[docs] CCCL scan architecture — lookback vs lookahead, tile_state allocation, grid sizing

Key findings from reading dispatch_scan.cuh:
1. Lookahead scan requires PTX ISA >= 860 (NVIDIA SM100+), completely
   unavailable on BI-V100. Our lookback-only strategy is correct.
2. Lookback scan passes 0 dynamic SMEM — SMEM is all static via
   __shared__. Different from lookahead which uses dynamic stages.
3. Scan launches exactly num_tiles blocks (not sm_count * subscription),
   one CTA per tile. For 100K tokens: ~12 tiles all fit in one wave
   on 16 SMs, explaining why no_delay (dcid=0) is optimal.
4. Lookahead's num_stages auto-tuning is irrelevant for BI-V100 but
   reveals NVIDIA's pipeline depth selection strategy.
This commit is contained in:
project_6
2026-08-05 03:21:47 +00:00
parent b50bd2dfd5
commit 3cc97c1d4e

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@@ -105,3 +105,55 @@ is exactly CCCL's two-pass pattern.
Not controlled by muh. Should be tunable: larger partition = fewer blocks =
less overhead but more work per block. Optimal value depends on SM count.
For 16 SMs: partition_size=1024 may be better (fewer partitions to reduce).
---
## CCCL Scan Architecture (dispatch_scan.cuh)
> Added: 2026-08-04
### Two algorithm paths
**Lookback** (all GPUs including BI-V100):
- Each CTA processes one tile, uses `ScanTileState` in global memory for inter-CTA communication
- Lookback delay policy controls how aggressively CTAs poll predecessors
- SMEM: static only (`__shared__`), passed as `0` dynamic SMEM
- BI-V100 optimal: `no_delay` (dcid=0) because 16 SMs → ~32 CTAs → tile_status fits in 6MB L2
**Lookahead** (SM100+ only, PTX ISA >= 860):
- Pipeline-based with `__pipeline_memcpy_async` and bulk copy
- Uses dynamic SMEM with auto-selected `num_stages`
- **Not available on BI-V100** — requires NVIDIA PTX ISA 860+ instructions
- All lookahead structs in our tuning_scan.cuh can remain empty shells
### ScanTileState allocation
Scan requires `d_temp_storage` for tile status descriptors:
```
tile_size = threads * items
num_tiles = ceil(num_items / tile_size)
temp_bytes = tile_state.AllocationSize(num_tiles)
```
For BI-V100 with 100K tokens and tile_size=384*22=8448:
num_tiles = ceil(100000/8448) = 12 tiles → negligible temp storage.
### Grid size for scan
Lookback scan launches `num_tiles` blocks (one per tile), NOT `sm_count * subscription_factor`.
This is different from reduce, which uses `GridEvenShare`.
For scan, every CTA processes exactly one tile and communicates with neighbors.
With 12 tiles on 16 SMs: all tiles fit in one wave, zero lookback contention.
This is why `no_delay` works on BI-V100 — the entire scan completes in a single wave.
### Lookahead num_stages optimization (SM100 only)
CCCL dynamically selects pipeline depth:
```cpp
max_stages = ceil(num_items / (sm_count * tile_size)) + 1
while (smem_for_stages(num_stages+1) <= max_dynamic_smem) num_stages++
```
For BI-V100 this is irrelevant (no pipeline support), but the formula shows
NVIDIA's strategy: match pipeline depth to problem size / SM count ratio.