OpenCL™ Developer Guide for Intel® Core™ and Intel® Xeon® Processors
ID
773005
Date
10/30/2018
Public
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
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
Avoid Extracting Vector Components
Consider the following kernel:
__constant float4 oneVec = (float4)(1.0f, 1.0f, 1.0f, 1.0f);
__kernel __attribute__((vec_type_hint(float4)))
void inverter2(__global float4* input, __global float4* output)
{
int tid = get_global_id(0);
output[tid] = oneVec – input[tid];
output[tid].w = input[tid].w;
output[tid] = sqrt(output[tid]);
}
For this example of the explicit vector code, the extraction of the w component is very costly. The reason is that the next vector operation forces reloading the same vector from memory. Consider loading a vector once and performing all changes by use of vector operations even for a single component.
In this specific case, two changes are required:
- Modify the oneVec, so that its w component is zero, causing only a sign flip in the w component of the input vector.
- Use float representation to manually flip the sign bit of the w component back.
As a result, the kernel appears as follows:
__constant float4 oneVec = (float4)(1.0f, 1.0f, 1.0f, 0.0f);
__constant int4 signChanger = (int4)(0, 0, 0, 0x80000000);
__kernel __attribute__((vec_type_hint(float4)))
void inverter3(__global float4* input, __global float4* output)
{
int tid = get_global_id(0);
output[tid] = oneVec – input[tid];
output[tid] = as_float4(as_int4(output[tid]) ^ signChanger);
output[tid] = sqrt(output[tid]);
}
At the cost of another constant vector, this implementation performs all the required operations addressing only full vectors. All the computations might also be performed in float8.