The Data Archaeology of Optimization: How an AI Assistant Systematically Mapped Eleven Phases of GPU Proof Generation Performance

Introduction

In the world of high-performance computing, optimization is rarely a straight line. It is a winding path of breakthroughs, dead ends, reverted changes, and incremental gains—each phase revealing new bottlenecks and demanding new architectural insights. When an engineering team needs to understand where they've been before deciding where to go next, they must assemble scattered data from multiple documents, reconcile conflicting measurements, and construct a coherent narrative of progress. This is the art of data archaeology, and it is precisely what an AI assistant performed across a seven-message subagent session in the opencode conversation under analysis.

The chunk we examine here—segment 28, chunk 0—captures a complete data-gathering and synthesis mission within a larger investigation of the SUPRASEAL_C2 Groth16 proof generation pipeline for Filecoin's Proof-of-Replication (PoRep) protocol. The parent session had been conducting a deep-dive into this pipeline's notorious ~200 GiB peak memory footprint, had already produced a comprehensive background reference document mapping the entire call chain from Curio's Go task layer through Rust FFI into C++/CUDA kernels, and had identified nine structural bottlenecks. The parent session had just abandoned a flawed Phase 10 two-lock GPU interlock design after discovering fundamental CUDA device-global synchronization conflicts, reverted to the proven Phase 9 single-lock approach, run comprehensive benchmarks, and designed a new Phase 11 with three targeted interventions to reduce DDR5 memory bandwidth contention.

It was at this pivotal moment that the user spawned a subagent with a focused mission: find all performance results across all optimization phases (Phase 0 through Phase 10) and return them in a structured format suitable for a comparison table. What follows is a masterclass in systematic data gathering—a seven-message arc that demonstrates how an AI assistant navigates incomplete information, compensates for tool limitations, and builds a comprehensive picture of a multi-phase optimization campaign spanning ten distinct proposals.

This article synthesizes the entire chunk, examining the assistant's methodology, the key decisions at each stage, the assumptions that shaped the search, and the ultimate output that fed back into the parent session's Phase 11 design work. We will trace the arc from the initial user request through the discovery phase, the parallel ingestion of eleven documents, the exhaustive search for supplementary data sources, and the final synthesis of a comprehensive comparison table that traces the optimization journey from a ~360-second baseline to an expected ~28-34 seconds per proof.

The Mission: A User's Request for Structured Performance Data

The subagent session begins with a user message ([msg 0]) that is remarkable for its precision and structure. The user does not ask a vague question like "what performance data do you have?" Instead, they issue a detailed specification:

Search the cuzk project documentation and code for all performance results across all optimization phases (Phase 0 through Phase 10). I need: 1. Read cuzk-project.md thoroughly — it should have documented results for Phases 0-9 2. Read c2-optimization-proposal-10.md for Phase 10 design/results 3. Search for any other phase result summaries in markdown files matching c2-optimization-proposal-*.md 4. Look for any benchmark log files in /home/theuser/ that contain phase results

For each phase, I need: - Phase name/number and what it did - Single-proof latency (isolation) - Throughput under concurrency (proofs/min or s/proof) - GPU utilization % if available - Any other key metrics (GPU kernel time per partition, synthesis time, etc.)

Return ALL the data you find in a structured format so I can create a comparison table.

This message reveals several things about the user's state of mind and the broader investigation. First, the user knows the project structure intimately—they know the naming conventions (cuzk-project.md, c2-optimization-proposal-*.md), they know the home directory path (/home/theuser/), and they know what data should exist (Phases 0-9 in the main document, Phase 10 in its own proposal). Second, the user has a clear schema in mind for the output: five specific metrics per phase, with the explicit goal of creating a comparison table. Third, the user is thinking about data completeness—they ask for "ALL the data" and include a catch-all for "any other key metrics."

The user's request also reveals an assumption that may not hold: that Phase 10 has results. As the parent session's context shows, Phase 10 was a design that was abandoned after fundamental flaws were discovered. The proposal document may contain only design specifications and expected results, not measured outcomes. The assistant will need to navigate this distinction carefully.

But perhaps the most important aspect of this message is what it tells us about the timing. The parent session was in the middle of designing Phase 11—three interventions to reduce DDR5 memory bandwidth contention. Before committing to a new design direction, the team needed to see the full trajectory of what had been achieved across all prior phases. The comparison table would serve as the evidence base for deciding whether Phase 11's projected improvements were worth the engineering investment. This was not an academic exercise; it was a decision-support tool for a live optimization campaign.

