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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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Using Floating Point for Calculations
Intel® Graphics device is much faster for floating-point add, sub, mul and so on in compare to the int type.
For example, consider the following code that performs calculations in type int4:
__kernel void amp (__constant uchar4* src, __global uchar4* dst)
…
uint4 tempSrc = convert_uint4(src[offset]);//Load one RGBA8 pixel
//some processing
uint4 value = (tempSrc.z + tempSrc.y + tempSrc.x);
uint4 tempDst = value + (tempSrc - value) * nSaturation;
//store
dst[offset] = convert_uchar4(tempDst);
}
Below is its float4 equivalent:
__kernel void amp (__constant uchar4* src, __global uchar4* dst)
…
uint4 tempSrc = convert_uint4(src[offset]);//Load one RGBA8 pixel
//some processing
float4 value = (tempSrc.z + tempSrc.y + tempSrc.x);
float4 tempDst = mad(tempSrc – value, fSaturation, value);
//store
dst[offset] = convert_uchar4(tempDst);
}
Intel® Advanced Vector Extensions (Intel® AVX) support (if available) accelerates floating-point calculations on the modern CPUs, so floating-point data type is preferable for the CPU OpenCL device as well.
NOTE:
The compiler can perform automatic fusion of multiplies and additions. Use compiler flag -cl-mad-enable to enable this optimization when compiling for both Intel® Graphics and CPU devices. However, explicit use of the "mad" built-in ensures that it is mapped directly to the efficient instruction.