Legal Information Getting Help and Support Introduction Coding for the Intel® Processor Graphics Platform-Level Considerations Application-Level Optimizations Optimizing OpenCL™ Usage with Intel® Processor Graphics Check-list for OpenCL™ Optimizations Performance Debugging Using Multiple OpenCL™ Devices Coding for the Intel® CPU OpenCL™ Device OpenCL™ Kernel Development for Intel® CPU OpenCL™ device
Mapping Memory Objects Using Buffers and Images Appropriately Using Floating Point for Calculations Using Compiler Options for Optimizations Using Built-In Functions Loading and Storing Data in Greatest Chunks Applying Shared Local Memory Using Specialization in Branching Considering native_ and half_ Versions of Math Built-Ins Using the Restrict Qualifier for Kernel Arguments Avoiding Handling Edge Conditions in Kernels
Using Shared Context for Multiple OpenCL™ Devices Sharing Resources Efficiently Synchronization Caveats Writing to a Shared Resource Partitioning the Work Keeping Kernel Sources the Same Basic Frequency Considerations Eliminating Device Starvation Limitations of Shared Context with Respect to Extensions
Why Optimizing Kernel Code Is Important? Avoid Spurious Operations in Kernel Code Perform Initialization in a Separate Task Use Preprocessor for Constants Use Signed Integer Data Types Use Row-Wise Data Accesses Tips for Auto-Vectorization Local Memory Usage Avoid Extracting Vector Components Task-Parallel Programming Model Hints
Work-Group Size Considerations
It is recommended to let the OpenCL™ implementation automatically determine the optimal work-group size for a kernel: pass NULL for a pointer to the work-group size when calling clEnqueueNDRangeKernel.
If you want to experiment with work-group size, you need to consider the following:
- To get best performance from using the vectorization module (see the "Benefitting from Implicit Vectorization" section), the work-group size must be larger or a multiple of 8.
- To reduce the overhead of maintaining a workgroup, you should create work-groups that are as large as possible, which means 64 and more work-items. One upper bound is the size of the accessed data set as it is better not to exceed the size of the L1 cache in a single work group. Also there should be sufficient number of work-groups, see the "Work-Group Level Parallelism" section for more information.
- To accommodate multiple architectures, query the device for the CL_KERNEL_PREFERRED_WORK_GROUP_SIZE_MULTIPLE parameter by calling to clGetKernelWorkGroupInfo, and set the work-group size accordingly.
- If your kernel code contains the barrier instruction, the issue of work-group size becomes a tradeoff. The more local and private memory each work-item in the work-group requires, the smaller the optimal work-group size is. The reason is that a barrier also issues copy instructions for the total amount of private and local memory used by all work-items in the work-group in the work-group since the state of each work-item that arrived at the barrier is saved before proceeding with another work-item.
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