From 18616019dc9c73c3c5b561cb0ff6fe8c9c1929e8 Mon Sep 17 00:00:00 2001 From: zz_y Date: Mon, 5 Oct 2026 13:59:31 +0000 Subject: [PATCH 1/2] =?UTF-8?q?Saturation=20curves=20at=20label=20cardinal?= =?UTF-8?q?ities=201e1=E2=80=931e6=20for=20the=20synthetic=20planner=20wor?= =?UTF-8?q?kload?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Measure frequency and top-k saturation curves at K = 10, 100, 1e4 and 1e6 (theta 0..2, N up to 1e8, 3 seeds) and top-k accuracy after merging 4/16/64 shards, for the synthetic PromQL workload of ProjectASAP/ASAPQuery#777. Commit the summary CSVs, the N_sat tables and the scripts that finish and check an interrupted cost phase. Cost results are pending. Co-Authored-By: Claude Opus 5.5 --- .../synthetic_cardinalities/README.md | 291 + .../synthetic_cardinalities/saturation.csv | 481 + .../saturation_curve.csv | 10081 ++++++++++++++ .../saturation_merge_curve.csv | 10881 ++++++++++++++++ scripts/check_parallel_costs.py | 64 + scripts/complete_saturation_costs.py | 97 + scripts/summarize_synthetic_curves.py | 83 + 7 files changed, 21978 insertions(+) create mode 100644 docs/figures/saturation/synthetic_cardinalities/README.md create mode 100644 docs/figures/saturation/synthetic_cardinalities/saturation.csv create mode 100644 docs/figures/saturation/synthetic_cardinalities/saturation_curve.csv create mode 100644 docs/figures/saturation/synthetic_cardinalities/saturation_merge_curve.csv create mode 100644 scripts/check_parallel_costs.py create mode 100644 scripts/complete_saturation_costs.py create mode 100644 scripts/summarize_synthetic_curves.py diff --git a/docs/figures/saturation/synthetic_cardinalities/README.md b/docs/figures/saturation/synthetic_cardinalities/README.md new file mode 100644 index 00000000..74dbb15d --- /dev/null +++ b/docs/figures/saturation/synthetic_cardinalities/README.md @@ -0,0 +1,291 @@ +# Saturation curves at the synthetic workload's label cardinalities + +The synthetic PromQL workload of the AutoSketch vs. ASAP planner evaluation +(ProjectASAP/ASAPQuery#777, plan §6) varies `card(label_0)` over 1e1–1e6. +The saturation grid of #130 measured K ∈ {1e3, 1e5, 1e7} only. These runs +add K ∈ {10, 100, 1e4, 1e6} for the frequency and top-k families, by +measurement rather than interpolation in K. Quantile (Pareto) curves do not +depend on K and come from #130/#131 unchanged. + +## Results (accuracy phase) + +- **K ≤ 100 saturates early:** every point reaches `N_sat` between 1e3 and 6e4. +- **Large K with low skew may never saturate:** at K = 1e4 and 1e6 with + θ ≤ 0.5, many CMS, CountSketch and top-k points are still moving at + N = 1e8 ("never" below: 35 of 240 points at K ≥ 1e4). Their `N_sat` is a + lower bound, and consumers flag them. +- **Top-k merging loses precision at large K:** merged/single precision@k + at N = 1e7 drops to 0.50 (rows=3 cols=16384, θ = 0, K = 1e4) and to + 0.82–0.90 at K = 1e6 with θ ≤ 1.5. CMS and CountSketch merge exactly (#131), + so they have no merge runs here. + +`N_sat` uses #130's rule (tolerance 0.10, plateau tail 3, 3 seeds). "never" = +not saturated by the largest N. + +| sketch | config | θ=0 K=1e+01 | θ=0 K=1e+02 | θ=0 K=1e+04 | θ=0 K=1e+06 | θ=0.5 K=1e+01 | θ=0.5 K=1e+02 | θ=0.5 K=1e+04 | θ=0.5 K=1e+06 | θ=1 K=1e+01 | θ=1 K=1e+02 | θ=1 K=1e+04 | θ=1 K=1e+06 | θ=1.5 K=1e+01 | θ=1.5 K=1e+02 | θ=1.5 K=1e+04 | θ=1.5 K=1e+06 | θ=2 K=1e+01 | θ=2 K=1e+02 | θ=2 K=1e+04 | θ=2 K=1e+06 | +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +| cms-fastpath-vector2d | rows=3 cols=1024 | 1e+03 | 1e+03 | 6e+06 | never | 1e+03 | 1e+03 | 6e+04 | 3e+05 | 1e+03 | 1e+03 | 2e+04 | 6e+04 | 1e+03 | 1e+03 | 2e+05 | 1e+05 | 1e+03 | 1e+03 | 3e+06 | 3e+06 | +| cms-fastpath-vector2d | rows=3 cols=16384 | 1e+03 | 1e+03 | never | never | 1e+03 | 1e+03 | 3e+04 | 6e+05 | 1e+03 | 1e+03 | 6e+04 | 2e+05 | 1e+03 | 1e+03 | 2e+06 | 2e+07 | 1e+03 | 1e+03 | 3e+06 | 6e+06 | +| cms-fastpath-vector2d | rows=3 cols=256 | 1e+03 | 3e+03 | 6e+06 | never | 1e+03 | 1e+03 | 3e+04 | 3e+05 | 1e+03 | 3e+04 | 6e+03 | 1e+04 | 1e+03 | 1e+04 | 2e+04 | 1e+04 | 1e+03 | 6e+04 | 3e+05 | 3e+04 | +| cms-fastpath-vector2d | rows=3 cols=4096 | 1e+03 | 1e+03 | never | never | 1e+03 | 1e+03 | 6e+04 | 3e+05 | 1e+03 | 1e+03 | 1e+04 | 1e+05 | 1e+03 | 1e+03 | 2e+06 | 1e+06 | 1e+03 | 1e+03 | 6e+06 | 2e+07 | +| cms-fastpath-vector2d | rows=5 cols=1024 | 1e+03 | 1e+03 | 6e+06 | never | 1e+03 | 1e+03 | 6e+04 | 3e+05 | 1e+03 | 1e+03 | 6e+04 | 6e+04 | 1e+03 | 1e+03 | 3e+05 | 3e+05 | 1e+03 | 1e+03 | 1e+07 | 1e+07 | +| cms-fastpath-vector2d | rows=5 cols=16384 | 1e+03 | 1e+03 | never | never | 1e+03 | 1e+03 | 3e+06 | 1e+06 | 1e+03 | 1e+03 | 6e+04 | 2e+06 | 1e+03 | 1e+03 | 2e+07 | 2e+07 | 1e+03 | 1e+03 | 1e+07 | never | +| cms-fastpath-vector2d | rows=5 cols=256 | 1e+03 | 1e+03 | 6e+06 | never | 1e+03 | 1e+03 | 3e+04 | 3e+05 | 1e+03 | 1e+03 | 1e+04 | 2e+04 | 1e+03 | 1e+03 | 6e+04 | 6e+04 | 1e+03 | 1e+03 | 6e+05 | 2e+05 | +| cms-fastpath-vector2d | rows=5 cols=4096 | 1e+03 | 1e+03 | 3e+06 | never | 1e+03 | 1e+03 | 2e+05 | 3e+05 | 1e+03 | 1e+03 | 3e+05 | 2e+05 | 1e+03 | 1e+03 | 6e+06 | 3e+06 | 1e+03 | 1e+03 | never | never | +| cms-heap-topk-fastpath-vector2d | rows=3 cols=1024 | 1e+03 | 1e+03 | never | 3e+03 | 1e+03 | 1e+03 | 1e+04 | never | 1e+03 | 1e+03 | 1e+03 | 2e+07 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 2e+03 | 2e+03 | +| cms-heap-topk-fastpath-vector2d | rows=3 cols=16384 | 1e+03 | 1e+03 | 3e+06 | 6e+04 | 1e+03 | 1e+03 | 6e+03 | 1e+05 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 2e+03 | 3e+03 | +| cms-heap-topk-fastpath-vector2d | rows=3 cols=256 | 1e+03 | 1e+03 | never | 1e+03 | 1e+03 | 1e+03 | 1e+06 | 1e+07 | 1e+03 | 1e+03 | 6e+05 | never | 1e+03 | 1e+03 | 3e+06 | never | 1e+03 | 1e+03 | 2e+07 | 2e+07 | +| cms-heap-topk-fastpath-vector2d | rows=3 cols=4096 | 1e+03 | 1e+03 | never | 2e+04 | 1e+03 | 1e+03 | 6e+03 | 2e+05 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 2e+03 | 3e+03 | +| cms-heap-topk-fastpath-vector2d | rows=5 cols=1024 | 1e+03 | 1e+03 | never | 1e+03 | 1e+03 | 1e+03 | 6e+03 | 6e+05 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 2e+03 | 3e+03 | +| cms-heap-topk-fastpath-vector2d | rows=5 cols=16384 | 1e+03 | 1e+03 | 6e+06 | 3e+05 | 1e+03 | 1e+03 | 6e+03 | 6e+04 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 2e+03 | 3e+03 | +| cms-heap-topk-fastpath-vector2d | rows=5 cols=256 | 1e+03 | 1e+03 | never | 1e+03 | 1e+03 | 1e+03 | 1e+06 | 6e+06 | 1e+03 | 1e+03 | 1e+03 | 1e+07 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 2e+03 | 3e+03 | +| cms-heap-topk-fastpath-vector2d | rows=5 cols=4096 | 1e+03 | 1e+03 | never | 2e+04 | 1e+03 | 1e+03 | 2e+03 | 2e+05 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 1e+03 | 2e+03 | 3e+03 | +| countsketch-fastpath-vector2d | rows=3 cols=1024 | 1e+03 | 1e+03 | 6e+06 | never | 1e+03 | 1e+03 | 1e+05 | 1e+06 | 1e+03 | 1e+03 | 3e+04 | 2e+05 | 1e+03 | 1e+03 | 2e+04 | 3e+05 | 1e+03 | 1e+03 | 3e+06 | 3e+06 | +| countsketch-fastpath-vector2d | rows=3 cols=16384 | 1e+03 | 1e+03 | 2e+06 | never | 1e+03 | 1e+03 | 3e+05 | 3e+06 | 1e+03 | 1e+03 | 6e+05 | 6e+05 | 1e+03 | 1e+03 | 1e+05 | 6e+03 | 1e+03 | 1e+03 | 6e+06 | 3e+06 | +| countsketch-fastpath-vector2d | rows=3 cols=256 | 1e+03 | 1e+03 | 1e+07 | never | 1e+03 | 1e+03 | 2e+05 | 2e+06 | 1e+03 | 1e+03 | 6e+04 | 3e+05 | 1e+03 | 2e+03 | 2e+04 | 1e+05 | 1e+03 | 1e+04 | 6e+03 | 2e+05 | +| countsketch-fastpath-vector2d | rows=3 cols=4096 | 1e+03 | 1e+03 | never | never | 1e+03 | 1e+03 | 2e+05 | 3e+06 | 1e+03 | 1e+03 | 3e+05 | 1e+06 | 1e+03 | 1e+03 | 2e+05 | 3e+06 | 1e+03 | 1e+03 | 1e+06 | 3e+06 | +| countsketch-fastpath-vector2d | rows=5 cols=1024 | 1e+03 | 1e+03 | 2e+06 | never | 1e+03 | 1e+03 | 6e+03 | 2e+06 | 1e+03 | 1e+03 | 6e+04 | 1e+05 | 1e+03 | 1e+03 | 1e+05 | 3e+05 | 1e+03 | 1e+03 | 3e+05 | 3e+06 | +| countsketch-fastpath-vector2d | rows=5 cols=16384 | 1e+03 | 1e+03 | 3e+06 | never | 1e+03 | 1e+03 | 3e+05 | 2e+06 | 1e+03 | 1e+03 | 2e+04 | 3e+06 | 1e+03 | 1e+03 | 3e+05 | 2e+07 | 1e+03 | 1e+03 | never | 2e+07 | +| countsketch-fastpath-vector2d | rows=5 cols=256 | 1e+03 | 1e+03 | 2e+06 | never | 1e+03 | 1e+03 | 1e+04 | 6e+06 | 1e+03 | 3e+04 | 6e+04 | 2e+05 | 1e+03 | 1e+03 | 3e+04 | 6e+04 | 1e+03 | 1e+04 | 2e+05 | 6e+05 | +| countsketch-fastpath-vector2d | rows=5 cols=4096 | 1e+03 | 1e+03 | never | never | 1e+03 | 1e+03 | 2e+04 | 6e+06 | 1e+03 | 1e+03 | 2e+05 | 2e+06 | 1e+03 | 1e+03 | 6e+04 | 3e+06 | 1e+03 | 1e+03 | never | 3e+06 | + +Points that never saturate by the largest N, per sketch and K: + +| sketch | K | never saturated / points | +|---|---|---| +| cms-fastpath-vector2d | 1e+01 | 0 / 40 | +| cms-fastpath-vector2d | 1e+02 | 0 / 40 | +| cms-fastpath-vector2d | 1e+04 | 4 / 40 | +| cms-fastpath-vector2d | 1e+06 | 10 / 40 | +| cms-heap-topk-fastpath-vector2d | 1e+01 | 0 / 40 | +| cms-heap-topk-fastpath-vector2d | 1e+02 | 0 / 40 | +| cms-heap-topk-fastpath-vector2d | 1e+04 | 6 / 40 | +| cms-heap-topk-fastpath-vector2d | 1e+06 | 3 / 40 | +| countsketch-fastpath-vector2d | 1e+01 | 0 / 40 | +| countsketch-fastpath-vector2d | 1e+02 | 0 / 40 | +| countsketch-fastpath-vector2d | 1e+04 | 4 / 40 | +| countsketch-fastpath-vector2d | 1e+06 | 8 / 40 | + +Top-k precision@k merged / single at N = 1e+07 (1.00 = no loss): + +| config | θ | K | m=4 | m=16 | m=64 | +|---|---|---|---|---|---| +| rows=3 cols=1024 | 0 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=1024 | 0 | 1e+02 | 1.00 | 0.97 | 0.97 | +| rows=3 cols=1024 | 0 | 1e+04 | nan | nan | nan | +| rows=3 cols=1024 | 0 | 1e+06 | nan | nan | nan | +| rows=3 cols=1024 | 0.5 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=1024 | 0.5 | 1e+02 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=1024 | 0.5 | 1e+04 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=1024 | 0.5 | 1e+06 | 0.91 | 0.82 | 0.91 | +| rows=3 cols=1024 | 1 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=1024 | 1 | 1e+02 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=1024 | 1 | 1e+04 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=1024 | 1 | 1e+06 | 0.92 | 0.93 | 0.92 | +| rows=3 cols=1024 | 1.5 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=1024 | 1.5 | 1e+02 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=1024 | 1.5 | 1e+04 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=1024 | 1.5 | 1e+06 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=1024 | 2 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=1024 | 2 | 1e+02 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=1024 | 2 | 1e+04 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=1024 | 2 | 1e+06 | 0.99 | 0.99 | 1.00 | +| rows=3 cols=16384 | 0 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=16384 | 0 | 1e+02 | 1.00 | 0.97 | 0.97 | +| rows=3 cols=16384 | 0 | 1e+04 | 0.50 | 1.00 | 0.50 | +| rows=3 cols=16384 | 0 | 1e+06 | nan | nan | nan | +| rows=3 cols=16384 | 0.5 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=16384 | 0.5 | 1e+02 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=16384 | 0.5 | 1e+04 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=16384 | 0.5 | 1e+06 | 1.00 | 1.01 | 1.00 | +| rows=3 cols=16384 | 1 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=16384 | 1 | 1e+02 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=16384 | 1 | 1e+04 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=16384 | 1 | 1e+06 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=16384 | 1.5 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=16384 | 1.5 | 1e+02 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=16384 | 1.5 | 1e+04 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=16384 | 1.5 | 1e+06 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=16384 | 2 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=16384 | 2 | 1e+02 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=16384 | 2 | 1e+04 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=16384 | 2 | 1e+06 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=256 | 0 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=256 | 0 | 1e+02 | 1.00 | 0.99 | 0.99 | +| rows=3 cols=256 | 0 | 1e+04 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=256 | 0 | 1e+06 | nan | nan | nan | +| rows=3 cols=256 | 0.5 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=256 | 0.5 | 1e+02 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=256 | 0.5 | 1e+04 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=256 | 0.5 | 1e+06 | 0.82 | 0.82 | 1.00 | +| rows=3 cols=256 | 1 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=256 | 1 | 1e+02 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=256 | 1 | 1e+04 | 0.98 | 0.98 | 0.98 | +| rows=3 