Root cause: cuMemAllocAsync (stream-ordered memory pools) produces
memory that cublasLtMatmul rejects with CUBLAS_STATUS_NOT_SUPPORTED
on H100 (CUDA 13.0 driver) when the matmul runs on a different
stream than the allocation. Despite CUDA documentation stating
same-device async allocations should be accessible from all streams
with proper event synchronization, cublasLtMatmul disagrees.
Proof chain:
- C++ test with cudaMalloc: ALL dimensions pass on H100 ✓
- Rust test with cuMemAlloc: ALL dimensions pass on H100 ✓
- Rust test with cudarc alloc_zeros (cuMemAllocAsync): FAILS ✗
- Same dimensions, same handle, same workspace — only alloc differs
Fix: set has_async_alloc=false unconditionally in CudaContext::new().
This forces cuMemAlloc_v2 for ALL allocations. Zero performance impact
(allocations at construction time, not in hot loops).
Also: converted has_async_alloc from bool to AtomicBool for safety.
Removed diagnostic code (CUBLASLT_DIAG, test_lt_matmul_raw).
Reverted cuMemAlloc workspace hack (now uses normal alloc_zeros).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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