pipeline or the original sequential pipeline). Without comparison, the numbers are meaningless — a 72s single-proof time could be good or bad depending on what the previous implementation achieved.

The Reasoning Behind the Plan

The TODO list embodies a specific engineering philosophy: measure first, optimize second, but plan the measurement before you start. The assistant is not diving into ad-hoc testing. It has a structured campaign that progresses from infrastructure → correctness → single-point latency → sustained throughput → comparative analysis.

This mirrors the scientific method in engineering contexts. Step 1 establishes the experimental apparatus (the config). Step 2 powers it on. Step 3 measures a single data point. Step 4 measures the system under load. Step 5 compares against controls. Each step depends on the previous one succeeding, and each step produces a specific kind of knowledge.

The choice of partition_workers=20 is itself a hypothesis. The semaphore limits concurrent synthesis to 20 workers, which for 10 partitions per proof means at most 2 proofs' worth of synthesis can run simultaneously. This is a deliberate trade-off: enough parallelism to keep the GPU fed with work, but not so much that CPU contention destroys the benefit. The assumption is that 20 synthesis workers will saturate the CPU without over-subscribing it, leaving room for the GPU worker's CPU-side preamble and epilogue to execute promptly.

Assumptions Embedded in the Plan

Every TODO list makes assumptions about the world, and this one is no exception:

The implementation is correct. The assistant assumes that the 578 lines of new code compile correctly (verified by cargo check in [msg 2073]), link correctly, and execute the intended logic. There is no TODO item for "debug crash on first run" — the plan assumes the first test will succeed.
The test infrastructure is available and working. The assistant assumes the cuzk-bench binary (rebuilt in [msg 2092]) can communicate with the daemon, that the C1 test data at /data/32gbench/c1.json is valid, and that the daemon's gRPC endpoint will respond correctly.
The GPU and CUDA environment are functional. The daemon requires CUDA-capable GPUs with the supraseal library. The plan assumes the hardware is present, the drivers are loaded, and the CUDA kernels will execute correctly.
The system has sufficient resources. Phase 7 was designed to reduce peak memory from ~228 GiB to ~71 GiB, but the plan assumes the test machine has enough RAM, disk space for SRS parameters, and thermal headroom for sustained GPU load.
The benchmarks will produce interpretable results. The assistant assumes that the wall-clock times, prove times, and queue times reported by the bench tool will be meaningful and can be analyzed to understand system behavior. These assumptions were largely validated in the subsequent messages. The single-proof test at [msg 2106] completed successfully in 72.8s (38.8s prove time), confirming correctness. The multi-proof test at [msg 2110] achieved ~50.7s/proof throughput with j=3, and [msg 2111] showed ~45-50s/proof with j=2. But the plan also revealed an incorrect assumption: the assistant expected ~100% GPU utilization, while the actual analysis at [msg 2115] showed only 64.3% GPU efficiency. This discrepancy — between the theoretical model and measured reality — would drive the next phase of optimization (Phase 8's dual-GPU-worker interlock).

Input Knowledge Required

To fully understand this message, a reader needs:

Knowledge of the Phase 7 architecture: The per-partition dispatch model, the partition_workers semaphore, the ProofAssembler routing logic, and the malloc_trim memory management strategy. This is documented in the commit message at [msg 2086] and the design document c2-optimization-proposal-7.md.
Understanding of the test infrastructure: The cuzk-bench tool's batch command, the TOML configuration format, the daemon's lifecycle (start, SRS preload, listen for requests), and the gRPC communication protocol.
Awareness of the optimization goals: The project aims to reduce proof latency, increase throughput, and lower peak memory for Filecoin PoRep C2 proving. The specific targets (~30s/proof, ~100% GPU utilization) are documented in the Phase 7 commit.
Context from previous phases: Phase 6's slotted pipeline, Phase 5's parallel synthesis, and the baseline performance numbers (~42.8s/proof for the standard pipeline, ~72s for the partitioned path) provide the comparison points that make the TODO list meaningful.

Output Knowledge Created

The TODO list itself is not a knowledge artifact — it does not contain benchmark results, architectural insights, or performance data. But it creates knowledge in a different sense: it structures the knowledge-gathering process that follows. By defining what to measure and in what order, the TODO list determines what will be learned.

The knowledge that will be created by executing this plan includes:

Single-proof latency: The end-to-end time for one proof, including cold-start effects. This tells us the minimum time a user will wait for the first proof.
Multi-proof throughput: The sustained proofs-per-minute rate under concurrency. This tells us the system's capacity for batch operations.
GPU utilization: The fraction of time the GPU spends computing vs. waiting. This reveals whether the pipeline is GPU-bound or CPU-bound.
Inter-partition gaps: The idle time between successive GPU calls. This exposes overhead in the dispatch and result-handling logic.
Comparison against baselines: Whether Phase 7 improves upon Phase 6 and the original sequential pipeline. All of this knowledge is downstream of the TODO list. The plan is the seed from which understanding grows.

The Thinking Process Revealed

The TODO list is a window into the assistant's reasoning at a critical moment. Having just committed 578 lines of new code, the assistant could have declared victory and moved on. Instead, it immediately pivots to validation. The items reveal a mind that thinks in terms of experiments: create the conditions (config), start the apparatus (daemon), measure the simplest case (single proof), increase complexity (multi-proof), and compare against controls.

The ordering also reveals risk management. Single-proof first, because if it fails, there's no point running multi-proof. Multi-proof with j=2 (not j=1 or j=10) because it's a conservative test that should show the pipelining benefit without overwhelming the system. Comparison last, because you need the Phase 7 numbers before you can compare them to anything.

The "in_progress" status on the first item while the message is being written shows that the assistant has already begun executing — it's not just planning, it's acting. The TODO list is a living document, updated as work progresses, not a static plan written once and followed blindly.

Conclusion

Message [msg 2095] is a TODO list. But it is also a theory of validation, a plan for knowledge creation, and a window into a systematic engineering methodology. It captures the moment between "it compiles" and "it works" — the critical transition from implementation to measurement that separates wishful thinking from evidence-based engineering. The subsequent messages would validate some assumptions (the code runs, the pipeline flows) and challenge others (GPU utilization is 64.3%, not 100%), driving the next iteration of optimization. The TODO list, humble as it is, is the engine of that iteration.