The last two releases of CUDA have added support for the powerful new features of C++. In the post The Power of C++11 in CUDA 7 I discussed the importance of…
Overview
The article discusses the integration of modern C++ features into CUDA 7.5, particularly focusing on the Hemi 2 library, which simplifies portable parallel programming for GPUs. It highlights how C++11 features like lambda expressions enhance the ease of writing parallel code that can run on both CPU and GPU.
What You'll Learn
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How to write parallel kernels using Hemi 2 that resemble standard for loops
2
Why GPU Lambda expressions simplify the process of writing GPU functions
3
How to use automatic execution configuration for GPU kernels with Hemi
4
When to utilize grid-stride loops for scalable parallel CUDA kernels
Prerequisites & Requirements
- Basic understanding of C++11 features such as lambda expressions
- CUDA 7.0 or later for device execution
- CUDA 7.5 or later for GPU Lambda support
Key Questions Answered
How do GPU Lambdas enhance parallel programming in CUDA?
GPU Lambdas allow developers to define C++11 Lambda functions with a __device__ annotation, enabling them to be passed to and executed by kernels on the device. This feature simplifies the writing of parallel code, making it almost as straightforward as writing sequential for loops.
What is Hemi 2 and how does it improve CUDA programming?
Hemi 2 is an open-source C++ library designed to facilitate portable CUDA programming. It allows developers to write parallel kernels in a way that resembles standard for loops, automatically managing kernel launch configurations and enabling code to run on both CPU and GPU seamlessly.
What are the benefits of using automatic execution configuration in Hemi?
Automatic execution configuration in Hemi simplifies the process of launching parallel work on GPUs by automatically determining the optimal grid and block sizes based on the GPU's resources. This allows developers to focus on writing parallel code without worrying about the underlying execution details.
When should I use grid-stride loops in CUDA kernels?
Grid-stride loops should be used when writing scalable CUDA kernels that need to decouple the grid size from the data size being processed. This design pattern enhances modularity and reusability of code, making it easier to maintain and debug.
Technologies & Tools
Backend
Cuda
Used for parallel programming on NVIDIA GPUs.
Programming Language
C++11
Provides modern programming features like lambda expressions utilized in Hemi.
Library
Hemi
A library that simplifies writing portable CUDA C/C++ code.
Key Actionable Insights
1Leverage Hemi 2 to simplify your CUDA programming by writing parallel kernels that look like standard for loops.This approach reduces the complexity of GPU programming and makes your code more readable and maintainable, especially for those familiar with C++.
2Utilize GPU Lambdas to encapsulate functionality in a concise manner, enhancing code clarity and reducing boilerplate.By using lambdas, you can pass functions directly to GPU kernels, which streamlines the process of defining and launching parallel tasks.
3Take advantage of automatic execution configuration to optimize your kernel launches without manual tuning.This feature allows you to focus on algorithm development rather than the intricacies of GPU architecture, making it easier to achieve high performance.
Common Pitfalls
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Failing to properly utilize the automatic execution configuration can lead to suboptimal performance.
Developers may attempt to manually configure kernel launches, which can complicate code and lead to inefficiencies. Relying on Hemi's automatic configurations allows for better resource utilization.
Related Concepts
Parallel Programming
C++11 Features
GPU Architecture
Cuda Programming