Int x = blockIdx.x * blockDim.x + threadIdx.x Remap workgroup and thread ID to an x-y coordinate on a 2D raster _global_ void mandelbrot(uint8_t* output, int width, int height) First, we need routines to multiply two complex numbers, add two complex numbers, and compute the squared magnitude of a complex number: Briefly, let's perform a quick scan of the CUDA code. Here, we'll be porting the venerable Mandelbrot fractal generator as we're more interested in learning the DPC++ programming model itself. The first order of business is to select a CUDA application to port for demonstration purposes. There are subjective reasons why one might prefer to write DPC++ code as well, namely, DPC++ programs read as semantically correct C++, without needing foreign syntax or attributes you might be accustomed to coming from CUDA. For example, with a few commands, you can easily benchmark your application against both an Arria 10 FPGA and a Xeon Platinum. In particular, much of the hardware available is cutting edge and perhaps impractical to experiment on at home or in the office. Perhaps the greatest potential benefit is the ability to deploy oneAPI software to the Intel DevCloud, a cloud environment providing CPUs, GPUs, and FPGAs at your disposal. Third, it's worth porting code to DPC++ to at least understand how the general programming model works, which may translate to new insights into how best to architect code that requires acceleration in the future. Intel is very keen on bringing work on DPC++ upstream to the LLVM project, which would have immediate impact on the value of the various parallel STL algorithms. Second, DPC++ is built on top of Clang and open source standards produced by Khronos. In short, there are a few compelling advantages to Intel's platform worth considering.įirst, DPC++ can target FPGA accelerators as easily as it can target GPUs. You might be wondering why we'd want to do such a port, given CUDA's widespread usage in the community for image analysis, machine learning, and more. DPC++ is a compiler built on LLVM's Clang compiler, extending modern C++ capabilities with SYCL, an open standard designed to allow C++ applications to target heterogeneous systems. The "oneAPI" toolkits refer to the Data Parallel C++ (or DPC++ for short) programming model along with a number of APIs intended to support high-performance computing applications. In this article, we'll be explaining how one might port CUDA code to Intel's oneAPI toolkits, and in particular, port a CUDA kernel to Intel's DPC++ compiler. The additional benefits to adopting CUDA are immediate access to a wide array of existing libraries, as well as the use of a number of tools to both debug and visualize CUDA code. It is widely used among researchers and industry practitioners to accelerate computationally-heavy workloads, without needing to adopt a wholly unfamiliar workflow and programming model compared to traditional software development. CUDA is an Nvidia-owned, parallel computing platform and programming model to run software on GPUs.
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