gpgpu

2 Articles

CUDA, But Make It AMD

July 16, 2024 by 40 Comments

Compute Unified Device Architecture, or CUDA, is a software platform for doing big parallel calculation tasks on NVIDIA GPUs. It’s been a big part of the push to use GPUs for general purpose computing, and in some ways, competitor AMD has thusly been left out in the cold. However, with more demand for GPU computation than ever, there’s been a breakthrough. SCALE from [Spectral Compute] will let you compile CUDA applications for AMD GPUs.

SCALE allows CUDA programs to run as-is on AMD GPUs, without modification. The SCALE compiler is also intended as a drop-in swap for nvcc, right down to the command line options. For maximum ease of use, it acts like you’ve installed the NVIDIA Cuda Toolkit, so you can build with cmake just like you would for a normal NVIDIA setup. Currently, Navi 21 and Navi 31 (RDNA 2.0 and RDNA 3.0) targets are supported, while a number of other GPUs are undergoing testing and development.

The basic aim is to allow developers to use AMD hardware without having to maintain an entirely separate codebase. It’s still a work in progress, but it’s a promising tool that could help break NVIDIA’s stranglehold on parts of the GPGPU market.

 

Peering Inside The GPU Black Box

March 30, 2016 by 27 Comments

Researchers at Binghamton University have built their own graphics processor unit (GPU) that can be flashed into an FGPA. While “graphics” is in the name, this GPU design aims to provide a general-purpose computing peripheral, a GPGPU testbed. Of course, that doesn’t mean that you can’t play Quake (slowly) on it.

The Binghamton crew’s design is not only open, but easily modifiable. It’s a GPGPU where you not only know what’s going on inside the silicon, but also have open-source drivers and interfaces. As Prof. [Timothy Miller] says,

 It was bad for the open-source community that GPU manufacturers had all decided to keep their chip specifications secret. That prevented open source developers from writing software that could utilize that hardware. With contributions from the ‘open hardware’ community, we can incorporate more creative ideas and produce an increasingly better tool.

That’s where you come in. [Jeff Bush], a member of the team, has a great blog with a detailed walk-through of a known GPU design. All of the Verilog and C++ code is up on [Jeff]’s GitHub, including documentation.

If you’re interested in the deep magic that goes on inside GPUs, here’s a great way to peek inside the black box.