The Discovery Phase: Finding the Data Sources

The assistant's first response ([msg 1]) is a textbook example of systematic reconnaissance. The assistant opens with a clear declaration of intent: "I'll start by finding and reading the main documentation files and searching for any benchmark logs." It then executes four parallel glob tool calls, each searching for files matching specific patterns.

The first two globs are the primary search: **/cuzk-project.md and **/c2-optimization-proposal-*.md. These return exactly what was expected: the main project document at /home/theuser/curio/cuzk-project.md and ten proposal files numbered 1 through 10. The second two globs are exploratory: **/*phase*result* and **/*benchmark*log*. Both return "No files found."

This discovery phase accomplishes several things simultaneously. It confirms that the primary data sources exist and are accessible. It establishes the project's file naming conventions (the c2- prefix, the sequential numbering). It reveals that no standalone benchmark log files or phase result summaries exist outside the markdown documentation. And it implicitly defines the scope of the subsequent reading phase: the assistant now knows it must extract all performance data from these eleven markdown files.

The negative results from the exploratory globs are themselves valuable information. They tell the assistant that the performance data is not available as pre-compiled logs or aggregate summaries—it must be extracted from prose descriptions and markdown tables within the proposal documents. This shapes the reading strategy for subsequent rounds: the assistant will need to parse natural language descriptions, normalize units across documents, and reconcile potentially conflicting numbers from different sources.

The articles [1] and [2] examine this discovery phase in depth, analyzing the assistant's search strategy, the assumptions embedded in the glob patterns, and the significance of the negative results. As [1] notes, the assistant's three-tier search strategy—central document first, then per-phase proposals, then speculative log file search—reveals a sophisticated understanding of how engineering documentation tends to be organized.

The Ingestion Phase: Parallel Reading of Eleven Documents

With the data sources identified, the assistant moves to the ingestion phase ([msg 2]). The message is deceptively simple: a single sentence—"Good, I found all the files. Let me read the main project doc and all proposal files in parallel."—followed by eleven read tool calls, one for each file.

This message is the hinge point of the entire subagent session. It follows the discovery phase and precedes the analysis phase. The assistant's decision to read all files in parallel is a deliberate optimization strategy that reveals several layers of reasoning about the tool execution model.

In the round-based execution model used by opencode, multiple tool calls within a single round are dispatched simultaneously, but the assistant must wait for ALL results before producing the next round. This means the assistant cannot conditionally act on one file's contents while reading another within the same round. The assistant's decision to read all 11 files in parallel is valid because these reads are genuinely independent: reading one file does not depend on the contents of another. There is no conditional logic like "if file A contains X, then read file B." All files need to be read regardless of their contents.

The assistant is also making a trade-off between round count and context management. Reading all files in parallel minimizes the number of rounds (one round instead of up to 11), which reduces overall latency. However, it also means that all file contents arrive simultaneously in the next round, potentially overwhelming the context window with a large volume of text. The assistant judged that the benefits of parallelism outweighed the context management costs.

As [7] examines in detail, this "parallel read" pattern is a recurring motif in AI-assisted software engineering. It represents a completeness-oriented strategy: the assistant assumes that all proposal files may contain relevant data, and it is more efficient to read them all upfront than to risk missing information and having to make additional read calls later. The assistant optimizes for completeness rather than minimal data transfer, recognizing that the cost of an extra parallel read is low while the cost of missing critical information and needing an additional round is high.

The assistant's phrasing also reveals a prioritization hierarchy. It says "the main project doc and all proposal files," not "all the files I found." The main document (cuzk-project.md) is positioned as the primary source, with the proposal files as secondary. This hierarchy reflects the assistant's expectation that the main document contains the most comprehensive and authoritative data, while the proposal files provide supplementary detail. In practice, this hierarchy proves accurate: the main document contains the cumulative results table, while individual proposal files provide per-phase breakdowns.

The Gap-Filling Phase: Exhaustive Search for Supplementary Data

After ingesting the primary documents, the assistant enters a gap-filling phase spanning messages 3, 4, and 5. The assistant recognizes that the initial read of cuzk-project.md returned content only up to approximately line 1260, and the critical E2E test results section begins at line 1261. The assistant also recognizes that raw benchmark log files might exist outside the markdown documentation, providing more granular data than the summarized tables.

