Moreover, APUs feature a unified CPU-GPU memory, which while on one hand helps alleviate the impact of the PCI bus on GPU applications performance, it adds more OpenCL programming complexity as different memory access modes are introduced.
#Opencl benchmark for 2016 amd graphics cards code
In order to deploy legacy scientific applications on hardware accelerators, OpenCL requires extensive programming efforts and often a high number of lines of code in the original code. HiCL: An OpenCL Abstraction Layer for Scientific Computing, Application to Depth Imaging on GPU and APU Issam Said (Lip6), Pierre Fortin, Jean-Luc Lamotte (Université Pierre et Marie Curie) and Henri Calandra (Total EP) The generated application will then be used on different platforms with no modification to the source code. Taking advantage of SYCL “single-source programming style”, VisionCpp allows programmers to easily develop custom vision operations in C++.
VisionCpp supports compile-time construction and optimisation of OpenCL kernels by using SYCL as a back-end architecture and is ideal for embedded platforms as it prevents the unpredictable run-time construction and memory usage required for OpenCL kernels of vision applications. In this presentation, we propose a high-level CV framework called VisionCpp that supports the performance portability of the developed CV applications over different OpenCL-enabled platforms. Envisioning the Future – Using SYCL to Develop Vision Tools Luke Iwanski and Mehdi Goli (Codeplay).Īlthough high-level libraries like OpenCV abstracts both the system-level and kernel-level optimisations of built-in operations over heterogeneous platforms, it can still be difficult for a programmer to develop a custom vision operation across different platforms.
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