triton-ascend-case-elemwise-broadcast-2d

To optimize 2D broadcast operations in Triton on Ascend devices, load divisors once within the kernel and broadcast them across large axes while tuning NUM_BLOCKS and SUB_M settings to balance parallelism and UB utilization.

2D broadcast division causes low UB utilization on Ascend Triton kernels due to memory fragmentation when processing large broadcast axes, which is mitigated by loading divisors once and broadcasting them within the kernel.

You need to tune NUM_BLOCKS and SUB_M for 2D broadcast operations when handling 2D shapes with a large broadcast axis and a small non-broadcast axis, ensuring kernel-level parallelism and UB utilization are balanced.

Yes, you can use Triton to accelerate large-scale 2D broadcast division on Ascend hardware by implementing an optimized kernel that fixes NUM_BLOCKS and SUB_M to maximize throughput while preserving numerical correctness.

The optimization of 2D broadcast patterns with fixed NUM_BLOCKS in Triton is limited to 2D shapes with a large broadcast axis and a comparatively small non-broadcast axis, requiring explicit tuning of SUB_M for efficient data reuse.

The best way to reduce fragmentation during 2D broadcast operations on Ascend is to load divisors once and broadcast them within the Triton kernel, improving UB utilization on large broadcast axes while keeping non-broadcast axes efficient.