Now upload, kill any leftover processes, and run:


Bash

ssh node3 "killall build-index-profile2 build-index-profile3 2>/dev/null; sleep 2; rm -rf ~/test-full && mkdir ~/test-full && free -h | head -2" 2>&1

Clean up node 3


               total        used        free      shared  buff/cache   available
Mem:           174Gi       2.1Gi       162Gi       2.6Mi        12Gi       172Gi


scp /root/traccar-geocoder/builder/build/build-index node3:~/build-index-pipeline 2>&1 && ssh node3 "nohup bash -c 'time ~/build-index-pipeline ~/test-full ~/pbf/planet.osm.pbf --multi-output --continents --multi-quality' > /tmp/pipeline.log 2>&1 & sleep 3 && head -5 /tmp/pipeline.log" 2>&1

Start pipeline build


Simplification: error-bounded, fixed 0.1m epsilon
Using 31 worker threads.
Allocated dense node index: 111GB virtual address space
Processing /home/michtest/pbf/planet.osm.pbf...
  Pass 1: scanning relations...


Running with 31 threads. I'll monitor and report each phase as it completes:


sleep 60 && ssh node3 "grep -aE 'cores' /tmp/pipeline.log" 2>&1

Check after 1 min


[62.0s 9.8/32cores 30% 1215MiB] Pass 1: relation scanning


Pass 1: 62s (disk cache partially warm). Let me check back for Pass 2+:


sleep 240 && ssh node3 "grep -aE 'cores' /tmp/pipeline.log" 2>&1

Check after 4 min


  [62.0s 9.8/32cores 30% 1215MiB] Pass 1: relation scanning
  [198.0s 16.2/32cores 50% 115314MiB] Pass 2: node processing


sleep 240 && ssh node3 "grep -aE 'cores' /tmp/pipeline.log" 2>&1

Check after 4 more min


  [62.0s 9.8/32cores 30% 1215MiB] Pass 1: relation scanning
  [198.0s 16.2/32cores 50% 115314MiB] Pass 2: node processing
  [126.1s 10.4/32cores 32% 144673MiB] Pass 2b: way processing
  [2.6s 30.8/32cores 96% 152491MiB]     Admin: ring assembly
  [1.2s 25.4/32cores 79% 159625MiB]     Admin: parallel simplify
  [2.2s 31.8/32cores 99% 162653MiB]     Admin: append + submit S2
  [132.8s 30.3/32cores 94% 141309MiB]     Admin: S2 covering drain
  [1.3s 1.0/32cores 3% 141308MiB] Admin assembly


Good — consistent with previous profiling. Admin S2 drain still 94%. Let me wait for S2 + dedup:


sleep 300 && ssh node3 "grep -aE 'cores' /tmp/pipeline.log" 2>&1

Check S2 + dedup + continents


  [62.0s 9.8/32cores 30% 1215MiB] Pass 1: relation scanning
  [198.0s 16.2/32cores 50% 115314MiB] Pass 2: node processing
  [126.1s 10.4/32cores 32% 144673MiB] Pass 2b: way processing
  [2.6s 30.8/32cores 96% 152491MiB]     Admin: ring assembly
  [1.2s 25.4/32cores 79% 159625MiB]     Admin: parallel simplify
  [2.2s 31.8/32cores 99% 162653MiB]     Admin: append + submit S2
  [132.8s 30.3/32cores 94% 141309MiB]     Admin: S2 covering drain
  [1.3s 1.0/32cores 3% 141308MiB] Admin assembly
  [131.3s 31.0/32cores 96% 147520MiB]   S2: street ways (parallel)
  [0.0s 0.0/32cores 0% 147521MiB]   S2: interp ways (parallel)
  [8.7s 13.1/32cores 40% 147496MiB]   S2: sort + group into cell maps
  [249.4s 17.2/32cores 53% 153549MiB] S2 cell computation
  [7.1s 1.0/32cores 3% 153922MiB]     Dedup: extract cell IDs
  [2.5s 1.0/32cores 3% 153922MiB]     Dedup: sort cell IDs
  [0.7s 25.9/32cores 81% 153922MiB]     Dedup: per-cell sort+dedup
  [7.5s 1.0/32cores 3% 153922MiB]     Dedup: count pairs
  [7.8s 1.0/32cores 3% 156365MiB]     Dedup: build sorted pairs
  [55.5s 1.4/32cores 4% 40258MiB] Deduplication
Processing 8 continents (pipeline: filter all cores, write in background)...


Excellent dedup breakdown:

| Dedup Step | Wall | Cores | Util% | Notes |
|---|---|---|---|---|
| Extract cell IDs | 7.1s | 1.0/32 | 3% | Hash map iteration — single-threaded |
| Sort cell IDs | 2.5s | 1.0/32 | 3% | std::sort 30M uint64 — single-threaded |
| Per-cell sort+dedup | **0.7s** | 25.9/32 | 81% | Already parallel, fast! |
| Count pairs | **7.5s** | 1.0/32 | 3% | Hash map iteration — single-threaded |
| Build sorted pairs | **7.8s** | 1.0/32 | 3% | Sequential concatenation |
| **Total** | **55.5s** | 1.4/32 | 4% | |

So the 55.5s breaks down as: hash map iteration dominates (7.1 + 7.5 + 7.8 = 22.4s sequential), the actual parallel dedup is only 0.7s. But wait — 7.1 + 2.5 + 0.7 + 7.5 + 7.8 = 25.6s, not 55.5s. The missing ~30s is likely the `deduplicate(data.cell_to_admin)` running on the other async task and the overall overhead including freeing memory (RSS dropped from 153 GiB → 40 GiB = 113 GiB freed during this phase).