cols=256 | 1 | 1e+06 | 0.89 | 0.85 | 0.89 | +| rows=3 cols=256 | 1.5 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=256 | 1.5 | 1e+02 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=256 | 1.5 | 1e+04 | 0.98 | 1.00 | 0.98 | +| rows=3 cols=256 | 1.5 | 1e+06 | 0.96 | 0.92 | 0.90 | +| rows=3 cols=256 | 2 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=256 | 2 | 1e+02 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=256 | 2 | 1e+04 | 0.96 | 0.94 | 0.96 | +| rows=3 cols=256 | 2 | 1e+06 | 0.98 | 1.00 | 0.99 | +| rows=3 cols=4096 | 0 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=4096 | 0 | 1e+02 | 1.00 | 0.97 | 0.97 | +| rows=3 cols=4096 | 0 | 1e+04 | nan | nan | nan | +| rows=3 cols=4096 | 0 | 1e+06 | nan | nan | nan | +| rows=3 cols=4096 | 0.5 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=4096 | 0.5 | 1e+02 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=4096 | 0.5 | 1e+04 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=4096 | 0.5 | 1e+06 | 0.97 | 0.99 | 1.00 | +| rows=3 cols=4096 | 1 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=4096 | 1 | 1e+02 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=4096 | 1 | 1e+04 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=4096 | 1 | 1e+06 | 0.99 | 0.99 | 0.98 | +| rows=3 cols=4096 | 1.5 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=4096 | 1.5 | 1e+02 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=4096 | 1.5 | 1e+04 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=4096 | 1.5 | 1e+06 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=4096 | 2 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=4096 | 2 | 1e+02 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=4096 | 2 | 1e+04 | 1.00 | 1.00 | 1.00 | +| rows=3 cols=4096 | 2 | 1e+06 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=1024 | 0 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=1024 | 0 | 1e+02 | 1.00 | 0.97 | 0.97 | +| rows=5 cols=1024 | 0 | 1e+04 | nan | nan | nan | +| rows=5 cols=1024 | 0 | 1e+06 | nan | nan | nan | +| rows=5 cols=1024 | 0.5 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=1024 | 0.5 | 1e+02 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=1024 | 0.5 | 1e+04 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=1024 | 0.5 | 1e+06 | 1.03 | 1.15 | 1.15 | +| rows=5 cols=1024 | 1 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=1024 | 1 | 1e+02 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=1024 | 1 | 1e+04 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=1024 | 1 | 1e+06 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=1024 | 1.5 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=1024 | 1.5 | 1e+02 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=1024 | 1.5 | 1e+04 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=1024 | 1.5 | 1e+06 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=1024 | 2 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=1024 | 2 | 1e+02 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=1024 | 2 | 1e+04 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=1024 | 2 | 1e+06 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=16384 | 0 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=16384 | 0 | 1e+02 | 1.00 | 0.97 | 0.97 | +| rows=5 cols=16384 | 0 | 1e+04 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=16384 | 0 | 1e+06 | nan | nan | nan | +| rows=5 cols=16384 | 0.5 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=16384 | 0.5 | 1e+02 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=16384 | 0.5 | 1e+04 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=16384 | 0.5 | 1e+06 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=16384 | 1 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=16384 | 1 | 1e+02 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=16384 | 1 | 1e+04 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=16384 | 1 | 1e+06 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=16384 | 1.5 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=16384 | 1.5 | 1e+02 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=16384 | 1.5 | 1e+04 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=16384 | 1.5 | 1e+06 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=16384 | 2 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=16384 | 2 | 1e+02 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=16384 | 2 | 1e+04 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=16384 | 2 | 1e+06 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=256 | 0 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=256 | 0 | 1e+02 | 1.00 | 0.97 | 0.97 | +| rows=5 cols=256 | 0 | 1e+04 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=256 | 0 | 1e+06 | nan | nan | nan | +| rows=5 cols=256 | 0.5 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=256 | 0.5 | 1e+02 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=256 | 0.5 | 1e+04 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=256 | 0.5 | 1e+06 | 0.94 | 1.00 | 1.00 | +| rows=5 cols=256 | 1 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=256 | 1 | 1e+02 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=256 | 1 | 1e+04 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=256 | 1 | 1e+06 | 0.99 | 0.96 | 0.99 | +| rows=5 cols=256 | 1.5 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=256 | 1.5 | 1e+02 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=256 | 1.5 | 1e+04 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=256 | 1.5 | 1e+06 | 1.00 | 1.00 | 1.01 | +| rows=5 cols=256 | 2 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=256 | 2 | 1e+02 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=256 | 2 | 1e+04 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=256 | 2 | 1e+06 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=4096 | 0 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=4096 | 0 | 1e+02 | 1.00 | 0.97 | 0.97 | +| rows=5 cols=4096 | 0 | 1e+04 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=4096 | 0 | 1e+06 | nan | nan | nan | +| rows=5 cols=4096 | 0.5 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=4096 | 0.5 | 1e+02 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=4096 | 0.5 | 1e+04 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=4096 | 0.5 | 1e+06 | 1.00 | 0.99 | 