CRITICAL FINDING from reading computility-run.yaml:
--max-num-seqs 1
This means the competition ALWAYS runs single-sequence inference.
All batch-level optimizations (padded_grid_reduction batching,
multi-seq V2 parallelism, batch-wise tensor caching) have ZERO
impact on actual performance.
The real bottleneck is single-sequence KV cache access:
- decode: 1 seq × all heads × all KV blocks
- prefill: 1 seq × chunked (max_num_batched_tokens=8192)
- MoE: 1 seq × top_k=8 experts × 64 layers
Updated muh_cc_dispatch.py to record QWEN36_MAX_NUM_SEQS=1.
CCCL insight from padded_grid_reduction.cu: the padded grid batching
pattern is only beneficial when num_seqs > 1. For single-seq,
the per-sequence loop (range(1)) has zero overhead — the focus
should be on single-sequence tile optimization instead.
CCCL files: thrust/examples/padded_grid_reduction.cu,
cub/block/block_exchange.cuh
