When working with the Portland Group (PGI) C++ compiler, you have access to several compiler-specific extensions that can enhance your programming experience. These extensions provide additional features and optimizations that are not available in standard C++.

In this blog post, we will explore some of the most useful PGI C++ compiler-specific extensions and how they can be utilized in your code.

__ppl_target__

The __ppl_target__ extension allows you to specify target devices for parallel programming. With this extension, you can take advantage of multi-threading and accelerator devices, such as GPUs, to improve the performance of your code.

Here is an example of how to use the __ppl_target__ extension:

#pragma omp target teams distribute parallel for thread_limit(256) __ppl_target__
for (int i = 0; i < size; ++i) {
    // Code to be executed in parallel
}

In this code snippet, we are using OpenMP directives in conjunction with the __ppl_target__ extension to offload the parallel computation to an accelerator device, such as a GPU.

__pgi_vector

The __pgi_vector extension enables vectorization for loops and computations. By utilizing this extension, you can take advantage of vector instruction sets, such as SSE or AVX, to perform parallel mathematical operations on data.

Here is an example of how to use the __pgi_vector extension:

#pragma acc parallel loop vector_length(8) gang worker __pgi_vector__
for (int i = 0; i < size; ++i) {
    // Vectorized computation using SIMD instructions
}

In this code snippet, we are using OpenACC directives in conjunction with the __pgi_vector extension to enable vectorization for the loop. The vector_length(8) directive instructs the compiler to use vector registers of length 8 for parallel computations.

By using the __pgi_vector extension, you can significantly enhance the performance of numerical computations that involve large arrays or matrices.

Conclusion

The Portland Group C++ compiler offers several compiler-specific extensions that can greatly improve the performance of your code. By utilizing extensions such as __ppl_target__ for parallel programming and __pgi_vector__ for vectorization, you can take full advantage of multi-threading, accelerator devices, and vector instruction sets.

Keep in mind that these extensions are specific to the PGI compiler and may not be supported by other compilers. It is always recommended to consult the PGI compiler documentation for detailed usage instructions and compatibility information.

#programming #c++ #pgi #compiler #extensions