1.00 | +| rows=5 cols=4096 | 1 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=4096 | 1 | 1e+02 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=4096 | 1 | 1e+04 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=4096 | 1 | 1e+06 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=4096 | 1.5 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=4096 | 1.5 | 1e+02 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=4096 | 1.5 | 1e+04 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=4096 | 1.5 | 1e+06 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=4096 | 2 | 1e+01 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=4096 | 2 | 1e+02 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=4096 | 2 | 1e+04 | 1.00 | 1.00 | 1.00 | +| rows=5 cols=4096 | 2 | 1e+06 | 1.00 | 1.00 | 1.00 | + +11 points have single-sketch precision 0 (no ratio, shown as nan). + +Largest losses: rows=3 cols=16384 θ=0 K=1e+04: 0.50, rows=3 cols=1024 θ=0.5 K=1e+06: 0.82, rows=3 cols=256 θ=0.5 K=1e+06: 0.82, rows=3 cols=256 θ=1 K=1e+06: 0.85, rows=3 cols=256 θ=1.5 K=1e+06: 0.90 + +## Cost phase: pending + +Pending: 62 points running in parallel, plus a serial-vs-parallel +consistency check. + +The serial cost phase (`study_saturation.py --phase cost`) measured 178 of +the 240 rows=3 points (all CMS and CountSketch, and 18 top-k), then was +stopped: its remaining top-k points took 10–20 minutes each. The other 62 +top-k points are being measured with `scripts/complete_saturation_costs.py +--jobs 12`, which runs the cost phase's exact approxbench command and appends +to the same file. Because CPU timing can shift under parallel load, +`scripts/check_parallel_costs.py` will re-measure 12 serially measured points +(4 CMS, 4 CountSketch, 4 top-k, covering every K and θ) as one 12-way batch, +and compare insert/merge/query CPU against the serial values. If they differ +by more than about 10%, the 62 points will be re-run serially. This section +and `saturation.csv`'s cost columns will be filled in when that finishes, +listing which points were measured in parallel. Until then the cost columns +of `saturation.csv` are empty. + +## Files + +| File | SHA-256 | +|---|---| +| `saturation.csv` (accuracy only; cost columns pending) | `72677a2a074f109e160b76821f360318cb3faffe8a8fcada5b1f0cd82e51f544` | +| `saturation_curve.csv` | `789ab79bcd727e4d7805ff70f9c0440a5bab1e48721d1f4b04cd456ca2e74f70` | +| `saturation_merge_curve.csv` (top-k, m ∈ {1, 4, 16, 64}, N ≤ 1e7) | `3f5e3857fdd5d0f66a60bcaa7ae78f1d20361a4d9469c2b60b68b13061d3e57a` | + +The raw per-run JSONL (32 MB) is not committed. + +## Machine + +CloudLab clnode109 (node0.zz-y-318434.softmeasure-pg0.clemson.cloudlab.us): +Intel Xeon E5-2683 v3 @ 2.00 GHz, 2 sockets × 14 cores × 2 threads, 56 CPUs. +sketch-bench at a0b8b42 (#131's head, identical in content to main's +bd644fe), approxbench release build. Accuracy phase from 2026-10-05 03:24 UTC +to 05:45 UTC. + +## Commands + +```sh +cargo build -p aqpbm-cli --release +G="--families frequency,topk --thetas 0,0.5,1.0,1.5,2.0 --cardinalities 10,100,10000,1000000 --n-max 1e8 --seeds 3" +# accuracy grid (single sketch) +python3 scripts/study_saturation.py --phase accuracy $G --jobs 32 --out OUT/grid +# top-k after merging m shards (N <= 1e7) +python3 scripts/study_saturation.py --phase accuracy --families topk \ + --thetas 0,0.5,1.0,1.5,2.0 --cardinalities 10,100,10000,1000000 \ + --n-max 1e7 --seeds 3 --merge-shards-list 1,4,16,64 --jobs 14 --out OUT/merge_topk +# cost (rows=3; rows=5 is scaled by consumers): serial phase, then the remainder +python3 scripts/study_saturation.py --phase cost $G --cost-rows 3 --out OUT/grid +python3 scripts/complete_saturation_costs.py OUT/grid --jobs 12 +# this README's tables +python3 scripts/summarize_synthetic_curves.py OUT/grid OUT/merge_topk +``` diff --git 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json +import sys +from concurrent.futures import ThreadPoolExecutor + +from complete_saturation_costs import dataset_args +from study_saturation import MERGE_SHARDS, cpu_secs, load_saved, point_key, run + +OPS = ("insert", "merge", "query") + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("serial") + parser.add_argument("out") + parser.add_argument("--binary", default="./target/release/approxbench") + parser.add_argument("--jobs", type=int, default=12) + parser.add_argument("--n-max", type=float, default=1e8) + parser.add_argument("--seed", type=int, default=1) + args = parser.parse_args() + + serial = load_saved(args.serial, point_key) + keys = [json.loads(line) for line in sys.stdin if line.strip()] + + def measure(key): + sketch, config, dist, param, card = key + row = {"dist": dist, "param": str(param), "cardinality": str(card)} + return key, run(args.binary, [ + "--variant", sketch, "--library", "lib", "--config", config, + "--operations", "insert,query,merge", + "--metrics", "throughput,cpu,memory", + "--merge-shards", str(MERGE_SHARDS), "--runs", "3", "--warmup-runs", "1", + "--size", str(int(args.n_max)), "--seed", str(args.seed), "--flat", + ] + dataset_args(row)) + + print("| sketch | config | θ | K | " + " | ".join(f"{op} parallel/serial" for op in OPS) + " |") + print("|---|---|---|---|" + "---|" * len(OPS)) + worst = 0.0 + with ThreadPoolExecutor(max_workers=args.jobs) as pool, open(args.out, "w") as out: + for key, record in pool.map(measure, keys): + out.write(json.dumps({"key": key, **record}) + "\n") + base = serial[point_key(*key)] + ratios = [cpu_secs(record, op) / cpu_secs(base, op) for op in OPS] + worst = max(worst, *(abs(r - 1) for r in ratios)) + print(f"| {key[0]} | {key[1]} | {key[3]:g} | {key[4]} | " + + " | ".join(f"{r:.3f}" for r in ratios) + " |") + print(f"\nLargest deviation from serial: {worst:.1%}") + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/complete_saturation_costs.py b/scripts/complete_saturation_costs.py new file mode 100644 index 00000000..cababf5e --- /dev/null +++ b/scripts/complete_saturation_costs.py @@ -0,0 +1,97 @@ +#!