triton-ascend-case-elemwise-zeros

To reduce kernel launch overhead for small tensor creation on Ascend, use a Triton-based optimization strategy that coalesces work into a single core with a fitting BLOCK_SIZE, leveraging tl.zeros and tl.store to achieve higher per-core performance.

Multi-core parallelism underperforms for small-shape tensor zeros on Ascend Atlas A2 because kernel launch overhead outweighs computation time, making single-core processing with targeted block scheduling significantly faster for tiny tensors.

Yes, you can optimize elementwise tensor creation like zeros and arange for small shapes using Triton by configuring a BLOCK_SIZE strategy with tl.zeros and tl.store to improve cache utilization and vectorization on Ascend backends.

The best way to handle elementwise creation for tensors with shapes like (2, 256, 16) on Ascend is to use single-core Triton kernels that reduce active kernel launches and apply per-thread-block scheduling for better performance.

This small tensor optimization approach works for common creation operations including zeros, arange, full, and zeros_like on Ascend Atlas A2/A3 backends, specifically targeting workloads with shapes in the thousands of elements.

You should avoid multi-core parallelism for tensor creation on Ascend when dealing with small-shape tensors in the thousands of elements, as the disproportionate kernel launch overhead makes single-core coalesced work more efficient.