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triton-ascend-case-index-histogram

Optimize expert ID histogram counting via pre-sorting and binary search.

258|48|Updated Jun 22, 2020
Search Tags:#optimization#binary-search#histogram#ascend#pre-sorting#float32#vec-core
One-click install
npx skills add https://github.com/mindspore-ai/akg --skill triton-ascend-case-index-histogram-mindspore-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: triton-ascend-case-index-histogram
Source: https://github.com/mindspore-ai/akg/tree/main/akg_agents/python/akg_agents/op/resources/skills/triton-ascend/cases/triton-ascend-case-index-histogram
Command: npx skills add https://github.com/mindspore-ai/akg --skill triton-ascend-case-index-histogram-mindspore-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Histogram optimization reduces O(n*m) complexity to O(n log n + m log n) for counting expert IDs in large indices.

Core Features & Use Cases

  • Pre-sorting + binary search optimization: sorts the index array once and uses per-expert binary search to locate ranges, lowering overall work.
  • Float32 Vec Core acceleration: converts indices to float32 to leverage Vec Core sort on Ascend for faster sorting and parallel execution.
  • Parallel per-expert processing: enables concurrent histogram calculations across many experts to scale with data size.

Quick Start

Process a dataset of 524,288 elements and 365 experts to apply the histogram optimization and measure performance improvements.

Frequently Asked Questions about triton-ascend-case-index-histogram

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I reduce histogram complexity for counting expert IDs in large indices?▼

Histogram optimization reduces O(n*m) complexity to O(n log n + m log n) by pre-sorting the index array once and using per-expert binary search to locate counting ranges.

How does float32 Vec Core acceleration speed up sorting on Ascend hardware?▼

Float32 Vec Core acceleration converts indices to float32 to leverage Ascend Vec Core sort, enabling faster sorting and parallel execution across per-expert histogram calculations.

Can I use parallel per-expert processing to scale histogram calculations for many experts?▼

Yes, parallel per-expert processing enables concurrent histogram calculations across many experts, allowing the workflow to scale efficiently with large data sizes and expert counts.

What is the best way to integrate Triton kernels for end-to-end histogram optimization?▼

The deterministic workflow enforces pre-sorting, per-expert binary search, and Triton kernel integration to achieve end-to-end histogram optimization for large input sizes and many experts.

Does this histogram optimization approach work with large datasets containing 524,288 elements and 365 experts?▼

Yes, the optimization is designed for large input sizes and many experts, demonstrated by processing a dataset of 524,288 elements and 365 experts to measure performance improvements.

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