Understanding Global and Local Work Size in OpenCL: FAQs
OpenCL is a powerful framework for parallel programming that allows developers to write code that executes across multiple computing devices. One of the key features of OpenCL is its ability to work with data in parallel, dividing up tasks into smaller pieces that can be executed simultaneously. In order to do this, OpenCL uses two important concepts: global and local work size.
What is Global Work Size?
Global work size refers to the total number of work items that will be executed in parallel by an OpenCL kernel. This is typically set by the developer and is based on the size of the input data or the number of iterations required. The global work size is used to determine the number of computing devices needed to execute the kernel and the number of work items that will be assigned to each device.
What is Local Work Size?
Local work size refers to the number of work items that will be executed in parallel on each computing device. This is also set by the developer and is used to optimize the performance of the kernel. By dividing the global work size into smaller local work sizes, developers can ensure that each computing device is being used to its full potential.
How are Global and Local Work Size Related?
Global and local work size are related in that the global work size must be evenly divisible by the local work size. This ensures that each computing device is assigned an equal number of work items and that all work items are executed in parallel. If the global work size is not evenly divisible by the local work size, some computing devices will be idle while others are still executing work items. This can lead to decreased performance and longer execution times.
Understanding global and local work size is essential for optimizing the performance of OpenCL kernels. By setting the global work size based on the size of the input data and dividing it into smaller local work sizes, developers can ensure that their code is executing efficiently and utilizing all available computing resources. With this knowledge, developers can take full advantage of the power of OpenCL to create high-performance parallel applications.