Concurrent programming in OpenCL vs Grand Central Dispatch

Question

With the introduction of OpenCL 2.0, OpenCL seems to have many Grand Central Dispatch (GCD) features, such as CLang / Apple style blocks and queues. Looking at their respective feature sets, I wonder if OpenCL can do everything GCD / libdispatch can do, but with the added ability to direct computations to the GPU as well as to the processor - or if GCD can offer more, which is separate from OpenCL.

In particular, my questions are:

• What distinguishes concurrency from GCD and OpenCL?

• If it makes sense to use them together (provided that GCD offers additional features), can C-blocks be redirected either to the GCD queue or to the OpenCL queue? And if targeting the CPU, will there be a reason to go through OpenCL vs direct execution

• Does OpenCL 2.0 support GCD-style load balancing that can fill threads spanning both the processor and GPU?

Answer 1

In its current form, OpenCL is able to solve both parallel parallel parallel parallel tasks and proofs of various OpenCL API primitives for working in a queue:

• clEnqueuNDRangeKernel: Used to host a kernel with the size of an N-dimensional workgroup. Commonly used for parallel data processing.
• clEnqueueTask: , . , , clEnqueueNDRangeKernel 1. OpenCL 2.0
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Answer 2

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Answer 3

"OpenCL has vector data types and vector built-in functions that allow you to directly reference the potential of SIMD architecture without using a compiler" - Erik Duymelinck

Apple introduced for iOS 8 and OS X Yosemite

• 2D, 3D, 4D Vector mach and geometry.
• Featues of Metal in C, Obj-C and C ++ on the processor
• Abstraction on SIMD architecture-specific types and internal capabilities.