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Getting Help and Support
Introduction
Check-list for OpenCL™ Optimizations
Tips and Tricks for Kernel Development
Application-Level Optimizations
Debugging OpenCL™ Kernels on Linux* OS
Performance Debugging with Intel® SDK for OpenCL™ Applications
Coding for the Intel® Architecture Processors
Why Optimizing Kernels Is Important?
Avoid Spurious Operations in Kernels
Avoid Handling Edge Conditions in Kernels
Use the Preprocessor for Constants
Prefer (32-bit) Signed Integer Data Types
Prefer Row-Wise Data Accesses
Use Built-In Functions
Avoid Extracting Vector Components
Task-Parallel Programming Model Hints
Common Mistakes in OpenCL™ Applications
Introduction for OpenCL™ Coding on Intel® Architecture Processors
Vectorization Basics for Intel® Architecture Processors
Vectorization: SIMD Processing Within a Work Group
Benefitting from Implicit Vectorization
Vectorizer Knobs
Targeting a Different CPU Architecture
Using Vector Data Types
Writing Kernels to Directly Target the Intel® Architecture Processors
Work-Group Size Considerations
Threading: Achieving Work-Group Level Parallelism
Efficient Data Layout
Using the Blocking Technique
Intel® Turbo Boost Technology Support
Global Memory Size
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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 (or average, geometric mean, and so on) value for the execution time is usually used for final projections.
An alternative to calling kernel several times is using a single “warm-up” run.
The warm-up run might be helpful for kernels with small amount of computations, as 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:
NOTE: You need to make your performance conclusions on reproducible data. If warm-up run does not help or execution time still varies, try running large number of iterations and then average the results. For time values that range too much, consider using geomean.
Consider the following:
- For bandwidth-limited kernels, operating on the data that does not fit in the last-level cache, the warm-up run does not improve the stability of measurement significantly.
- 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 that the kernel run time is at least 20 milliseconds for CPU device.
- Kernels with smaller run time might provide unreliable data, so increasing the amount of computations artificially gives you important insights into the hotspots. For example, you can add loop in the kernel, or replicate some pieces.
Refer to the “OpenCL™ Optimizations Tutorial” SDK sample for code examples of performing warm-up run before starting performance measurement.