What is a work group in OpenCL?

What is a work group in OpenCL?

NDRangeKernel Work-Item within a Work-Group Execution In an OpenCL application, the body of a kernel function expresses the computation to be completed for a single work-item. That variability will be based on the hardware architecture of the device on which the work-items are executing.

What is wavefront in OpenCL?

Therefore, a wavefront is a group of work items. Because a compute unit on a GCN processor has 64 processing elements, a wavefront typically has 64 work items. The division of work group into wavefronts is handled internally by OpenCL and hardware. Developers cannot directly control wavefronts.

What is kernel in OpenCL?

A kernel is essentially a function written in the OpenCL language that enables it to be compiled for execution on any device that supports OpenCL. The kernel is the only way the host can call a function that will run on a device. When the host invokes a kernel, many work items start running on the device.

What is NDRange in OpenCL?

NDRange describes the space of work-items, which can be 1-, 2- or 3-dimensional. Each work-item then executes the kernel code (usually on different data depending on its position in the work-item space).

How do I start OpenCL?

The main steps of a host program is as follows:

1. Get information about the platform and the devices available on the computer (line 42)
2. Select devices to use in execution (line 43)
3. Create an OpenCL context (line 47)
4. Create a command queue (line 50)
5. Create memory buffer objects(line 53-58)

What is Windows OpenCL?

OpenCL™ (Open Computing Language) is a low-level API for heterogeneous computing that runs on CUDA-powered GPUs. In addition to OpenCL, NVIDIA supports a variety of GPU-accelerated libraries and high-level programming solutions that enable developers to get started quickly with GPU Computing.

What is wavefront in GPU?

In all GCN-GPUs, a “wavefront” consists of 64 threads, and in all Nvidia GPUs a “warp” consists of 32 threads. AMD’s solution is to attribute multiple wavefronts to each SIMD-VU. Wavefronts are attributed per SIMD-VU.

What is OpenCL good for?

Advantages. OpenCL provides abstract memory and portability, due to its run-time execution model. The OpenCL kernel can run on any supported software implementation. OpenCL supports a heterogeneous system architecture that enables efficient communication between the GPU and the processor using C++ 17 atomics.

What is OpenCL used for?

OpenCL™ (Open Computing Language) is a low-level API for heterogeneous computing that runs on CUDA-powered GPUs. Using the OpenCL API, developers can launch compute kernels written using a limited subset of the C programming language on a GPU.

Is OpenCL written in C?

OpenCL Programming Model An OpenCL application is split into host and device parts with host code written using a general programming language such as C or C++ and compiled by a conventional compiler for execution on a host CPU. All versions of the OpenCL C language are based on C99.

How many work items are there in OpenCL?

Work-items Each work-item in OpenCL is a thread in terms of its control flow, and its memory model. The hardware may run multiple work-items on a single thread, and you can easily picture this by imagining four OpenCL work-items operating on the separate lanes of an SSE vector.

How are work groups assigned in OpenCL applications?

The work-groups for an NDRangeKernel submission can be started in any order, they can be completed in any order and they can be assigned to any core on the device. In an OpenCL application, the body of a kernel function expresses the computation to be completed for a single work-item.

How are work item functions used in Khronos?

Built-in work-item functions can be used to query the number of dimensions, the global and local work size specified to clEnqueueNDRangeKernel, and the global and local identifier of each work-item when this kernel is being executed on a device.

What’s the thread concept in OpenCL 2.0?

It would simply be compiler trickery that achieves that, and on GPUs it tends to be a mixture of compiler trickery and hardware assistance. OpenCL 2.0 actually exposes this underlying hardware thread concept through sub-groups, so there is another level of hierarchy to deal with.