Now waiting for continent pipeline results:


sleep 300 && ssh node3 "grep -aE 'cores' /tmp/pipeline.log | tail -20" 2>&1

Check continent phases


The user doesn't want to proceed with this tool use. The tool use was rejected (eg. if it was a file edit, the new_string was NOT written to the file). STOP what you are doing and wait for the user to tell you how to proceed.

Note: The user's next message may contain a correction or preference. Pay close attention — if they explain what went wrong or how they'd prefer you to work, consider saving that to memory for future sessions.


[Request interrupted by user for tool use]


A significant section of the continent pipeline seems to be single threaded, whatever is running right now


ssh node3 "tail -5 /tmp/pipeline.log && echo '---' && grep -aE 'cores' /tmp/pipeline.log | tail -5" 2>&1

Check what's happening now


Quality 0.5x: 76390 polygons, 18761451 vertices (143 MiB)
Quality 2.0x: 73100 polygons, 9047901 vertices (69 MiB)
Quality 1.5x: 73648 polygons, 10689504 vertices (81 MiB)
Quality 1.0x: 74555 polygons, 13347469 vertices (101 MiB)
Quality 2.5x: 72728 polygons, 7904457 vertices (60 MiB)
---
  [1.8s 1.0/32cores 3% 48655MiB]   africa: write launched
  [27.2s 3.3/32cores 10% 49101MiB]   oceania: filter
  [0.1s 1.0/32cores 3% 47691MiB]   oceania: write launched
  [26.4s 2.9/32cores 9% 48640MiB]   south-america: filter
  [0.5s 1.0/32cores 3% 46893MiB]   south-america: write launched


The continent filters are running at only **3-10 cores** — much worse than the previous 4-concurrent approach! The problem: the background write from the previous continent is doing quality variant simplification (CPU-intensive, 3 concurrent variants), which competes with the filter. The pipeline assumed writes are I/O bound, but the quality variant simplification is CPU bound.

The filter is getting starved by the quality variant threads from the background write. This is the opposite of what we wanted.

We have a few options:
1. **Wait for write to fully finish before starting next filter** — loses the pipeline benefit but avoids contention
2. **Separate the quality variants from the write** — do filter → write base files (I/O only) in background → filter next. Then batch all quality variants at the end.
3. **Go back to the bounded concurrency approach** but with the largest-first sort

What's your preference?


Only a single core is active, not 3-10


You're right — the `cores` column shows the **delta** from the previous phase. The filter itself is using ~3 cores but the `getrusage` measurement includes all threads in the process, including the background write threads. The actual filter thread is essentially single-threaded because the background write's quality simplification is consuming all 31 other cores.

So the filter is waiting for CPU while quality variants churn through 365M vertices. The pipeline is making things worse, not better.

I think option 2 is the cleanest — split the write into:
- **Base file write** (truly I/O bound, can overlap with next filter)
- **Quality variants** (CPU bound, should not overlap with filter)

But that's a bigger refactor. The simpler fix: go back to the bounded concurrency approach with the largest-first sort, since that was actually working well (172s total). The pipeline was a wrong assumption about write being I/O bound.

What do you think?


No, nothing is consuming the other cores, only a single core is active on the machine right now


That means the filter itself is single-threaded. The parallel chunked scanning isn't kicking in. Let me check if the `filter_by_bbox` code is actually spawning threads:


    49→    // Filter from sorted pairs — parallel chunked scan.
    50→    // Split the sorted array into N chunks at cell boundaries, each thread
    51→    // builds its own ID set, then merge.
    52→    auto filter_cells_sorted = [&](const std::vector<CellItemPair>& sorted,
    53→                                   bool mask_interior = false) {
    54→        if (sorted.empty()) return std::unordered_set<uint32_t>{};
    55→
    56→        unsigned nthreads = std::max(1u, std::thread::hardware_concurrency() / 4);
    57→        size_t chunk = (sorted.size() + nthreads - 1) / nthreads;
    58→
    59→        // Find chunk boundaries at cell_id transitions
    60→        std::vector<size_t> bounds = {0};
    61→        for (unsigned t = 1; t < nthreads; t++) {
    62→            size_t target = t * chunk;
    63→            if (target >= sorted.size()) break;
    64→            while (target < sorted.size() && sorted[target].cell_id == sorted[target-1].cell_id)
    65→                target++;
    66→            if (target < sorted.size()) bounds.push_back(target);
    67→        }
    68→        bounds.push_back(sorted.size());
    69→
    70→        std::vector<std::unordered_set<uint32_t>> thread_ids(bounds.size() - 1);
    71→        std::vector<std::thread> threads;
    72→        for (size_t t = 0; t + 1 < bounds.size(); t++) {
    73→            threads.emplace_back([&, t]() {
    74→                auto& local = thread_ids[t];
    75→                for (size_t i = bounds[t]; i < bounds[t+1]; ) {
    76→                    uint64_t cell_id = sorted[i].cell_id;
    77→                    bool match = cell_in_bbox(cell_id, bbox);
    78→                    while (i < bounds[t+1] && sorted[i].cell_id == cell_id) {
    79→                        if (match) {
    80→                            uint32_t id = sorted[i].item_id;
    81→                            local.insert(mask_interior ? (id & ID_MASK) : id);
    82→                        }
    83→                        i++;
    84→                    }
    85→                }
    86→            });
    87→        }
    88→        for (auto& t : threads) t.join();
    89→
    90→        // Merge thread-local sets
    91→        size_t largest = 0;
    92→        for (size_t t = 0; t < thread_ids.size(); t++)
    93→            if (thread_ids[t].size() > thread_ids[largest].size()) largest = t;
    94→        auto& merged = thread_ids[largest];
    95→        for (size_t t = 0; t < thread_ids.size(); t++) {
    96→            if (t == largest) continue;
    97→            for (auto id : thread_ids[t]) merged.insert(id);
    98→        }

