Data Parallel C++: An Open Alternative for Cross-Architecture Development | oneAPI | Intel Software
58Although Data Parallel C++ is a new programming language, its programs are actually all C++ programs. For a majority of developers, this means a much shorter learning curve to parallel programming productivity.
Data Parallelism—aka data parallel compute—is no longer a new thing. It is THE programming model for most compute-intense applications and solutions running on multicore systems, including those that drive AI, machine learning, and video processing.
And according to Intel Senior Fellow Geoff Lowney, it will likely remain the dominant compute pattern for the next 10 years.
The challenge, then, is helping developers express parallelism more easily across the expanse of hardware architectures—CPUs for sure, but also GPUs and FPGAs and VPUs and IPUs and … you get the picture. To do this, a new language is needed.
That language is Data Parallel C++ (DPC++), a key part of Intel’s oneAPI initiative and an extension of familiar C++ that enables new ways to express parallelism for cross-architecture development.
In this 12-minute video, Geoff sits down with Tech.Decoded to discuss DPC++ and what you need to know, including:
• Does DPC++ require separate host and kernel code?
• Why use DPC++ for heterogeneous parallelism vs adopting OpenCL™ or CUDA*?
• Do my legacy C++ programs need updating to take advantage of DPC++? If so, how much?
• Can I combine DPC++, Threading Building Block, Parallel STL, and OpenMP* in the same program?
• Will DPC++ features eventually become part of the C++ standard?
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Data Parallel C++: An Open Alternative for Cross-Architecture Development | oneAPI | Intel Software
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