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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
Visible to Intel only — GUID: GUID-1E62BC0B-E2AE-4698-A629-35FF7840A257
Coding for the Intel® Architecture Processors
- 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