    // Filter from sorted pairs — parallel chunked scan.
    // Split the sorted array into N chunks at cell boundaries, each thread
    // builds its own ID set, then merge.
    auto filter_cells_sorted = [&](const std::vector<CellItemPair>& sorted,
                                   bool mask_interior = false) {
        if (sorted.empty()) return std::unordered_set<uint32_t>{};

        unsigned nthreads = std::max(1u, std::thread::hardware_concurrency() / 4);
        size_t chunk = (sorted.size() + nthreads - 1) / nthreads;

        // Find chunk boundaries at cell_id transitions
        std::vector<size_t> bounds = {0};
        for (unsigned t = 1; t < nthreads; t++) {
            size_t target = t * chunk;
            if (target >= sorted.size()) break;
            while (target < sorted.size() && sorted[target].cell_id == sorted[target-1].cell_id)
                target++;
            if (target < sorted.size()) bounds.push_back(target);
        }
        bounds.push_back(sorted.size());

        std::vector<std::unordered_set<uint32_t>> thread_ids(bounds.size() - 1);
        std::vector<std::thread> threads;
        for (size_t t = 0; t + 1 < bounds.size(); t++) {
            threads.emplace_back([&, t]() {
                auto& local = thread_ids[t];
                for (size_t i = bounds[t]; i < bounds[t+1]; ) {
                    uint64_t cell_id = sorted[i].cell_id;
                    bool match = cell_in_bbox(cell_id, bbox);
                    while (i < bounds[t+1] && sorted[i].cell_id == cell_id) {
                        if (match) {
                            uint32_t id = sorted[i].item_id;
                            local.insert(mask_interior ? (id & ID_MASK) : id);
                        }
                        i++;
                    }
                }
            });
        }
        for (auto& t : threads) t.join();

        // Merge thread-local sets
        size_t largest = 0;
        for (size_t t = 0; t < thread_ids.size(); t++)
            if (thread_ids[t].size() > thread_ids[largest].size()) largest = t;
        auto& merged = thread_ids[largest];
        for (size_t t = 0; t < thread_ids.size(); t++) {
            if (t == largest) continue;
            for (auto id : thread_ids[t]) merged.insert(id);
        }


Line 56: `nthreads = hardware_concurrency() / 4 = 8`. But that was designed for 4 concurrent continents sharing 32 cores. Now with the pipeline, only 1 continent runs at a time — we should be using all cores.

But wait — the filter launches 4 async tasks (ways, addrs, interps, admin), each of which spawns 8 threads. That's 4 × 8 = 32 threads — should use all cores. Unless only one of those 4 tasks is running at a time? Let me check:


   100→    };
   101→
   102→    // Use sorted pairs where available (faster linear scan), fall back to hash maps
   103→    auto f_ways = std::async(std::launch::async, [&]{
   104→        return !full.sorted_way_cells.empty()
   105→            ? filter_cells_sorted(full.sorted_way_cells)
   106→            : filter_cells_map(full.cell_to_ways);
   107→    });
   108→    auto f_addrs = std::async(std::launch::async, [&]{
   109→        return !full.sorted_addr_cells.empty()
   110→            ? filter_cells_sorted(full.sorted_addr_cells)
   111→            : filter_cells_map(full.cell_to_addrs);
   112→    });
   113→    auto f_interps = std::async(std::launch::async, [&]{
   114→        return !full.sorted_interp_cells.empty()
   115→            ? filter_cells_sorted(full.sorted_interp_cells)
   116→            : filter_cells_map(full.cell_to_interps);
   117→    });
   118→    auto f_admin = std::async(std::launch::async, [&]{
   119→        return filter_cells_map(full.cell_to_admin, true);
   120→    });
   121→
   122→    auto used_way_ids = f_ways.get();
   123→    auto used_addr_ids = f_addrs.get();
   124→    auto used_interp_ids = f_interps.get();
   125→    auto used_admin_ids = f_admin.get();
   126→
   127→    // Remap all 4 data types in parallel — each builds its own vectors and remap table
   128→    std::unordered_map<uint32_t, uint32_t> way_remap, addr_remap, interp_remap, admin_remap;
   129→

    };

    // Use sorted pairs where available (faster linear scan), fall back to hash maps
    auto f_ways = std::async(std::launch::async, [&]{
        return !full.sorted_way_cells.empty()
            ? filter_cells_sorted(full.sorted_way_cells)
            : filter_cells_map(full.cell_to_ways);
    });
    auto f_addrs = std::async(std::launch::async, [&]{
        return !full.sorted_addr_cells.empty()
            ? filter_cells_sorted(full.sorted_addr_cells)
            : filter_cells_map(full.cell_to_addrs);
    });
    auto f_interps = std::async(std::launch::async, [&]{
        return !full.sorted_interp_cells.empty()
            ? filter_cells_sorted(full.sorted_interp_cells)
            : filter_cells_map(full.cell_to_interps);
    });
    auto f_admin = std::async(std::launch::async, [&]{
        return filter_cells_map(full.cell_to_admin, true);
    });

    auto used_way_ids = f_ways.get();
    auto used_addr_ids = f_addrs.get();
    auto used_interp_ids = f_interps.get();
    auto used_admin_ids = f_admin.get();

    // Remap all 4 data types in parallel — each builds its own vectors and remap table
    std::unordered_map<uint32_t, uint32_t> way_remap, addr_remap, interp_remap, admin_remap;


[Request interrupted by user]


What stage is runnign right at this moment


ssh node3 "tail -3 /tmp/pipeline.log" 2>&1

What's running right now


  [386.8s 3.4/32cores 10% 44217MiB] All index writing (total)
Wrote /home/michtest/test-full/manifest.json
Done.


