Tag: GPU

30
Sep
2020
Posted in technology

Intel, Heidelberg University team up to bring Radeon GPU support to AI

A smiling robot looks at the camera.
Enlarge / Machine learning doesn’t actually have a face. But if it ever does, we’d like that face to look something like this.

We’ve been following Intel’s oneAPI—an artificial intelligence development platform designed to abstract hardware away from the task of developing AI code—with great interest since its launch last November. This week, Intel and the Heidelberg University Computing Center (URZ) announced a new Academic Center of Excellence (CoE), which will support and conduct research on the oneAPI platform.

The new collaboration immediately followed Intel’s own announcement of the oneAPI specification reaching 1.0 status. The 1.0 milestone is significant, since it enables collaborators to focus on adapting hardware to a standard, fixed implementation, worrying about the specification itself shifting rapidly beneath their feet.

URZ’s own announcement of the oneAPI Center of Excellence begins by reiterating the raison d’etre of oneAPI itself:

URZ will focus its research and programming efforts on a fundamental high-performance computing (HPC) challenge where modern computers utilize different types of hardware for different calculations. Accelerators, including graphics processing units (GPUs) and field programmable gate arrays (FPGAs), are used in combination with general compute processors (CPUs). Using different types of hardware make computers very powerful and provide versatility for a wide range of situations and workloads. Hardware heterogeneity, however, complicates software development for these computers, especially when specialized components from a variety of vendors are used in tandem.

One major reason for this complication is that many accelerated compute architectures require their own programming models. Therefore, software developers need to learn and use a different—and sometimes proprietary—language for each processing unit in a heterogeneous system, which increases complexity and limits flexibility.

oneAP’s cross-architecture language Data Parallel C++ (DPC++), based on Khronos Groups’ SYCL standard for heterogeneous programming in C++, overcomes these challenges with its single, unified open development