The use of accelerators is increasing every year, with software developers taking advantage of GPUs in particular to run a variety of HPC and AI algorithms on highly parallel systems. The data center accelerator market is projected to grow from $13.7 billion in 2021 to $65.3 billion by 2026 according to research from MarketsandMarkets1. During the past decade or so software developers have largely been bound to CUDA* to write highly parallel software that can make use of GPUs that, whilst originally designed for graphics processing, are now being used in a wide range of disciplines that include AI and machine learning. The challenge with this approach for software developers is that CUDA is a proprietary programming interface and can only be used to run on processors from NVIDIA. This ties organizations into a single vendor and limits the ability to innovate with the latest processor architectures.

There is an ever-growing number of accelerators in the world. This raises the question of how various ecosystems will evolve to allow programmers to leverage these accelerators. At higher levels of abstraction, domain-specific layers like Tensorflow and PyTorch provide great abstractions to the underlying hardware. However, for developers who maintain code that talks to the hardware without such an abstraction the challenge still exists. One solution that is supported on multiple underlying hardware architectures is C with SYCL.

This week is the eighth annual International Workshop on OpenCL, SYCL, Vulkan, and SPIR-V, and the event is available online for the very first time in its history thanks to the coronavirus pandemic. One of the event organizers, and the conference chair, is Simon McIntosh-Smith, who is a professor of high performance computing at Bristol University in Great Britain and also the head of its Microelectronics Group. Among other things, McIntosh-Smith was a microprocessor architect at STMicroeletronics, where he designed SIMD units for the dual-core, superscalar Chameleon and SH5 set-top box ASICs back in the late 1990s. McIntosh-Smith moved to Pixelfusion in 1999, which created the first general purpose GPU – arguably eight or nine years before Nvidia did it with its Tesla GPUs, where he was an architect on the 1,536-core chip and software manager for two years. In 2002, McIntosh-Smith was one of the co-founders of ClearSpeed, which created floating point math accelerators used in HPC systems before GPU accelerators came along, and was first director of architecture and applications and then vice president of applications.