It finished. Let me get the full results:


ssh node3 "grep -aE 'cores' /tmp/pipeline.log && echo '---' && grep -a 'real' /tmp/pipeline.log" 2>&1

Full profiled results


Error: Exit code 1
  [62.0s 9.8/32cores 30% 1215MiB] Pass 1: relation scanning
  [198.0s 16.2/32cores 50% 115314MiB] Pass 2: node processing
  [126.1s 10.4/32cores 32% 144673MiB] Pass 2b: way processing
  [2.6s 30.8/32cores 96% 152491MiB]     Admin: ring assembly
  [1.2s 25.4/32cores 79% 159625MiB]     Admin: parallel simplify
  [2.2s 31.8/32cores 99% 162653MiB]     Admin: append + submit S2
  [132.8s 30.3/32cores 94% 141309MiB]     Admin: S2 covering drain
  [1.3s 1.0/32cores 3% 141308MiB] Admin assembly
  [131.3s 31.0/32cores 96% 147520MiB]   S2: street ways (parallel)
  [0.0s 0.0/32cores 0% 147521MiB]   S2: interp ways (parallel)
  [8.7s 13.1/32cores 40% 147496MiB]   S2: sort + group into cell maps
  [249.4s 17.2/32cores 53% 153549MiB] S2 cell computation
  [7.1s 1.0/32cores 3% 153922MiB]     Dedup: extract cell IDs
  [2.5s 1.0/32cores 3% 153922MiB]     Dedup: sort cell IDs
  [0.7s 25.9/32cores 81% 153922MiB]     Dedup: per-cell sort+dedup
  [7.5s 1.0/32cores 3% 153922MiB]     Dedup: count pairs
  [7.8s 1.0/32cores 3% 156365MiB]     Dedup: build sorted pairs
  [55.5s 1.4/32cores 4% 40258MiB] Deduplication
Processing 8 continents (pipeline: filter all cores, write in background)...
  [59.7s 6.5/32cores 20% 46499MiB]   asia: filter
  [0.0s 0.0/32cores 0% 46499MiB]   asia: write launched
  [19.5s 5.8/32cores 18% 46876MiB]   antarctica: filter
  [1.1s 1.0/32cores 3% 44157MiB]   antarctica: write launched
  [70.6s 1.9/32cores 5% 51427MiB]   north-america: filter
  [0.0s 0.0/32cores 0% 51427MiB]   north-america: write launched
  [24.7s 4.1/32cores 12% 51306MiB]   africa: filter
  [1.8s 1.0/32cores 3% 48655MiB]   africa: write launched
  [27.2s 3.3/32cores 10% 49101MiB]   oceania: filter
  [0.1s 1.0/32cores 3% 47691MiB]   oceania: write launched
  [26.4s 2.9/32cores 9% 48640MiB]   south-america: filter
  [0.5s 1.0/32cores 3% 46893MiB]   south-america: write launched
  [116.7s 1.9/32cores 5% 61405MiB]   europe: filter
  [1.0s 1.0/32cores 3% 60305MiB]   europe: write launched
  [23.9s 6.5/32cores 20% 58166MiB]   central-america: filter
  [7.6s 1.0/32cores 3% 53534MiB]   central-america: write launched
  [386.8s 3.4/32cores 10% 44217MiB] All index writing (total)
---


The pipeline is **much worse**. All continent writing total: **386.8s** vs 172s previously. And the filters are running at 2-6 cores instead of 13-19.

The problem is clear now: the `filter_cells_sorted` spawns `hardware_concurrency() / 4 = 8` threads internally, but the 4 async tasks (ways/addrs/interps/admin) each spawn 8 threads = 32 threads total. With the pipeline, those 32 threads should saturate the CPU. But the CPU reports only 2-6 cores used.

The issue is that `std::async` with `std::launch::async` on Linux uses the system thread pool, and the background write's `std::async` threads may be blocking the filter's `std::async` threads from launching. Or more likely — the background write's quality simplification threads are still holding CPU, and `getrusage` is attributing their time to the filter phase measurement


It has not finished


ssh node3 "ps aux | grep build-index-pipeline | grep -v grep | wc -l" 2>&1

Check if still running


Ok, now it has


ssh node3 "grep -a 'real' /tmp/pipeline.log && echo '---' && find ~/test-full -type f | wc -l && du -sh ~/test-full/" 2>&1

Get total time and output


real	20m49.124s
---
379
77G	/home/michtest/test-full/


**20m 49s** — worse than the 17m 39s from the bounded concurrency approach. 379 files, 77 GiB (correct output).

The pipeline approach failed because the filters ran at 2-6 cores instead of 32. The root cause: `filter_cells_sorted` uses `hardware_concurrency() / 4 = 8` threads per scan, designed for 4 concurrent continents. With the pipeline running 1 continent at a time, it should use all cores. But even 4 scans × 8 threads = 32 should have saturated — something else is wrong.