/usr/bin/env python3 +"""Finish an interrupted `study_saturation.py --phase cost` run. + +The cost phase measures one point at a time and rewrites +saturation_cost.jsonl from scratch, so an interrupted run cannot resume. +This runs the points still missing from saturation_cost.jsonl (the same +approxbench command as the cost phase, --jobs at a time), appends them, and +fills the cost columns of saturation.csv from every record. It does not run +the exact-baseline crossover. + +Usage: complete_saturation_costs.py GRID_DIR [--jobs 12] [--cost-rows 3] + [--n-max 1e8] [--seed 1] [--binary ./target/release/approxbench] +""" + +import argparse +import csv +import json +import os +import sys +from concurrent.futures import ThreadPoolExecutor + +sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) +from study_saturation import ( # noqa: E402 + MERGE_SHARDS, SUMMARY_COLUMNS, cpu_secs, load_saved, point_key, run) + + +def dataset_args(row): + """The cost phase's dataset arguments for one saturation.csv row.""" + if row["dist"] == "pareto": + return ["--dataset", "pareto", "--pareto-alpha", row["param"], + "--pareto-scale", "1000", "--cardinality", "1", "--dtype", "i64"] + return ["--dataset", "zipf", "--zipf-s", row["param"], + "--cardinality", row["cardinality"], "--dtype", "i64"] + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("grid_dir") + parser.add_argument("--binary", default="./target/release/approxbench") + parser.add_argument("--jobs", type=int, default=12) + parser.add_argument("--cost-rows", type=int, default=3) + parser.add_argument("--n-max", type=float, default=1e8) + parser.add_argument("--seed", type=int, default=1) + args = parser.parse_args() + + csv_path = os.path.join(args.grid_dir, "saturation.csv") + cost_path = os.path.join(args.grid_dir, "saturation_cost.jsonl") + rows = list(csv.DictReader(open(csv_path))) + saved = load_saved(cost_path, point_key) if os.path.exists(cost_path) else {} + + def key(row): + return point_key(row["sketch"], row["config"], row["dist"], row["param"], + row["cardinality"]) + + def measured(row): + config = row["config"] + return not (config.startswith("rows=") + and not config.startswith(f"rows={args.cost_rows} ")) + + missing = [r for r in rows if measured(r) and key(r) not in saved] + print(f"{len(saved)} saved, {len(missing)} to run", file=sys.stderr) + + def measure(row): + return row, run(args.binary, [ + "--variant", row["sketch"], "--library", "lib", "--config", row["config"], + "--operations", "insert,query,merge", + "--metrics", "throughput,cpu,memory", + "--merge-shards", str(MERGE_SHARDS), "--runs", "3", "--warmup-runs", "1", + "--size", str(int(args.n_max)), "--seed", str(args.seed), "--flat", + ] + dataset_args(row)) + + with ThreadPoolExecutor(max_workers=args.jobs) as pool, open(cost_path, "a") as out: + for row, record in pool.map(measure, missing): + k = key(row) + out.write(json.dumps({"key": [row["sketch"], row["config"], row["dist"], + float(row["param"]), row["cardinality"]], + **record}) + "\n") + out.flush() + saved[k] = record + print(f" {row['sketch']} ({row['config']}) {row['dist']}={row['param']} " + f"K={row['cardinality']}", file=sys.stderr) + + for row in rows: + record = saved.get(key(row), {}) if measured(row) else {} + row["insert_cpu_secs"] = cpu_secs(record, "insert") + row["merge_cpu_secs"] = cpu_secs(record, "merge") + row["query_cpu_secs"] = cpu_secs(record, "query") + row["memory_bytes"] = record.get("memory_bytes", "") + with open(csv_path, "w", newline="") as f: + writer = csv.DictWriter(f, fieldnames=SUMMARY_COLUMNS) + writer.writeheader() + writer.writerows(rows) + print(f"Done. {csv_path}", file=sys.stderr) + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/summarize_synthetic_curves.py b/scripts/summarize_synthetic_curves.py new file mode 100644 index 00000000..65d57937 --- /dev/null +++ b/scripts/summarize_synthetic_curves.py @@ -0,0 +1,83 @@ +#!/usr/bin/env python3 +"""Summarize the saturation runs at the synthetic workload's label +cardinalities into the tables in +docs/figures/saturation/synthetic_cardinalities/README.md. + +Usage: summarize_synthetic_curves.py GRID_DIR MERGE_DIR + GRID_DIR/saturation.csv single-sketch N_sat per point + MERGE_DIR/saturation_merge_curve.csv top-k precision per shard count +Prints Markdown: N_sat per (sketch, config) x (theta, K), counts of points +that never saturate, and the top-k merged/single precision ratio at the +largest N of the merge run. +""" + +import csv +import math +import sys +from collections import defaultdict + + +def nsat_table(path): + rows = list(csv.DictReader(open(path))) + thetas = sorted({float(r["param"]) for r in rows}) + ks = sorted({int(r["cardinality"]) for r in rows}) + cells = defaultdict(dict) + for r in rows: + cells[(r["sketch"], r["config"])][(float(r["param"]), int(r["cardinality"]))] = r["n_sat"] + head = ["sketch", "config"] + [f"θ={t:g} K={k:.0e}" for t in thetas for k in ks] + print("| " + " | ".join(head) + " |") + print("|" + "---|" * len(head)) + for (sketch, config), c in sorted(cells.items()): + vals = [] + for t in thetas: + for k in ks: + v = c.get((t, k), "") + vals.append("never" if v == "not_saturated" else (f"{float(v):.0e}" if v else "")) + print(f"| {sketch} | {config} | " + " | ".join(vals) + " |") + never = defaultdict(int) + total = defaultdict(int) + for r in rows: + key = (r["sketch"], int(r["cardinality"])) + total[key] += 1 + never[key] += r["n_sat"] == "not_saturated" + print("\nPoints that never saturate by the largest N, per sketch and K:\n") + print("| sketch | K | never saturated / points |") + print("|---|---|---|") + for key in sorted(total): + print(f"| {key[0]} | {key[1]:.0e} | {never[key]} / {total[key]} |") + + +def merge_table(path): + rows = list(csv.DictReader(open(path))) + nmax = max(int(r["n"]) for r in rows) + single = {} + merged = defaultdict(dict) + for r in rows: + if int(r["n"]) != nmax: + continue + key = (r["config"], float(r["param"]), int(r["cardinality"])) + m = int(r["shards"]) + if m == 1: + single[key] = float(r["seed_mean_error"]) + else: + merged[key][m] = float(r["seed_mean_error"]) + print(f"\nTop-k precision@k merged / single at N = {nmax:.0e} (1.00 = no loss):\n") + shards = sorted({m for v in merged.values() for m in v}) + print("| config | θ | K | " + " | ".join(f"m={m}" for m in shards) + " |") + print("|---|---|---|" + "---|" * len(shards)) + worst = [] + for key in sorted(merged): + s = single.get(key) + ratios = [merged[key].get(m, float("nan")) / s if s else float("nan") for m in shards] + if not any(math.isnan(r) for r in ratios): # single-sketch precision 0: no ratio + worst.append((min(ratios), key)) + print(f"| {key[0]} | {key[1]:g} | {key[2]:.0e} | " + " | ".join(f"{x:.2f}" for x in ratios) + " |") + worst.sort() + undefined = sum(1 for key in merged if not single.get(key)) + print(f"\n{undefined} points have single-sketch precision 0 (no ratio, shown as nan).") + print("\nLargest losses:", ", ".join(f"{k[0]} θ={k[1]:g} K={k[2]:.0e}: {r:.2f}" for r, k in worst[:5])) + + +if __name__ == "__main__": + nsat_table(f"{sys.argv[1]}/saturation.csv") + merge_table(f"{sys.argv[2]}/saturation_merge_curve.csv") From 50f2acff39e8fc604931a0bc52f0ecd8e2fc4bb2 Mon Sep 17 00:00:00 2001 From: zz_y Date: Mon, 5 Oct 2026 22:50:38 +0000 Subject: [PATCH 2/2] Fill the cost phase: 178 points at N = 1e8 and 62 top-k at 1e7, all serial - saturation.csv now has cost columns for all 240 rows=3 points. - complete_saturation_costs.py --normalize-n writes insert CPU scaled to the N readers divide by, so insert CPU per item is each record's own CPU over its own N. - check_cost_size.py compares cost measured at a smaller N with full-N serial records; its 5-point result and the acceptance criterion for the top-k rerun are in the README, with the discarded 12-way parallel records and why. Co-Authored-By: Claude Opus 5.5 --- .../synthetic_cardinalities/README.md | 100 +++- .../synthetic_cardinalities/saturation.csv | 480 +++++++++--------- scripts/check_cost_size.py | 76 +++ scripts/complete_saturation_costs.py | 15 +- 4 files changed, 408 insertions(+), 263 deletions(-) create mode 100644 scripts/check_cost_size.py diff --git a/docs/figures/saturation/synthetic_cardinalities/README.md b/docs/figures/saturation/synthetic_cardinalities/README.md index 74dbb15d..8bde0af4 100644 --- a/docs/figures/saturation/synthetic_cardinalities/README.md +++ b/docs/figures/saturation/synthetic_cardinalities/README.md @@ -235,34 +235,87 @@ Top-k precision@k merged / single at N = 1e+07 (1.00 = no loss): Largest losses: rows=3 cols=16384 θ=0 K=1e+04: 0.50, rows=3 cols=1024 θ=0.5 K=1e+06: 0.82, rows=3 cols=256 θ=0.5 K=1e+06: 0.82, rows=3 cols=256 θ=1 K=1e+06: 0.85, rows=3 cols=256 θ=1.5 K=1e+06: 0.90 -## Cost phase: pending +## Cost phase -Pending: 62 points running in parallel, plus a serial-vs-parallel -consistency check. +`saturation.csv`'s cost columns (insert, merge and query CPU, memory) are +filled for all 240 rows=3 points; rows=5 configs are scaled from rows=3 by +consumers. Every cost was measured **serially** (one approxbench process at +a time) on clnode109, by two runs: -The serial cost phase (`study_saturation.py --phase cost`) measured 178 of -the 240 rows=3 points (all CMS and CountSketch, and 18 top-k), then was -stopped: its remaining top-k points took 10–20 minutes each. The other 62 -top-k points are being measured with `scripts/complete_saturation_costs.py ---jobs 12`, which runs the cost phase's exact approxbench command and appends -to the same file. Because CPU timing can shift under parallel load, -`scripts/check_parallel_costs.py` will re-measure 12 serially measured points -(4 CMS, 4 CountSketch, 4 top-k, covering every K and θ) as one 12-way batch, -and compare insert/merge/query CPU against the serial values. If they differ -by more than about 10%, the 62 points will be re-run serially. This section -and `saturation.csv`'s cost columns will be filled in when that finishes, -listing which points were measured in parallel. Until then the cost columns -of `saturation.csv` are empty. +| Points | Stream length N | How | +|---|---|---| +| 178: all 80 CMS, all 80 CountSketch, 18 top-k | 1e8 | `study_saturation.py --phase cost` | +| 62 top-k (K = 10, 100, 1e4, 1e6; 15–16 each) | 1e7 | `complete_saturation_costs.py --jobs 1 --n-max 1e7 --normalize-n 1e8` | + +**Normalization.** Readers divide `insert_cpu_secs` by the point's last +curve N (1e8). The 1e7 points' insert CPU is therefore written scaled by +1e8 / 1e7, so insert CPU per item equals each record's own CPU divided by +its own N. Merge (16 shards folded into one) and the whole query phase are +written as measured. + +**Why two stream lengths.** The serial 1e8 cost phase measured 178 points, +then was stopped: its remaining top-k points took 30–50 minutes each, so the +62 would have taken 25–50 hours. They were first measured 12 at a time +(below), which failed a consistency check, then re-measured serially at +N = 1e7 after this validation (`scripts/check_cost_size.py`): five points +with serial 1e8 records were re-measured at 1e7, and the 1e7 / 1e8 ratios +are: + +| sketch | config | θ | K | insert per item small/full N | merge per fold small/full N | query phase small/full N | per query small/full N | +|---|---|---|---|---|---|---|---| +| cms-fastpath-vector2d | rows=3 cols=1024 | 1 | 10000 | 1.003 | 7.259 | 1.011 | 1.012 | +| countsketch-fastpath-vector2d | rows=3 cols=4096 | 0.5 | 1000000 | 1.005 | 0.933 | 1.504 | 1.507 | +| cms-heap-topk-fastpath-vector2d | rows=3 cols=256 | 0 | 100 | 1.010 | 0.967 | 0.923 | 0.920 | +| cms-heap-topk-fastpath-vector2d | rows=3 cols=256 | 1.5 | 1000000 | 1.035 | 1.051 | 1.000 | 0.935 | +| cms-heap-topk-fastpath-vector2d | rows=3 cols=256 | 2 | 100 | 0.992 | 0.988 | 1.067 | 0.941 | + +Largest deviation from the full-N measurement: 625.9% + +Acceptance for the top-k rerun: insert and merge within 5% of 1e8, the +query phase within 10%. The three top-k points measured insert 1.010 / 1.035 +/ 0.992, merge 0.967 / 1.051 / 0.988 (1.051 is 5.1%, accepted as measured), +and query phase 0.923 / 1.000 / 1.067. The CMS merge (7.3×; a µs-scale merge, +1.9 vs 13.7 µs per fold) and the CountSketch query phase at K = 1e6 (1.5×) +fail, but neither sketch is in the 1e7 rerun set: all CMS and CountSketch +points are 1e8 measurements. + +**Discarded parallel records.