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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
See Also
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
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Using Buffers and Images Appropriately
On both CPU and Intel® Graphics devices, buffers usually perform better than images: more data transfers per read or write operation for buffers with much lower latency.
If your algorithm does not require linear data interpolation or specific border modes, consider using buffers instead of images. Still, if your legacy code uses images, or if you want to use the linear interpolation ability of the sampler, consider using the cl_khr_image2d_from_buffer extension which offers creating zero copy image aliases for the buffers.
To improve performance on the Intel Graphics, do the following:
- Consider using images if you need linear interpolation between pixel values.
- Consider using images for irregular access patterns. For example, use buffers when processing in memory (in row-major) order. Yet prefer image2D and texture sampling your access pattern is other than simple linear. For example, a kernel that reads diagonally or generally irregular positions.
- Use local memory for explicit caching of data values rather than relying on sampler’s caches, as the caches do not support image write operations.
Notice however, that for (even mildly large) look-up tables the regular global memory is preferable over local memory.
- Use constant samplers to be able to specify and optimize the sampling behavior in compile time.
- Consider using the CL_ADDRESS_CLAMP_NONE as it is the fastest addressing mode, and use CL_ADDRESS_CLAMP_TO_EDGE rather than CL_ADDRESS_CLAMP.
In general, image2D sampling on the Intel Graphics offers:
- Free type conversions. For example, uchar4 to uint4 (unavailable on CPU).
- Automatic handling image boundaries (slower on the CPU).
- Fast bilinear sampling (works slow on CPU; may vary between devices).
Notice that images are software-emulated on a CPU. So, make sure to choose the fastest interpolation mode that meets your needs. Specifically:
- Nearest-neighbor filtering works well for most (interpolating) kernels.
- Linear filtering might decrease CPU device performance.