During his talk, Macfarlane recounted some significant examples that highlighted the effectiveness of oneAPI and DPC++ relative to CUDA. In one example, the Zuse Institute Berlin took code for a tsunami simulation workload called easyWave, which was originally written for Nvidia GPUs using CUDA, and automatically converted that code to DPC++ using Intel’s DPC++ Compatibility Tool .
A 4% performance loss won’t get many people excited enough to convert from CUDA to DPC++, even if they acknowledge that a little tuning might achieve even better performance, so Macfarlane provided a more convincing example. Codeplay took N-body kernel code written in CUDA for Nvidia GPUs and converted it into SYCL code using DPCT.
Macfarlane explained that there are two optimization levels available to developers for making DPC++ code run even faster: auto tuning, which selects the “best” algorithm from available libraries, and hand tuning using platform-specific optimization guidelines. There’s yet another optimization tool available to developers when targeting Intel CPUs and accelerators – the VTune Profiler – which is Intel’s widely used and highly respected performance analysis and power optimization tool.
The open oneAPI platform offers two major benefits: multivendor compatibility and portability across different types of hardware accelerators. Multivendor compatibility means that the same code can run on hardware from AMD, Intel, Nvidia, or any other hardware vendor for which a compatible compiler is available.
After Intel acquires Codeplay, it remains to be seen how well the new Intel subsidiary continues to support accelerator hardware from non-Intel vendors. Given Curley’s remarks quoted above and the open nature of oneAPI, it's quite possible that Codeplay will continue to support multiple hardware vendors. Not only would this be the right thing to do for developers, it also hands Gelsinger an important set of metrics to measure any Intel XPU group that produces accelerator chips.
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