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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 Data Parallelism
The OpenCL™ standard basic data parallelism uses the Single Program Multiple Data (SPMD) technique. SPMD resembles fragment processing with pixel shaders in the context of graphics.
In this programming model, a kernel executes concurrently on multiple elements. Each element has its own data and its own program counter. If elements are vectors of any kind (for example, four-way pixel values for an RGBA image), consider using vector types.
This section describes how to convert regular C code to an OpenCL program using a simple "hello world" example. Consider the following C function:
void scalar_mul(int n, const float *a, const float *b, float *result)
{
int i;
for (i = 0; i < n; ++i)
result[i] = a[i] * b[i];
}
This function performs element-wise multiplication of two arrays, a and b. Each element in result stores the product of the corresponding elements from arrays a and b.
Consider the following:
- The for loop consists of two parts: the loop statement that defines the range of operation (a single dimension containing n elements), and the loop body itself.
- The basic operation is done on scalar variables (float data types).
- Loop iterations are independent.
The same function in OpenCL appears as follows:
__kernel void scalar_mul(__global const float *a,
__global const float *b,
__global float *result)
{
int i = get_global_id(0);
result[i] = a[i] * b[i];
}
The kernel function performs the same basic element-wise multiplication of two scalar variables. The index is provided by use of a built-in function that gives the global ID, a unique number for each work-item within the grid defined by the NDRange.
The code itself does not imply any parallelism. Only the combination of the code with the execution over a global grid implements the parallelism of the device.
This parallelization method abstracts the details of the underlying hardware. You can write your code according to the native data types of the algorithm. The implementation takes care of the mapping to specific hardware.