triton-ascend-case-reduction-mean-large

Name: triton-ascend-case-reduction-mean-large
Availability: InStock
Author: MindSpore

Optimize large-scale mean reductions on the last axis for Triton on Ascend Atlas A2/A3.

258|48|Updated Jun 22, 2020

Search Tags:#performance-tuning #triton #autotune #ascend #mean-reduction #2d-reduction #kernel-tiling

Ready to run

npx skills add https://github.com/mindspore-ai/akg --skill triton-ascend-case-reduction-mean-large-mindspore-ai

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: triton-ascend-case-reduction-mean-large
Source: https://github.com/mindspore-ai/akg/tree/main/akg_agents/python/akg_agents/op/resources/skills/triton-ascend/cases/triton-ascend-case-reduction-mean-large
Command: npx skills add https://github.com/mindspore-ai/akg --skill triton-ascend-case-reduction-mean-large-mindspore-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill optimizes large-scale mean reductions along the last axis using row-wise tiling to reduce kernel thread blocks and in-kernel partitioning to avoid UB, targeting Ascend Atlas A2/A3 with Triton for 2D reductions where the reducing axis is large.

Core Features & Use Cases

Row-wise tiling to reduce total thread blocks and improve cache locality.
In-kernel sub-blocking to avoid cache/buffer overflows and ensure stable performance.
Autotune-ready configurations to explore optimal BLOCK_SIZE_M, SUB_BLOCK_SIZE_M, and BLOCK_SIZE_N for different grid sizes.
Use Case: large-scale mean reductions on prominent 2D tensors where the reduce axis dominates.

Quick Start

Run the Triton Ascend kernel with tuned BLOCK_SIZE_M, SUB_BLOCK_SIZE_M, and BLOCK_SIZE_N for your input shapes to achieve optimal performance.

Frequently Asked Questions about triton-ascend-case-reduction-mean-large

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

FAQPage Schema

How do I optimize large-scale mean reductions on Ascend using Triton?▼

Optimize large-scale mean reductions on Ascend by applying row-wise tiling to reduce thread blocks and in-kernel sub-blocking to avoid cache overflows. This approach targets 2D tensors where the reducing axis dominates.

Does Triton autotune work for 2D reduction kernels on Atlas A2 or A3?▼

Yes, Triton autotune works for 2D reduction kernels on Atlas A2/A3 by exploring configurations for BLOCK_SIZE_M, SUB_BLOCK_SIZE_M, and BLOCK_SIZE_N to find optimal grid settings for peak performance.

Why does my Triton kernel hit UB overflows during large 2D mean reductions?▼

UB overflows during large 2D mean reductions occur when in-kernel data exceeds buffer limits. Applying in-kernel sub-blocking partitions the reduction data to avoid cache and buffer overflows, ensuring stable performance.

What are the best block size parameters for Triton mean reduction on Ascend?▼

The best block size parameters for Triton mean reduction on Ascend are BLOCK_SIZE_M, SUB_BLOCK_SIZE_M, and BLOCK_SIZE_N. These must be tuned via autotune to match your specific input shapes and grid sizes.

When should I use row-wise tiling for mean reduction on Ascend hardware?▼

Use row-wise tiling for mean reduction on Ascend when processing 2D tensors with a moderate non-reducing axis and a large reducing axis. This technique improves cache locality and reduces total kernel thread blocks.