triton-ascend-case-index-histogram

What problem does it solve?

Large-scale histogram counting often suffers from O(n×m) complexity when counting occurrences across many categories. This skill proposes a design that pre-sorts the input and uses binary search to reduce to O(n log n + m log n), enabling substantial speedups on big datasets.

Core Features & Use Cases

• Pre-sort input indices and perform per-expert binary searches to count occurrences efficiently.
• Leverage Triton kernels and Vec Core acceleration by operating on float32 data for sorting and parallel counting.
• Use cases include counting occurrences in datasets with tens of thousands to hundreds of thousands of elements and many categories.

Quick Start

Run the Triton-accelerated histogram workflow on your dataset to compute per-expert counts efficiently.