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>
16 lines
380 B
JSON
16 lines
380 B
JSON
{
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"cross_session_tokens": 1723017,
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"fractions": {
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"cross": 0.05679481258506571,
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"intra": 0.9321238805590836,
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"shared": 0.011081306855850749,
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"unclassified": 0.0
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},
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"intra_session_tokens": 28278380,
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"shared_prefix_min_sessions": 8,
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"shared_prefix_tokens": 336180,
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"status": "supported",
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"total_cached_tokens": 30371008,
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"unclassified_tokens": 0
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}
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