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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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Host-Side Timing
The following code snippet is a host-side timing routine around a kernel call (error handling is omitted):
float start = …;//getting the first time-stamp
clEnqueueNDRangeKernel(g_cmd_queue, …);
clFinish(g_cmd_queue);// to make sure the kernel completed
float end = …;//getting the last time-stamp
float time = (end-start);
In this example, host-side timing is implemented using the following functions:
- clEnqueueNDRangeKernel puts a kernel to a queue and immediately returns
- clFinish explicitly indicates the completion of kernel execution. You can also use clWaitForEvents.
Remember to profile operations on memory objects separately.