OpenCL™ Developer Guide for Intel® Core™ and Intel® Xeon® Processors

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ID 773005
Date 10/30/2018
Public
Document Table of Contents
Document Table of Contents x
About This Document
About This Document x
Legal Information 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
Introduction x
About This Document OpenCL™ Standard Basic Concepts Using Data Parallelism
Check-list for OpenCL™ Optimizations x
Use Array Notation with int32 Indices: A[i][j] Use Floating Point for Calculations Note on Local Memory Use Use Branching Accurately Mapping Memory Objects (USE_HOST_PTR) Prefer Buffers over Images Use Lower Math Precision Use Restrict Qualifier for Kernel Arguments
Tips and Tricks for Kernel Development x
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
Application-Level Optimizations x
Avoid Needless Synchronization Reuse Compilation Results with clCreateProgramWithBinary
Debugging OpenCL™ Kernels on Linux* OS x
Enabling Debugging in OpenCL™ CPU Compiler and Runtime Start a Debugging Session Conditional Breakpoints on Work Items
Performance Debugging with Intel® SDK for OpenCL™ Applications x
Performance Debugging Introduction Host-Side Timing Profiling Operations Using OpenCL™ Profiling Events Comparing OpenCL™ and Native Code Performance Getting Credible Performance Numbers Tools for OpenCL™ Development
Coding for the Intel® Architecture Processors x
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
About This Document

Reuse Compilation Results with clCreateProgramWithBinary

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