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CASS: Nvidia to AMD Transpilation with Data, Models, and Benchmark

Ahmed Heakl, Sarim Hashmi, Gustavo Bertolo Stahl, Seung Hun Eddie Han, Salman Khan, Abdulrahman Mahmoud

2025-05-27

CASS: Nvidia to AMD Transpilation with Data, Models, and Benchmark

Summary

This paper talks about CASS, a new collection of data and tools that help convert computer code written for Nvidia graphics cards so it can run on AMD graphics cards, making sure the converted code works just as well as the original.

What's the problem?

The problem is that programs designed for one type of graphics card, like Nvidia, usually can't run on another type, like AMD, without a lot of extra work. This makes it hard for developers to support different hardware and limits users' choices.

What's the solution?

The authors created CASS, which includes a dataset and a set of models that can automatically translate code from Nvidia to AMD, both at the original programming level and at the more detailed assembly level. Their system is able to match the speed and accuracy of code that was written directly for AMD cards.

Why it matters?

This is important because it allows software and games to work on more types of computers without extra effort, giving people more flexibility and making technology more accessible to everyone.

Abstract

CASS is a dataset and model suite for GPU code transpilation at both source and assembly levels, achieving high accuracy and performance matching with native code.