In [msg 3], the assistant reads the remainder of cuzk-project.md starting from line 1260, capturing the E2E Test Results section that reveals the Phase 2 baseline metrics (88.9s total, 54.x seconds synthesis) on the RTX 5070 Ti hardware. The assistant also searches for benchmark and phase files in the home directory root, finding none.

In [msg 4], the assistant broadens the search, looking for any .log files anywhere under the home directory and for files matching *total*impact* within the curio project directory. Both return "No files found."

In [msg 5], the assistant tries one more pattern: c2-total* in the curio directory. Again, no files found.

These three messages represent a methodical exhaustion of the space of plausible data sources. The assistant is testing specific hypotheses about where additional data might live: raw benchmark logs (.log files), aggregate summary files (*total*impact*, c2-total*), and files in the home directory root (*benchmark*, *phase*). Each negative result narrows the search space and confirms that the markdown documents are the sole source of performance information.

As [3], [4], and [5] examine in detail, these negative results are not failures—they are boundary markers that define the shape of available knowledge. By establishing that no raw log files exist, the assistant confirms that any analysis will be limited to summary statistics. Run-to-run variance, confidence intervals, and raw timing distributions cannot be reconstructed. This is an important caveat for any conclusions drawn from the data, and the assistant's documentation of the search process provides methodological transparency.

The gap-filling phase also reveals the assistant's meta-cognitive abilities. The assistant knows what it knows (the beginning of cuzk-project.md, the proposal file contents) and what it doesn't know (the E2E test results, any benchmark logs). It actively works to close that gap. This self-awareness—the ability to monitor one's own knowledge state and take corrective action—is a hallmark of effective problem-solving, whether in humans or machines.

The Synthesis Phase: A Comprehensive Performance Comparison Table

With all available data gathered, the assistant produces the final output ([msg 6]): a comprehensive structured performance comparison spanning eleven optimization phases. This message is the payoff of the entire data-gathering mission, and it is a masterclass in structured data presentation.

The message opens with a header identifying the hardware configuration (NVIDIA RTX 5070 Ti, AMD Threadripper PRO 7995WX, 512-754 GiB DDR5 RAM) and the workload (32 GiB PoRep C2 proof, 10 partitions, ~130M constraints each). This framing establishes that all measurements were taken on comparable hardware, making the phase-to-phase comparisons valid.

Each phase is then presented with a consistent structure: a brief description of the optimization, followed by a table of metrics. This consistency is the assistant's most important structural decision. By normalizing the presentation across phases—even when the original documentation used different units, different metrics, or different levels of detail—the assistant creates a directly comparable dataset.

The handling of incomplete data is particularly nuanced. Phase 1 (Multi-Type + Scheduling) is labeled "Design Only" with "Not separately benchmarked" for latency. Phase 4 (Compute Quick Wins) is also "Design Only" but includes estimated speedup ranges from the design document. Phase 10 (Two-Lock GPU Interlock) is "Design Only" but includes both Phase 9 measured values and Phase 10 expected values side by side. This distinction between "design only" and "implemented and measured" is crucial. The assistant does not fabricate data or conflate estimates with measurements. Instead, it clearly labels the nature of each phase's data, allowing the reader to distinguish between proven results and projected improvements.

The cumulative summary table at the end traces the entire optimization journey in a single view:

| After Phase | What Changed | Single-Proof Latency | Throughput (s/proof) | Peak RAM | GPU Util | Cumulative vs Baseline | |---|---|---|---|---|---|---| | Baseline | ffiselect child process | ~360s | ~360s | ~200+ GiB | ~24% | 1.0x | | Phase 0 | SRS residency | ~89s | ~89s | 203 GiB | -- | ~1.3x | | Phase 2 | Synth || GPU overlap | 88.9s | ~71s | 203 GiB | ~40% | ~1.27x | | Phase 3 | Cross-sector batching | 62.7s/proof | 62.3s | 360-420 GiB | -- | 1.42x | | Phase 5 W1 | PCE (synthesis 50.4→35.5s) | -- | -- | +25.7 GiB static | -- | 1.42x synth | | Phase 6 | Per-partition pipeline | ~38-41s | 66-72s | 27-54 GiB | ~96% | Worse standalone | | Phase 7 | Engine per-partition pipeline | ~59s | 50.7s | ~430 GiB | ~100% | -- | | Phase 8 | Dual-worker GPU interlock | 69.3s | 37.4s | ~430 GiB | 100% | 2.4x | | Phase 9 | Pinned DMA + deferred Pippenger | -- | 32.1s/41.3s | ~430 GiB | 48.5% silicon | 2.8x | | Phase 10 | Two-lock 3-worker (design) | -- | 28-34s (expected) | ~430 GiB | ~100% (target) | ~3.2x (est.) |