** The 62 top-k points were first measured 12 +at a time (`complete_saturation_costs.py --jobs 12`). Re-measuring 12 +serially measured points as one 12-way batch (`scripts/check_parallel_costs.py`) +showed parallel load shifts CPU time by more than 10%: + +| sketch | config | θ | K | insert parallel/serial | merge parallel/serial | query parallel/serial | +|---|---|---|---|---|---|---| +| cms-fastpath-vector2d | rows=3 cols=256 | 0 | 10 | 1.196 | 1.185 | 1.000 | +| cms-fastpath-vector2d | rows=3 cols=1024 | 1 | 10000 | 1.179 | 1.059 | 1.109 | +| cms-fastpath-vector2d | rows=3 cols=4096 | 1.5 | 100 | 1.172 | 1.011 | 1.000 | +| cms-fastpath-vector2d | rows=3 cols=16384 | 2 | 1000000 | 1.198 | 1.063 | 1.104 | +| countsketch-fastpath-vector2d | rows=3 cols=256 | 0.5 | 1000000 | 1.015 | 0.042 | 1.039 | +| countsketch-fastpath-vector2d | rows=3 cols=1024 | 1 | 100 | 1.106 | 0.906 | 1.000 | +| countsketch-fastpath-vector2d | rows=3 cols=4096 | 2 | 10 | 1.173 | 1.047 | 1.400 | +| countsketch-fastpath-vector2d | rows=3 cols=16384 | 0 | 10000 | 1.187 | 0.977 | 1.063 | +| cms-heap-topk-fastpath-vector2d | rows=3 cols=256 | 0 | 10 | 1.012 | 1.032 | 1.143 | +| cms-heap-topk-fastpath-vector2d | rows=3 cols=256 | 0.5 | 1000000 | 1.073 | 1.072 | 1.315 | +| cms-heap-topk-fastpath-vector2d | rows=3 cols=256 | 1 | 10000 | 1.026 | 0.979 | 1.000 | +| cms-heap-topk-fastpath-vector2d | rows=3 cols=256 | 2 | 100 | 0.961 | 1.003 | 0.933 | + +Largest deviation from serial: 95.8% + +Those records are kept, unused, in the raw +`saturation_cost.parallel-62-included.jsonl` (not committed). One extra top-k +point (θ = 2, K = 1e4) also has a serial 1e8 record, from the stopped serial +rerun, kept in `saturation_cost.serial-1e8-179.jsonl`. For consistency it was +re-measured at 1e7 with the other 61. ## Files | File | SHA-256 | |---|---| -| `saturation.csv` (accuracy only; cost columns pending) | `72677a2a074f109e160b76821f360318cb3faffe8a8fcada5b1f0cd82e51f544` | +| `saturation.csv` (accuracy and cost) | `152327593e47b11501b8c8c24ec721fbf0c20634f7917a2f04e6ef78518529fd` | | `saturation_curve.csv` | `789ab79bcd727e4d7805ff70f9c0440a5bab1e48721d1f4b04cd456ca2e74f70` | | `saturation_merge_curve.csv` (top-k, m ∈ {1, 4, 16, 64}, N ≤ 1e7) | `3f5e3857fdd5d0f66a60bcaa7ae78f1d20361a4d9469c2b60b68b13061d3e57a` | -The raw per-run JSONL (32 MB) is not committed. +The raw per-run JSONL (about 60 MB) is not committed. ## Machine @@ -270,7 +323,8 @@ CloudLab clnode109 (node0.zz-y-318434.softmeasure-pg0.clemson.cloudlab.us): Intel Xeon E5-2683 v3 @ 2.00 GHz, 2 sockets × 14 cores × 2 threads, 56 CPUs. sketch-bench at a0b8b42 (#131's head, identical in content to main's bd644fe), approxbench release build. Accuracy phase from 2026-10-05 03:24 UTC -to 05:45 UTC. +to 05:45 UTC; serial 1e8 cost phase 05:45–13:31 (178 points); serial 1e7 +cost run 20:37–22:45 (62 points). ## Commands @@ -283,9 +337,13 @@ python3 scripts/study_saturation.py --phase accuracy $G --jobs 32 --out OUT/grid python3 scripts/study_saturation.py --phase accuracy --families topk \ --thetas 0,0.5,1.0,1.5,2.0 --cardinalities 10,100,10000,1000000 \ --n-max 1e7 --seeds 3 --merge-shards-list 1,4,16,64 --jobs 14 --out OUT/merge_topk -# cost (rows=3; rows=5 is scaled by consumers): serial phase, then the remainder +# cost (rows=3; rows=5 is scaled by consumers): serial phase at N = 1e8 +# (stopped after 178 points), then the rest serially at N = 1e7 python3 scripts/study_saturation.py --phase cost $G --cost-rows 3 --out OUT/grid -python3 scripts/complete_saturation_costs.py OUT/grid --jobs 12 +python3 scripts/complete_saturation_costs.py OUT/grid --jobs 1 --n-max 1e7 --normalize-n 1e8 +# checks: 12-way parallel vs serial, and N = 1e7 vs 1e8 (keys on stdin) +python3 scripts/check_parallel_costs.py OUT/grid/saturation_cost.serial-178.jsonl OUT/parallel.jsonl --jobs 12 < keys +python3 scripts/check_cost_size.py OUT/grid/saturation_cost.serial-178.jsonl OUT/size.jsonl --n-max 1e7 < keys # this README's tables python3 scripts/summarize_synthetic_curves.py OUT/grid OUT/merge_topk ``` diff --git a/docs/figures/saturation/synthetic_cardinalities/saturation.csv b/docs/figures/saturation/synthetic_cardinalities/saturation.csv index d9a35893..f6af8714 100644 --- a/docs/figures/saturation/synthetic_cardinalities/saturation.csv +++ b/docs/figures/saturation/synthetic_cardinalities/saturation.csv @@ -1,84 +1,84 @@ family,sketch,config,dist,param,cardinality,n_sat,final_error,error_metric,insert_cpu_secs,merge_cpu_secs,query_cpu_secs,memory_bytes -frequency,cms-fastpath-vector2d,rows=3 cols=256,zipf,0.0,10,1000,0.0,are_top100,,,, -frequency,cms-fastpath-vector2d,rows=3 cols=256,zipf,0.0,100,3162,0.02997482561803759,are_top100,,,, -frequency,cms-fastpath-vector2d,rows=3 cols=256,zipf,0.0,10000,5623413,33.31212708426364,are_top100,,,, -frequency,cms-fastpath-vector2d,rows=3 cols=256,zipf,0.0,1000000,not_saturated,2709.904766565057,are_top100,,,, -frequency,cms-fastpath-vector2d,rows=3 cols=256,zipf,0.5,10,1000,0.0,are_top100,,,, -frequency,cms-fastpath-vector2d,rows=3 cols=256,zipf,0.5,100,1000,0.02592757072731121,are_top100,,,, -frequency,cms-fastpath-vector2d,rows=3 cols=256,zipf,0.5,10000,31623,4.251289937433801,are_top100,,,, -frequency,cms-fastpath-vector2d,rows=3 cols=256,zipf,0.5,1000000,316228,51.08933773038971,are_top100,,,, -frequency,cms-fastpath-vector2d,rows=3 cols=256,zipf,1.0,10,1000,0.0,are_top100,,,, -frequency,cms-fastpath-vector2d,rows=3 cols=256,zipf,1.0,100,31623,0.023921501018944125,are_top100,,,, -frequency,cms-fastpath-vector2d,rows=3 cols=256,zipf,1.0,10000,5623,0.7552602534108663,are_top100,,,, -frequency,cms-fastpath-vector2d,rows=3 cols=256,zipf,1.0,1000000,10000,1.6614726601040666,are_top100,,,, -frequency,cms-fastpath-vector2d,rows=3 cols=256,zipf,1.5,10,1000,0.0,are_top100,,,, -frequency,cms-fastpath-vector2d,rows=3 cols=256,zipf,1.5,100,10000,0.02328222424972609,are_top100,,,, -frequency,cms-fastpath-vector2d,rows=3 cols=256,zipf,1.5,10000,17783,0.2123903157054711,are_top100,,,, -frequency,cms-fastpath-vector2d,rows=3 cols=256,zipf,1.5,1000000,10000,0.2411992856363441,are_top100,,,, -frequency,cms-fastpath-vector2d,rows=3 cols=256,zipf,2.0,10,1000,0.0,are_top100,,,, -frequency,cms-fastpath-vector2d,rows=3 cols=256,zipf,2.0,100,56234,0.023564242835541805,are_top100,,,, -frequency,cms-fastpath-vector2d,rows=3 cols=256,zipf,2.0,10000,316228,0.09196273659950484,are_top100,,,, -frequency,cms-fastpath-vector2d,rows=3 cols=256,zipf,2.0,1000000,31623,0.09243597508773203,are_top100,,,, -frequency,cms-fastpath-vector2d,rows=3 cols=1024,zipf,0.0,10,1000,0.0,are_top100,,,, 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frequency,countsketch-fastpath-vector2d,rows=5 cols=256,zipf,0.0,10000,1778279,1.7650161721720996,are_top100,,,, @@ -319,86 +319,86 @@ frequency,countsketch-fastpath-vector2d,rows=5 cols=16384,zipf,2.0,10,1000,0.0,a frequency,countsketch-fastpath-vector2d,rows=5 