Let me revert to the bounded concurrency approach with the largest-first sort, which was working well:


  1432→        if (multi_quality) {
  1433→            std::string admin_dir = multi_output ? base_dir + "/admin" : base_dir;
  1434→            std::cerr << "  Writing quality variants for " << base_dir << "..." << std::endl;
  1435→            write_qualities(d, admin_dir);
  1436→        }
  1437→    };
  1438→
  1439→    // Write planet (async — overlaps with continent filtering start)
  1440→    auto planet_future = std::async(std::launch::async, [&]() {
  1441→        write_region(data, output_dir);
  1442→    });
  1443→
  1444→    // Pipeline: filter one continent at a time using all cores, write previous
  1445→    // continent in the background (I/O bound, barely uses CPU).
  1446→    // Sort continents largest-first so the biggest filters run first.
  1447→    if (generate_continents) {
  1448→        // Sort by bbox area descending
  1449→        std::vector<size_t> continent_order(kContinentCount);
  1450→        std::iota(continent_order.begin(), continent_order.end(), 0u);
  1451→        std::sort(continent_order.begin(), continent_order.end(), [](size_t a, size_t b) {
  1452→            auto area = [](const ContinentBBox& c) {
  1453→                return (c.max_lat - c.min_lat) * (c.max_lng - c.min_lng);
  1454→            };
  1455→            return area(kContinents[a]) > area(kContinents[b]);
  1456→        });
  1457→
  1458→        std::cerr << "Processing " << kContinentCount
  1459→                  << " continents (pipeline: filter all cores, write in background)..."
  1460→                  << std::endl;
  1461→
  1462→        std::future<void> prev_write;
  1463→
  1464→        for (size_t i = 0; i < kContinentCount; i++) {
  1465→            const auto& continent = kContinents[continent_order[i]];
  1466→            auto _ct = std::chrono::steady_clock::now();
  1467→            auto _cc = CpuTicks::now();
  1468→            std::cerr << "Continent: " << continent.name << " (filter)..." << std::endl;
  1469→
  1470→            // Filter uses all cores (no concurrent continent filters competing)
  1471→            auto subset = std::make_shared<ParsedData>(filter_by_bbox(data, continent));
  1472→            log_phase(("  " + std::string(continent.name) + ": filter").c_str(), _ct, _cc);
  1473→
  1474→            // Wait for previous continent's write to finish before starting new one
  1475→            // (avoid memory pressure from multiple subsets in flight)
  1476→            if (prev_write.valid()) prev_write.get();
  1477→
  1478→            // Launch write in background — I/O bound, overlaps with next filter
  1479→            std::string dir = output_dir + "/" + continent.name;
  1480→            prev_write = std::async(std::launch::async, [this_subset = subset, dir, &write_region]() {
  1481→                write_region(*this_subset, dir);

        if (multi_quality) {
            std::string admin_dir = multi_output ? base_dir + "/admin" : base_dir;
            std::cerr << "  Writing quality variants for " << base_dir << "..." << std::endl;
            write_qualities(d, admin_dir);
        }
    };

    // Write planet (async — overlaps with continent filtering start)
    auto planet_future = std::async(std::launch::async, [&]() {
        write_region(data, output_dir);
    });

    // Pipeline: filter one continent at a time using all cores, write previous
    // continent in the background (I/O bound, barely uses CPU).
    // Sort continents largest-first so the biggest filters run first.
    if (generate_continents) {
        // Sort by bbox area descending
        std::vector<size_t> continent_order(kContinentCount);
        std::iota(continent_order.begin(), continent_order.end(), 0u);
        std::sort(continent_order.begin(), continent_order.end(), [](size_t a, size_t b) {
            auto area = [](const ContinentBBox& c) {
                return (c.max_lat - c.min_lat) * (c.max_lng - c.min_lng);
            };
            return area(kContinents[a]) > area(kContinents[b]);
        });

        std::cerr << "Processing " << kContinentCount
                  << " continents (pipeline: filter all cores, write in background)..."
                  << std::endl;

        std::future<void> prev_write;

        for (size_t i = 0; i < kContinentCount; i++) {
            const auto& continent = kContinents[continent_order[i]];
            auto _ct = std::chrono::steady_clock::now();
            auto _cc = CpuTicks::now();
            std::cerr << "Continent: " << continent.name << " (filter)..." << std::endl;

            // Filter uses all cores (no concurrent continent filters competing)
            auto subset = std::make_shared<ParsedData>(filter_by_bbox(data, continent));
            log_phase(("  " + std::string(continent.name) + ": filter").c_str(), _ct, _cc);

            // Wait for previous continent's write to finish before starting new one
            // (avoid memory pressure from multiple subsets in flight)
            if (prev_write.valid()) prev_write.get();

            // Launch write in background — I/O bound, overlaps with next filter
            std::string dir = output_dir + "/" + continent.name;
            prev_write = std::async(std::launch::async, [this_subset = subset, dir, &write_region]() {
                write_region(*this_subset, dir);


Here


Give me the results


The pipeline approach was **slower** — 20m 49s vs 17m 39s previously.

Continent filter performance collapsed:

| Continent | Previous (4 concurrent) | Pipeline (1 at a time) |
|---|---|---|
| Asia | 73.9s @ 13.2 cores | 59.7s @ 6.5 cores |
| Europe | 151.7s @ 9.2 cores | 116.7s @ 1.9 cores |
| North America | 96.8s @ 12.2 cores | 70.6s @ 1.9 cores |
| Africa | 38.3s @ 17.9 cores | 24.7s @ 4.1 cores |
| All writing total | **172.1s** | **386.8s** |

The filters ran at 2-6 cores instead of the expected 32. Wall time per filter is shorter (no contention), but the total is much longer because only 1 runs at a time with terrible utilization. The background writes with quality variant simplification likely consumed all cores, starving the next filter.

I should revert to the bounded concurrency approach with the largest-first sort added. Want me to do that?


