Over two decades ago, the Java programming language, originally developed by Sun Microsystems, offered developers the promise of being able to build an application once and then have it run on any operating system.
Greg Lavender, CTO of Intel, remembers the original promise of Java better than most, as he spent over a decade working at Sun. Instead of needing to build applications for different hardware and operating systems, the promise of Java was more uniform and streamlined development.
The ability to build once and run anywhere, however, is not uniform across the computing landscape in 2022. It’s a situation that Intel is looking to help change, at least when it comes to accelerated computing and the use of GPUs.
The need for a uniform, Java-like language for GPUs
“Today in the accelerated computing and GPU world, you can use CUDA and then you can only run on an Nvidia GPU, or you can go use AMD’s CUDA equivalent running on an AMD GPU,” Lavender told VentureBeat. “You can’t use CUDA to program an Intel GPU, so what do you use?”
That’s where Intel is contributing heavily to the open-source SYCL specification (SYCL is pronounced like “sickle”) that aims to do for GPU and accelerated computing what Java did decades ago for application development. Intel’s investment in SYCL is not entirely selfless and isn’t just about supporting an open-source effort; it’s also about helping to steer more development toward its recently released consumer and data center GPUs.
SYCL is an approach for data parallel programming in the C++ language and, according to Lavender, it looks a lot like CUDA.
Intel supports standardization for one code to rule them all
To date, SYCL development has been managed by the Khronos Group, which is a multi-stakeholder organization that is helping to build out standards for parallel computing, virtual reality and 3D graphics. On June 1, Intel acquired Scottish development firm Codeplay Software, which is one of the leading contributors to the SYCL specification.
“We should have an open programming language with extensions to C++ that are being standardized, that can run on Intel, AMD and Nvidia GPUs without changing your code,” Lavender said.
Automated tool for converting CUDA into SYCL
Lavender is also a realist and he knows that there is a lot of code already written specifically for CUDA. That’s why Intel developers built an open-source tool called SYCLomatic, which aims to migrate CUDA code into SYCL. Lavender claimed that SYCLomatic today has coverage for approximately 95% of all the functionality that is present in CUDA. He noted that the 5% SYCLomatic doesn’t cover are capabilities that are specific to Nvidia hardware.
With SYCL, Lavender said that there are code libraries that developers can use that are device independent. The way that works is code is written by a developer once, and then SYCL can compile the code to work with whatever architecture is needed, be it for an Nvidia, AMD or Intel GPU.
Looking forward, Lavender said that he’s hopeful that SYCL can become a Linux Foundation project, to further enable participation and growth of the open-source effort. Intel and Nvidia are both members of the Linux Foundation supporting multiple efforts. Among the projects where Intel and Nvidia are both members today is the Open Programmable Infrastructure (OPI) project, which is all about providing an open standard for infrastructure programming units (IPUs) and data processing units (DPUs).
“We should have write once, run everywhere for accelerated computing, and then let the market decide which GPU they want to use, and level the playing field,” Lavender said.
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