Mirrors the existing decode 4-cases study for prefill on the LLaMA-3.1-70B
single-KV-head group (h_q=8, h_kv=1, d_head=128) across 8 cubes × 8 PEs:
Case 1 Cube-SP × PE-TP → KV S_kv-Ring across cubes; Q/O T_q-split 64-way
Case 2 Cube-Repl × PE-TP → full KV per cube; only CUBE 0's P PEs split T_q
Case 3 Cube-Repl × PE-SP → full KV per cube; PEs SP on S_kv (intra-cube AR)
Case 4 Cube-SP × PE-SP → KV split 64-way + Q T_q-split across cubes
(Ring KV + per-cube intra-CUBE AR) ★ optimal
Cases 2 and 3 use Q-axis tiling (TILE_Q) so the per-PE scratch is bounded
by TILE_Q × TILE_S_KV regardless of T_q (the full Q tensor would be
G·T_q·d_head·2 bytes which scales linearly with T_q and blew the 1 MB
budget at T_q≥128 without tiling).
Per-panel try/except in run_sweep tolerates per-panel failures so
sweep.json always lands with whichever cases succeed plus a failures
list. Case 4 uses configure_sfr_intercube_ring with the snake submesh
(2,4) which installs both the Ring E/W lanes and the intra_* lanes
needed for the intra-CUBE reduce — single SFR config for all 4 cases.
The plot script generates 4 PNGs (latency, traffic, memory, parallelism)
into 1H_milestone_output/gqa/long_ctx/. The parallelism chart shows
total compute work (active_PE × T_q_per_PE × S_kv_processed) — exposes
Case 3's 8× redundancy vs. Cases 1/2/4 which all do unique work.
gqa_prefill_long_ctx_4cases.py exposes run_sweep() rather than a @bench
decorator — the umbrella bench in the next commit invokes it.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