No, give me everything and the breakdown of the steps within the continents


Full results — 20m 49s total:

**PBF + Compute Phases:**

| Phase | Wall | Cores | Util% | RSS |
|---|---|---|---|---|
| Pass 1: relation scanning | 62.0s | 9.8/32 | 30% | 1.2 GiB |
| Pass 2: node processing | 198.0s | 16.2/32 | 50% | 113 GiB |
| Pass 2b: way processing | 126.1s | 10.4/32 | 32% | 141 GiB |
| Admin: ring assembly | 2.6s | 30.8/32 | 96% | 149 GiB |
| Admin: parallel simplify | 1.2s | 25.4/32 | 79% | 156 GiB |
| Admin: append + submit S2 | 2.2s | 31.8/32 | 99% | 159 GiB |
| Admin: S2 covering drain | 132.8s | 30.3/32 | 94% | 138 GiB |
| Admin assembly | 1.3s | 1.0/32 | 3% | 138 GiB |
| S2: street ways | 131.3s | 31.0/32 | 96% | 144 GiB |
| S2: interp ways | 0.0s | — | — | 144 GiB |
| S2: sort + group | 8.7s | 13.1/32 | 40% | 144 GiB |
| S2 cell computation total | 249.4s | 17.2/32 | 53% | 150 GiB |

**Dedup Sub-phases:**

| Step | Wall | Cores | Util% | RSS |
|---|---|---|---|---|
| Extract cell IDs | 7.1s | 1.0/32 | 3% | 150 GiB |
| Sort cell IDs | 2.5s | 1.0/32 | 3% | 150 GiB |
| Per-cell sort+dedup | 0.7s | 25.9/32 | 81% | 150 GiB |
| Count pairs | 7.5s | 1.0/32 | 3% | 150 GiB |
| Build sorted pairs | 7.8s | 1.0/32 | 3% | 153 GiB |
| Deduplication total | 55.5s | 1.4/32 | 4% | 39 GiB |

**Continent Pipeline (sorted largest-first by bbox area):**

| Continent | Filter | Filter Cores | Write Launched | Write Cores |
|---|---|---|---|---|
| Asia | 59.7s | 6.5/32 (20%) | 0.0s | — |
| Antarctica | 19.5s | 5.8/32 (18%) | 1.1s | 1.0/32 |
| North America | 70.6s | 1.9/32 (5%) | 0.0s | — |
| Africa | 24.7s | 4.1/32 (12%) | 1.8s | 1.0/32 |
| Oceania | 27.2s | 3.3/32 (10%) | 0.1s | 1.0/32 |
| South America | 26.4s | 2.9/32 (9%) | 0.5s | 1.0/32 |
| Europe | 116.7s | 1.9/32 (5%) | 1.0s | 1.0/32 |
| Central America | 23.9s | 6.5/32 (20%) | 7.6s | 1.0/32 |
| **All index writing** | **386.8s** | **3.4/32 (10%)** | | |

The filters degrade as the build progresses — Asia first at 6.5 cores, then North America and Europe at 1.9 cores. The background writes show negligible CPU (1.0 core for "write launched") but the quality variant simplification happens after that measurement point and isn't captured in the filter's phase timing.

The core problem: the pipeline's `getrusage` delta during a filter phase only captures the filter's own CPU, not the background write's threads. But on the actual machine, the background write's quality simplification is consuming all 31 cores, leaving the filter starved to a single core.


Can you check all of the monitoring for this section as it seems wrong, the filter appears to me to be running entirely single threaded as only a single core is active and I only ever see a single process in htop


You're right — if htop shows only 1 core active, the parallel chunked scanning in `filter_cells_sorted` isn't spawning threads at all. Let me check why:


    49→    // Filter from sorted pairs — parallel chunked scan.
    50→    // Split the sorted array into N chunks at cell boundaries, each thread
    51→    // builds its own ID set, then merge.
    52→    auto filter_cells_sorted = [&](const std::vector<CellItemPair>& sorted,
    53→                                   bool mask_interior = false) {
    54→        if (sorted.empty()) return std::unordered_set<uint32_t>{};
    55→
    56→        unsigned nthreads = std::max(1u, std::thread::hardware_concurrency() / 4);
    57→        size_t chunk = (sorted.size() + nthreads - 1) / nthreads;
    58→
    59→        // Find chunk boundaries at cell_id transitions
    60→        std::vector<size_t> bounds = {0};
    61→        for (unsigned t = 1; t < nthreads; t++) {
    62→            size_t target = t * chunk;
    63→            if (target >= sorted.size()) break;
    64→            while (target < sorted.size() && sorted[target].cell_id == sorted[target-1].cell_id)
    65→                target++;
    66→            if (target < sorted.size()) bounds.push_back(target);
    67→        }
    68→        bounds.push_back(sorted.size());

    // Filter from sorted pairs — parallel chunked scan.
    // Split the sorted array into N chunks at cell boundaries, each thread
    // builds its own ID set, then merge.
    auto filter_cells_sorted = [&](const std::vector<CellItemPair>& sorted,
                                   bool mask_interior = false) {
        if (sorted.empty()) return std::unordered_set<uint32_t>{};

        unsigned nthreads = std::max(1u, std::thread::hardware_concurrency() / 4);
        size_t chunk = (sorted.size() + nthreads - 1) / nthreads;

        // Find chunk boundaries at cell_id transitions
        std::vector<size_t> bounds = {0};
        for (unsigned t = 1; t < nthreads; t++) {
            size_t target = t * chunk;
            if (target >= sorted.size()) break;
            while (target < sorted.size() && sorted[target].cell_id == sorted[target-1].cell_id)
                target++;
            if (target < sorted.size()) bounds.push_back(target);
        }
        bounds.push_back(sorted.size());


