mxmaca-api

Provides low-level GPU device management, memory, stream, and kernel-launching APIs.

Updated Aug 23, 2026
One-click install
npx skills add https://github.com/dongg622/china-ai-chip-skill --skill mxmaca-api
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: mxmaca-api
Source: https://github.com/dongg622/china-ai-chip-skill/tree/main/MetaX/mxmaca-api
Command: npx skills add https://github.com/dongg622/china-ai-chip-skill --skill mxmaca-api

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides comprehensive GPU device management, memory handling, stream control, and kernel launching through low-level APIs, simplifying hardware programming tasks.

Core Features & Use Cases

  • Device Management: Query and set active GPUs and access device properties for optimized resource allocation.
  • Memory Operations: Allocate, copy, and free device and host memory efficiently for high-performance computing.
  • Stream & Event Control: Create and synchronize streams and events for fine-grained execution scheduling.
  • Kernel Launch: Deploy GPU kernels with configurable grid, block, shared memory, and stream parameters for tailored computation.

Quick Start

Use the API functions to select a device, allocate memory, create streams, and launch kernels directly within your development environment.

Frequently Asked Questions about mxmaca-api

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

FAQPage Schema
How do I manage GPU device memory and streams for hardware acceleration?

GPU device memory and streams are managed through low-level APIs that handle memory allocation, copying, and stream synchronization to enable efficient hardware acceleration workflows. This Skill provides interfaces for these exact operations.

Can I launch custom GPU kernels with configurable grid and block parameters?

Custom GPU kernels can be launched using APIs that accept configurable grid, block, shared memory, and stream parameters. This allows tailored computation and flexible deployment within development environments.

What low-level APIs are needed for GPU resource monitoring and performance profiling?

Low-level APIs for GPU resource monitoring and performance profiling require device query interfaces and property access functions. This Skill provides device management capabilities to query active GPUs and access device properties for optimized resource allocation.

How do I synchronize execution streams and events for fine-grained GPU scheduling?

Execution streams and events are synchronized using dedicated control APIs for fine-grained GPU scheduling. This Skill enables creating and synchronizing streams and events to ensure proper execution order and dependency management.

Does this approach support allocating and freeing both device and host memory efficiently?

Allocating and freeing both device and host memory is supported through dedicated memory operation APIs. This Skill ensures efficient memory handling for high-performance computing by providing allocation, copy, and free operations.