Gahow Wang
0c3220cbb8
analysis/characterization/window_1_results.md is the headline write-up for Window 1: workload characterization (KV per request, real reuse decomposition, APC theoretical ceilings), B3 5-policy sweep with per-policy interpretation, B2 same-vs-different-worker interference microbench with causal reading, and an explicit list of what Window 1 does *not* answer (deferred to B4 SRR sweep + B5 attribution). Under window_1_results/: - 5 raw result JSONs from the B3 sweep, the B2 microbench, the APC upper bound, and the KV footprint - per-policy hotspot_index.json snapshots so render_window1_figures.py can plot per-worker TTFT p90 distributions - 8 PNG figures (figures/) covering the headline claims Three takeaways the figures pin down: 1) intra-session reuse dominates (93.2%), so session-affinity routing is the right primary lever 2) unified hybrid affinity hits 79.4% APC (97% of the 79.6% intra- session ceiling) AND cuts TTFT p90 from lmetric's 15.6s to 7.24s 3) B2 different-worker control sits at idx ≈ 1.0 across 32× prefill- size variation; same-worker TTFT idx scales 2.15× -> 218×, which is the cleanest causal evidence for same-worker prefill-decode interference Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
137 lines
4.1 KiB
JSON
137 lines
4.1 KiB
JSON
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"usage_note": "Full trace analysis can be run CPU-only on dash0, or the needed JSONL files can be copied/rsynced from dash0 to this machine before running this analyzer."
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},
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"request_id",
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"latency_s",
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"ttft_s",
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"tpot_s",
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"error",
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],
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"hash_ids",
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"outputs": [
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"append_delta_stats.json",
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"invalid_runs.md",
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"kv_footprint_summary.json",
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"manifest.json",
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"raw/merged_requests.jsonl",
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"raw/unmatched_breakdown.jsonl",
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"raw/unmatched_metrics.jsonl",
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"reuse_decomposition.json",
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"session_arrival_stats.json",
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"session_concurrency.json",
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"session_skew.json",
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"trace_profile.json",
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"turn_interval_stats.json",
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"workload_summary.json"
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]
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}
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