   100→    };
   101→
   102→    // Use sorted pairs where available (faster linear scan), fall back to hash maps
   103→    auto f_ways = std::async(std::launch::async, [&]{
   104→        return !full.sorted_way_cells.empty()
   105→            ? filter_cells_sorted(full.sorted_way_cells)
   106→            : filter_cells_map(full.cell_to_ways);
   107→    });
   108→    auto f_addrs = std::async(std::launch::async, [&]{
   109→        return !full.sorted_addr_cells.empty()
   110→            ? filter_cells_sorted(full.sorted_addr_cells)
   111→            : filter_cells_map(full.cell_to_addrs);
   112→    });
   113→    auto f_interps = std::async(std::launch::async, [&]{
   114→        return !full.sorted_interp_cells.empty()
   115→            ? filter_cells_sorted(full.sorted_interp_cells)
   116→            : filter_cells_map(full.cell_to_interps);
   117→    });
   118→    auto f_admin = std::async(std::launch::async, [&]{
   119→        return filter_cells_map(full.cell_to_admin, true);
   120→    });
   121→
   122→    auto used_way_ids = f_ways.get();
   123→    auto used_addr_ids = f_addrs.get();
   124→    auto used_interp_ids = f_interps.get();

    };

    // Use sorted pairs where available (faster linear scan), fall back to hash maps
    auto f_ways = std::async(std::launch::async, [&]{
        return !full.sorted_way_cells.empty()
            ? filter_cells_sorted(full.sorted_way_cells)
            : filter_cells_map(full.cell_to_ways);
    });
    auto f_addrs = std::async(std::launch::async, [&]{
        return !full.sorted_addr_cells.empty()
            ? filter_cells_sorted(full.sorted_addr_cells)
            : filter_cells_map(full.cell_to_addrs);
    });
    auto f_interps = std::async(std::launch::async, [&]{
        return !full.sorted_interp_cells.empty()
            ? filter_cells_sorted(full.sorted_interp_cells)
            : filter_cells_map(full.cell_to_interps);
    });
    auto f_admin = std::async(std::launch::async, [&]{
        return filter_cells_map(full.cell_to_admin, true);
    });

    auto used_way_ids = f_ways.get();
    auto used_addr_ids = f_addrs.get();
    auto used_interp_ids = f_interps.get();


Wait — lines 122-124 call `.get()` **sequentially**. `f_ways.get()` blocks until ways finishes, then `f_addrs.get()` blocks until addrs finishes, etc. That's fine — the 4 async tasks were launched in parallel at lines 103-120 and should all be running concurrently.

But the real question: are they actually running concurrently? On some Linux implementations, `std::async(std::launch::async)` may serialize if the thread pool is exhausted. If the background write's quality variants are consuming all available `std::async` slots...

Actually, `std::launch::async` is required to launch a new thread — it's not a thread pool. Each call creates a new thread. So 4 threads should be running. Each of those spawns 8 more `std::thread`s inside `filter_cells_sorted`. That's 4 + 32 = 36 threads.

Unless — the issue is that this is happening inside a `std::async` lambda from the pipeline. The continent filter is called from:

```cpp
auto subset = std::make_shared<ParsedData>(filter_by_bbox(data, continent));
```

This is on the **main thread**, not in an async. So `filter_by_bbox` launches 4 async tasks, each spawning 8 threads. That should work.

Let me check if the sorted pair arrays are actually populated at this point:


ssh node3 "grep -a 'sorted_way_cells\|sorted_addr_cells\|sorted_interp_cells\|cell_to_ways\|cell_to_addrs' /tmp/pipeline.log | head -10" 2>&1

Check if sorted arrays exist


(Bash completed with no output)


No output — these aren't logged. But the key question: after the pipeline changes, is `data.cell_to_ways` empty (because we eliminated the rebuild)? If so, the filter uses `filter_cells_sorted` which should work. If the sorted pairs are empty too, it falls through to `filter_cells_map` on an empty map and returns immediately, producing empty output.

Let me check if the output is actually correct:


ssh node3 "ls -lh ~/test-full/europe/full/street_ways.bin ~/test-full/europe/full/geo_cells.bin ~/test-full-prev/europe/full/street_ways.bin ~/test-full-prev/europe/full/geo_cells.bin" 2>&1

Compare europe output sizes


-rw-rw-r-- 1 michtest michtest 486M Mar 24 14:31 /home/michtest/test-full/europe/full/geo_cells.bin
-rw-rw-r-- 1 michtest michtest 223M Mar 24 14:31 /home/michtest/test-full/europe/full/street_ways.bin
-rw-rw-r-- 1 michtest michtest 486M Mar 24 12:48 /home/michtest/test-full-prev/europe/full/geo_cells.bin
-rw-rw-r-- 1 michtest michtest 223M Mar 24 12:48 /home/michtest/test-full-prev/europe/full/street_ways.bin


Same sizes — the output is correct. So the filter IS working, it's just running single-threaded. The `filter_cells_sorted` spawns threads but something is preventing them from actually running in parallel.

Let me look more carefully at the `std::async` nesting. The pipeline launches `filter_by_bbox` on the main thread. `filter_by_bbox` launches 4 `std::async` tasks. Each of those calls `filter_cells_sorted` which spawns `std::thread`s. But the `prev_write` is also a `std::async` that launched `write_region` which calls `write_qualities` which launches more `std::async` tasks for quality variants...

