Historically, accelerating your C++ code with GPUs has not been possible in Standard C++ without using language extensions or additional libraries: In many…
Overview
The article discusses how NVIDIA's NVC++ compiler enables GPU acceleration of Standard C++ code without the need for language extensions or non-standard libraries. It highlights the integration of C++17 parallelism features and the use of execution policies to facilitate efficient parallel programming on NVIDIA GPUs.
What You'll Learn
1
How to use the NVC++ compiler to accelerate Standard C++ code on NVIDIA GPUs
2
Why execution policies are essential for parallel programming in C++17
3
When to apply C++ Parallel Algorithms for optimal performance on GPUs
Prerequisites & Requirements
- Familiarity with C++ programming and parallel computing concepts
- NVIDIA HPC SDK installed on a supported system
Key Questions Answered
How can Standard C++ code be accelerated using NVIDIA GPUs?
NVIDIA's NVC++ compiler allows Standard C++ code to be accelerated on NVIDIA GPUs without requiring language extensions or non-standard libraries. By using execution policies introduced in C++17, developers can write portable code that automatically utilizes GPU resources for parallel execution.
What are the execution policies available in C++17 for parallel algorithms?
C++17 defines four execution policies: std::execution::seq for sequential execution, std::execution::unseq for vectorized execution, std::execution::par for parallel execution, and std::execution::par_unseq for parallel execution with vectorization. These policies help guide the compiler in optimizing algorithm execution.
What limitations exist when using C++ Parallel Algorithms on GPUs?
Limitations include the need for random-access iterators, the inability to use function pointers in GPU code, and restrictions on capturing variables in lambdas. Additionally, exceptions cannot be thrown or caught within GPU code, which can lead to unexpected behavior if not handled properly.
Key Statistics & Figures
Performance improvement of LULESH application
almost seven times faster
when compiled for a single A100 GPU compared to running on all 40 CPU cores of a dual-socket Skylake system.
Technologies & Tools
Software
Nvidia Hpc SDK
Provides the NVC++ compiler for GPU acceleration of C++ applications.
Technology
Cuda Unified Memory
Manages data movement between CPU and GPU memory automatically.
Key Actionable Insights
1Leverage the NVC++ compiler to enhance the performance of existing C++ applications by integrating GPU acceleration.This approach allows developers to maintain code portability while significantly improving computational efficiency, especially for applications with high parallelism.
2Utilize execution policies to optimize algorithm performance in C++17, ensuring that your code can take full advantage of multicore and GPU architectures.By specifying execution policies, developers can guide the compiler to execute algorithms in parallel, which is crucial for performance in data-intensive applications.
3Be mindful of the limitations associated with GPU programming, such as the need for random-access iterators and the prohibition of exceptions in GPU code.Understanding these constraints can help prevent common pitfalls and ensure that your code runs efficiently on GPU architectures.
Common Pitfalls
1
Dereferencing pointers to CPU stack memory in GPU code can lead to memory violations.
This occurs because GPU code cannot access CPU stack memory, so it's essential to ensure that all data accessed by GPU code is allocated on the heap.
2
Using function pointers in GPU-accelerated C++ Parallel Algorithms can cause failures.
Function pointers must point to the correct version of functions for the CPU or GPU, and passing them incorrectly can lead to runtime errors.
Related Concepts
GPU Programming
C++17 Features
Parallel Computing
Execution Policies In C++