This table is the payoff of the entire exercise. It shows the progression from a 360-second baseline to an expected 28-34 second Phase 10—a greater than 10x improvement. The inclusion of "Cumulative vs Baseline" as a column makes the compounding effect of optimizations visible. The table also reveals the shifting bottleneck: from CPU synthesis (Phase 0-2) to GPU compute (Phase 3-7) to PCIe transfers (Phase 8-9) to CPU memory bandwidth (Phase 9-10).

As [6] examines in exhaustive detail, the message reveals not just what was achieved, but how the system's fundamental constraints evolved. The Phase 9 isolation throughput of 32.1s/proof is impressive, but the high-concurrency throughput of 41.3s/proof—and the crash at c=30, j=20 due to OOM—reveals that the system is now CPU memory-bandwidth-bound, not GPU-bound. The theoretical GPU-limited floor of 18.2s/proof (1.82s × 10 partitions) represents the remaining optimization headroom, but practical constraints from DDR5 contention are expected to limit actual performance to 28-34s even with the proposed Phase 10 improvements.

The Broader Significance: From Data Gathering to Decision Support

The subagent's work in this chunk did not exist in isolation. It was spawned from a parent session that was actively designing Phase 11—three interventions to reduce DDR5 memory bandwidth contention. The structured comparison table produced by the subagent served as the evidence base for these design decisions.

By tracing the optimization journey from Phase 0 through Phase 10, the table reveals several critical insights that directly inform Phase 11:

The system is no longer GPU-bound. Phase 8 achieved 100% GPU utilization, and Phase 9 cut GPU kernel time by 51%. Further GPU-side optimizations would yield diminishing returns.
The bottleneck has shifted to CPU memory bandwidth. Phase 9's per-partition timing shows that CPU-side metrics (prep_msm_ms, b_g2_msm_ms) actually worsened under concurrency due to DDR5 contention. This is the frontier that Phase 11 must address.
The theoretical ceiling is visible. The GPU-limited floor of 18.2s/proof provides a target, but the practical constraints of DDR5 bandwidth mean that achieving this ceiling would require fundamentally different approaches—perhaps multi-GPU configurations, algorithmic innovations, or hardware changes.
Memory footprint has grown dramatically. From ~200 GiB at baseline to ~430 GiB at Phase 9, the memory cost of parallelism is significant. Phase 11's interventions must balance throughput improvements against memory constraints. The subagent's work also demonstrates a methodological pattern that is broadly applicable to AI-assisted engineering analysis. The systematic progression from discovery to ingestion to gap-filling to synthesis is a template for any data-gathering mission. The assistant's explicit reasoning about its strategy, its handling of incomplete data, and its normalization of disparate sources into a consistent format are all practices that human analysts would recognize as best practices.

Conclusion

The seven-message arc of segment 28, chunk 0, is a microcosm of the broader optimization journey it documents. Just as the cuzk project's optimization campaign progressed through phases of discovery, implementation, measurement, and refinement, the assistant's data-gathering mission progressed through phases of file discovery, parallel ingestion, gap-filling search, and structured synthesis. Both journeys reveal the same underlying pattern: systematic exploration, methodical elimination of unknowns, and progressive refinement of understanding.

The assistant's final output—a comprehensive comparison table spanning eleven optimization phases—transforms scattered documentation into a coherent narrative of progressive optimization. It reveals how each phase shifted the bottleneck, what was achieved, and what remains to be done. For the parent session designing Phase 11, this table provided the evidence base needed to make informed decisions about where to invest engineering effort.

In the end, this chunk demonstrates that data archaeology is not a passive act of collection but an active process of construction. The assistant did not simply find and copy numbers—it normalized units, reconciled sources, distinguished measured from estimated results, and organized the data into a narrative arc. The result is not just a table but a story: the story of how a GPU-accelerated proof generation pipeline evolved from a 360-second baseline to a projected 28-second future, and of the engineering insights that made that journey possible.