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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
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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Getting Credible Performance Numbers
Performance measurements are done on a large number of invocations of the same routine. Since the first iteration is almost always significantly slower than the subsequent ones, the minimum value for the execution time is usually used for final projections. Projections could also be made using other measures such as average or geometric mean of execution time.
An alternative to calling the kernel many times is to use a single “warm-up” run.
The warm-up run might be helpful for small or "lightweight" kernels, for example, the kernels with execution time less than 10 milliseconds. Specifically, it helps to amortize the following potential (one-time) costs:
- Bringing data to the cache
- “Lazy” object creation
- Delayed initializations
- Other costs incurred by the OpenCL™ runtime.
NOTE:
You need to build your performance conclusions on reproducible data. If the warm-up run does not help or execution time still varies, you can try running a large number of iterations and then average the results. For time values that range too much use geomean.
Consider the following:
- For bandwidth-limited kernels, which operate on the data that does not fit in the last-level cache, the ”warm-up” run does not have as much impact on the measurement.
- For a kernel with a small number of instructions executed over a small data set, make sure there is a sufficient number of iterations, so the kernel runs for at least 20 milliseconds.
Kernels that are very lightweight do not give reliable data, so making them artificially heavier could give you important insights into the hotspots. For example, you can add loop in the kernel, or replicate its heavy pieces.
Refer to the “OpenCL Optimizations Tutorial” SDK sample for code examples of performing the warm-up activities before starting performance measurement. You can download the sample from the Intel® SDK for OpenCL Applications website at intel.com/software/opencl/.
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