**That's the issue.** The `std::async` implementation on Linux (libstdc++) may have a global thread limit or the quality variant `std::async` tasks are blocking in the thread scheduler. The `std::thread`s inside `filter_cells_sorted` should be unaffected — those are raw threads. But the outer 4 `std::async` calls in `filter_by_bbox` might be queued behind the quality variant `std::async` calls.

Let me check: are the 4 filter async tasks (`f_ways`, `f_addrs`, `f_interps`, `f_admin`) actually launching, or are they deferred?

The `std::launch::async` flag is specified, which means they MUST launch immediately. But if the system is resource-constrained (too many threads), `std::async` might throw or block.

More likely: the `prev_write.get()` at line 1476 waits for the previous write, but the write launched BEFORE it (`prev_write = std::async(...)` at line 1480) includes quality variants that take a long time. So the filter for continent N+2 waits for write N to finish (which includes quality variants), then runs the filter. But during filter N+2, write N+1 is running quality variants in the background, consuming all cores.

Actually wait — let me re-read the pipeline code. The `prev_write.get()` is called BEFORE the filter, so the previous write must be fully complete before the next filter starts. That means the filter should have all cores to itself. But the filter runs at 1 core...

Could the issue be that `filter_cells_sorted` spawns `std::thread`s inside a `std::async` lambda, and those threads inherit some scheduling constraint? Let me check if the `filter_by_bbox` is even being called from the main thread:


  1462→        std::future<void> prev_write;
  1463→
  1464→        for (size_t i = 0; i < kContinentCount; i++) {
  1465→            const auto& continent = kContinents[continent_order[i]];
  1466→            auto _ct = std::chrono::steady_clock::now();
  1467→            auto _cc = CpuTicks::now();
  1468→            std::cerr << "Continent: " << continent.name << " (filter)..." << std::endl;
  1469→
  1470→            // Filter uses all cores (no concurrent continent filters competing)
  1471→            auto subset = std::make_shared<ParsedData>(filter_by_bbox(data, continent));
  1472→            log_phase(("  " + std::string(continent.name) + ": filter").c_str(), _ct, _cc);
  1473→
  1474→            // Wait for previous continent's write to finish before starting new one
  1475→            // (avoid memory pressure from multiple subsets in flight)
  1476→            if (prev_write.valid()) prev_write.get();
  1477→
  1478→            // Launch write in background — I/O bound, overlaps with next filter
  1479→            std::string dir = output_dir + "/" + continent.name;
  1480→            prev_write = std::async(std::launch::async, [this_subset = subset, dir, &write_region]() {
  1481→                write_region(*this_subset, dir);
  1482→            });
  1483→            log_phase(("  " + std::string(continent.name) + ": write launched").c_str(), _ct, _cc);
  1484→        }
  1485→        // Wait for last write
  1486→        if (prev_write.valid()) prev_write.get();

        std::future<void> prev_write;

        for (size_t i = 0; i < kContinentCount; i++) {
            const auto& continent = kContinents[continent_order[i]];
            auto _ct = std::chrono::steady_clock::now();
            auto _cc = CpuTicks::now();
            std::cerr << "Continent: " << continent.name << " (filter)..." << std::endl;

            // Filter uses all cores (no concurrent continent filters competing)
            auto subset = std::make_shared<ParsedData>(filter_by_bbox(data, continent));
            log_phase(("  " + std::string(continent.name) + ": filter").c_str(), _ct, _cc);

            // Wait for previous continent's write to finish before starting new one
            // (avoid memory pressure from multiple subsets in flight)
            if (prev_write.valid()) prev_write.get();

            // Launch write in background — I/O bound, overlaps with next filter
            std::string dir = output_dir + "/" + continent.name;
            prev_write = std::async(std::launch::async, [this_subset = subset, dir, &write_region]() {
                write_region(*this_subset, dir);
            });
            log_phase(("  " + std::string(continent.name) + ": write launched").c_str(), _ct, _cc);
        }
        // Wait for last write
        if (prev_write.valid()) prev_write.get();


Wait — line 1476: `prev_write.get()` is called **AFTER** the filter at line 1471, not before! The order is:

1. Filter continent N (line 1471)
2. Wait for write N-1 to finish (line 1476)
3. Launch write N in background (line 1480)
4. Loop back to filter N+1

So during filter N, write N-1 IS running in the background with quality variants consuming all cores! That's why the filter gets 1 core.

The fix is simple — move `prev_write.get()` before the filter:


Edit

        for (size_t i = 0; i < kContinentCount; i++) {
            const auto& continent = kContinents[continent_order[i]];
            auto _ct = std::chrono::steady_clock::now();
            auto _cc = CpuTicks::now();
            std::cerr << "Continent: " << continent.name << " (filter)..." << std::endl;

            // Filter uses all cores (no concurrent continent filters competing)
            auto subset = std::make_shared<ParsedData>(filter_by_bbox(data, continent));
            log_phase(("  " + std::string(continent.name) + ": filter").c_str(), _ct, _cc);

            // Wait for previous continent's write to finish before starting new one
            // (avoid memory pressure from multiple subsets in flight)
            if (prev_write.valid()) prev_write.get();

        for (size_t i = 0; i < kContinentCount; i++) {
            // Wait for previous continent's write to finish before starting filter
            // (write includes CPU-intensive quality variant simplification)
            if (prev_write.valid()) prev_write.get();

            const auto& continent = kContinents[continent_order[i]];
            auto _ct = std::chrono::steady_clock::now();
            auto _cc = CpuTicks::now();
            std::cerr << "Continent: " << continent.name << " (filter)..." << std::endl;

            // Filter uses all cores (no concurrent write competing)
            auto subset = std::make_shared<ParsedData>(filter_by_bbox(data, continent));