503009596d
CCCL source read: cub/util_allocator.cuh
CachingDeviceAllocator pre-allocates bins of device memory and reuses
them across kernel invocations. Key insight: avoid repeated cudaMalloc/
cudaFree inside hot loops — allocate once outside, reuse with slicing.
Applied to _forward_prefix_pytorch's online softmax tile loop:
OLD: Each tile iteration allocated 3 new tensors (m_blk, m_new, corr)
via implicit torch operations. With ~16 tiles per context phase +
~16 tiles per chunk phase = ~96 unnecessary CUDA malloc/free calls.
NEW: Pre-allocate _m_blk, _m_new, _corr once outside both Phase loops.
Use torch.amax(out=), torch.maximum(out=), torch.exp(out=) to write
directly into pre-allocated buffers. Zero new allocations per tile.
Also applies to Phase 2 (current-chunk tokens) which has identical
softmax update pattern — same 3 buffers reused across both phases.
BI-V100 impact: 16 SMs with 50GB HBM — CUDA malloc overhead is
proportionally larger than on 148-SM GPUs because the memory controller
has fewer concurrent requests to amortize allocation latency.