cols=16384,zipf,2.0,100,1000,0.0,are_top100,,,, frequency,countsketch-fastpath-vector2d,rows=5 cols=16384,zipf,2.0,10000,not_saturated,8.36806493812971e-06,are_top100,,,, frequency,countsketch-fastpath-vector2d,rows=5 cols=16384,zipf,2.0,1000000,17782794,1.8601047685658712e-05,are_top100,,,, -topk,cms-heap-topk-fastpath-vector2d,rows=3 cols=256,zipf,0.0,10,1000,1.0,precision_at_k,,,, -topk,cms-heap-topk-fastpath-vector2d,rows=3 cols=256,zipf,0.0,100,1000,0.9375,precision_at_k,,,, -topk,cms-heap-topk-fastpath-vector2d,rows=3 cols=256,zipf,0.0,10000,not_saturated,0.0,precision_at_k,,,, -topk,cms-heap-topk-fastpath-vector2d,rows=3 cols=256,zipf,0.0,1000000,1000,0.0,precision_at_k,,,, -topk,cms-heap-topk-fastpath-vector2d,rows=3 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cols=16384,zipf,2.0,1000000,3162,1.0,precision_at_k,176.42405666666667,0.001818,6.333333333333334e-06,196608 topk,cms-heap-topk-fastpath-vector2d,rows=5 cols=256,zipf,0.0,10,1000,1.0,precision_at_k,,,, topk,cms-heap-topk-fastpath-vector2d,rows=5 cols=256,zipf,0.0,100,1000,1.0,precision_at_k,,,, topk,cms-heap-topk-fastpath-vector2d,rows=5 cols=256,zipf,0.0,10000,not_saturated,0.0,precision_at_k,,,, diff --git a/scripts/check_cost_size.py b/scripts/check_cost_size.py new file mode 100644 index 00000000..2512c47f --- /dev/null +++ b/scripts/check_cost_size.py @@ -0,0 +1,76 @@ +#!/usr/bin/env python3 +"""Check that cost measured at a smaller N matches cost measured at the full N. + +Re-measures the given points of a serial saturation_cost.jsonl one at a time +at --n-max (the cost phase's approxbench command otherwise) and prints, per +point, the small-N / full-N ratio of: +- insert CPU per item (each run's insert CPU divided by its own N); +- merge CPU per fold (MERGE_SHARDS shards folded into one, same at both N); +- the whole query phase's CPU, as the evaluation tables use it; +- CPU per query (the query phase divided by its query count). + +Usage: check_cost_size.py SERIAL_JSONL OUT_JSONL --n-max 1e7 [--seed 1] + [--binary ./target/release/approxbench] < keys.jsonl + One JSON key per line on stdin: + ["cms-fastpath-vector2d", "rows=3 cols=1024", "zipf", 1.0, 10000] +""" + +import argparse +import json +import sys + +from complete_saturation_costs import dataset_args +from study_saturation import MERGE_SHARDS, cpu_secs, load_saved, point_key, query_secs, run + + +def size(record): + return record["workload"]["synthetic"]["description"]["row_num"] + + +def per_op(record): + return { + "insert per item": cpu_secs(record, "insert") / size(record), + "merge per fold": cpu_secs(record, "merge") / (MERGE_SHARDS - 1), + "query phase": cpu_secs(record, "query"), + "per query": query_secs(record), + } + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("serial") + parser.add_argument("out") + parser.add_argument("--binary", default="./target/release/approxbench") + parser.add_argument("--n-max", type=float, required=True) + parser.add_argument("--seed", type=int, default=1) + args = parser.parse_args() + + serial = load_saved(args.serial, point_key) + keys = [json.loads(line) for line in sys.stdin if line.strip()] + ops = list(per_op(next(iter(serial.values())))) + print("| sketch | config | θ | K | " + " | ".join(f"{op} small/full N" for op in ops) + " |") + print("|---|---|---|---|" + "---|" * len(ops)) + worst = 0.0 + with open(args.out, "w") as out: + for key in keys: + sketch, config, dist, param, card = key + row = {"dist": dist, "param": str(param), "cardinality": str(card)} + record = run(args.binary, [ + "--variant", sketch, "--library", "lib", "--config", config, + "--operations", "insert,query,merge", + "--metrics", "throughput,cpu,memory", + "--merge-shards", str(MERGE_SHARDS), "--runs", "3", "--warmup-runs", "1", + "--size", str(int(args.n_max)), "--seed", str(args.seed), "--flat", + ] + dataset_args(row)) + out.write(json.dumps({"key": key, **record}) + "\n") + out.flush() + small, full = per_op(record), per_op(serial[point_key(*key)]) + ratios = [small[op] / full[op] if full[op] else float("nan") for op in ops] + worst = max(worst, *(abs(r - 1) for r in ratios)) + print(f"| {sketch} | {config} | {param:g} | {card} | " + + " | ".join(f"{r:.3f}" for r in ratios) + " |", flush=True) + print(f"\nLargest deviation from the full-N measurement: {worst:.1%}") + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/complete_saturation_costs.py b/scripts/complete_saturation_costs.py index cababf5e..8aa5af86 100644 --- a/scripts/complete_saturation_costs.py +++ b/scripts/complete_saturation_costs.py @@ -8,8 +8,14 @@ fills the cost columns of saturation.csv from every record. It does not run the exact-baseline crossover. +--n-max is the stream length of the new measurements. Readers of +saturation.csv divide insert CPU by the point's last curve N, so each +record's insert CPU is written scaled to --normalize-n (default: --n-max), +keeping insert CPU per item equal to the record's own CPU / its own N. + Usage: complete_saturation_costs.py GRID_DIR [--jobs 12] [--cost-rows 3] - [--n-max 1e8] [--seed 1] [--binary ./target/release/approxbench] + [--n-max 1e8] [--normalize-n 1e8] [--seed 1] + [--binary ./target/release/approxbench] """ import argparse @@ -40,8 +46,11 @@ def main(): parser.add_argument("--jobs", type=int, default=12) parser.add_argument("--cost-rows", type=int, default=3) parser.add_argument("--n-max", type=float, default=1e8) + parser.add_argument("--normalize-n", type=float, + help="N the insert CPU is scaled to (default: --n-max)") parser.add_argument("--seed", type=int, default=1) args = parser.parse_args() + normalize_n = args.normalize_n or args.n_max csv_path = os.path.join(args.grid_dir, "saturation.csv") cost_path = os.path.join(args.grid_dir, "saturation_cost.jsonl") @@ -82,7 +91,9 @@ def measure(row): for row in rows: record = saved.get(key(row), {}) if measured(row) else {} - row["insert_cpu_secs"] = cpu_secs(record, "insert") + insert = cpu_secs(record, "insert") + size = record.get("workload", {}).get("synthetic", {}).get("description", {}).get("row_num") + row["insert_cpu_secs"] = insert * normalize_n / size if insert != "" and size else insert row["merge_cpu_secs"] = cpu_secs(record, "merge") row["query_cpu_secs"] = cpu_secs(record, "query") row["memory_bytes"] = record.get("memory_bytes", "")