triton-ascend-example-relu

Implement a Triton Ascend elementwise ReLU kernel with 1D tiling and boundary masking.

6|1|Updated Apr 19, 2026

Search Tags:#gpu #kernel #pytorch #triton #ascend #relu #elementwise

Ready to run

npx skills add https://github.com/xchang1121/AutoResearch-CC-hook --skill triton-ascend-example-relu

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: triton-ascend-example-relu
Source: https://github.com/xchang1121/AutoResearch-CC-hook/tree/main/skills/triton-ascend/examples/triton-ascend-example-relu
Command: npx skills add https://github.com/xchang1121/AutoResearch-CC-hook --skill triton-ascend-example-relu

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill demonstrates how to implement and validate a Triton Ascend-based elementwise ReLU kernel, providing a concrete example for high-performance kernel development on Ascend hardware.

Core Features & Use Cases

1D tiling and BLOCK_SIZE-based processing to maximize throughput.
Boundary masking to handle arbitrary input lengths safely.
Interleaved execution and PyTorch integration for easy testing and reuse.
Use Case: reference for building other elementwise ops on Ascend devices.

Quick Start

Run the triton-ascend-example-relu tutorial to execute the kernel on a sample input tensor.

Frequently Asked Questions about triton-ascend-example-relu

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

FAQPage Schema

How do I implement an elementwise ReLU kernel on Ascend devices using Triton?▼

Implement an elementwise ReLU kernel on Ascend devices using Triton by applying 1D tiling, setting BLOCK_SIZE and CORE_NUM parameters, and integrating boundary masking for safe memory access.

How does boundary masking work for elementwise operations in Triton Ascend?▼

Boundary masking in Triton Ascend handles arbitrary input lengths safely by preventing out-of-bounds memory access during 1D tiling, ensuring correct elementwise operations across varying tensor sizes.

Can I test Triton Ascend kernels using PyTorch?▼

Yes, you can test Triton Ascend kernels using PyTorch integration, which allows you to execute the elementwise ReLU kernel on sample input tensors and validate performance and correctness.

What is the best way to maximize throughput for elementwise ops on Ascend hardware?▼

Maximize throughput for elementwise ops on Ascend hardware by using 1D tiling with BLOCK_SIZE-based processing and interleaved execution, optimizing parallel kernel performance and memory access.

Do I need to specify CORE_NUM when building Triton kernels for Ascend?▼

Yes, specifying CORE_NUM is required when building Triton kernels for Ascend to define functional requirements, ensure correct memory access, and enable interleaved execution for performance.

Why does my Triton Ascend ReLU kernel fail on non-standard input lengths?▼

Triton Ascend ReLU kernels fail on non-standard input lengths without proper boundary masking, which is necessary to handle arbitrary tensor dimensions safely during 1D tiling execution.