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I've been looking into libraries/extensions for C++ that will allow GPU-based processing on a high level. I'm not an expert in GPU programming and I don't want to dig too deep. I have a neural network consisting of classes with virtual functions. I need a library that basically does the GPU allocation for me - on a high level. There is a guy who wrote a thesis on a system called GPU++ which does most of the GPU stuff for you. I can't find the code anywhere, just his thesis.
Does anyone know of a similar library, or does anyone have the code for GPU++? Libraries like CUDA are too low level and can't handle most of my operations (at least not without rewriting all my processes and algorithms - which I don't want to do).
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Using the CUDA Toolkit you can accelerate your C or C++ applications by updating the computationally intensive portions of your code to run on GPUs. To accelerate your applications, you can call functions from drop-in libraries as well as develop custom applications using languages including C, C++, Fortran and Python.
For example, GPU programming has been used to accelerate video, digital image, and audio signal processing, statistical physics, scientific computing, medical imaging, computer vision, neural networks and deep learning, cryptography, and even intrusion detection, among many other areas.
Open-source vs commercial Generally if the app of your choice supports both CUDA and OpenCL, going with CUDA is the best option as it generates better performance results in this scenario. This is because NVIDIA provides top quality support.
The problem is porting algorithms to utilize the GPU most efficiently. This means taking into account SIMD architecture, warps, different kinds of memory, ... It's easy to port code to run on the GPU, it's not easy to actually make it run faster than a general purpose CPU.
There are many high-level libraries dedicated to GPGPU programming. Since they rely on CUDA and/or OpenCL, they have to be chosen wisely (a CUDA-based program will not run on AMD's GPUs, unless it goes through a pre-processing step with projects such as gpuocelot).
You can find some examples of CUDA libraries on the NVIDIA website.
Thrust is a parallel algorithms library which resembles the C++ Standard Template Library (STL). Thrust's high-level interface greatly enhances programmer productivity while enabling performance portability between GPUs and multicore CPUs. Interoperability with established technologies (such as CUDA, TBB, and OpenMP) facilitates integration with existing software.
As @Ashwin pointed out, the STL-like syntax of Thrust makes it a widely chosen library when developing CUDA programs. A quick look at the examples shows the kind of code you will be writing if you decide to use this library. NVIDIA's website presents the key features of this library. A video presentation (from GTC 2012) is also available.
CUB provides state-of-the-art, reusable software components for every layer of the CUDA programming mode. It is a flexible library of cooperative threadblock primitives and other utilities for CUDA kernel programming.
It provides device-wide, block-wide and warp-wide parallel primitives such as parallel sort, prefix scan, reduction, histogram etc.
It is open-source and available on GitHub. It is not high-level from an implementation point of view (you develop in CUDA kernels), but provides high-level algorithms and routines.
This library is mostly used for machine learning, and relies on expression templates.
Starting from Eigen 3.3, it is now possible to use Eigen's objects and algorithms within CUDA kernels. However, only a subset of features are supported to make sure that no dynamic allocation is triggered within a CUDA kernel.
Note that OpenCL does more than GPGPU computing, since it supports heterogeneous platforms (multi-core CPUs, GPUs etc.).
The OpenACC Application Program Interface describes a collection of compiler directives to specify loops and regions of code in standard C, C++ and Fortran to be offloaded from a host CPU to an attached accelerator, providing portability across operating systems, host CPUs and accelerators.
Bolt is a C++ template library optimized for heterogeneous computing. Bolt is designed to provide high-performance library implementations for common algorithms such as scan, reduce, transform, and sort. The Bolt interface was modeled on the C++ Standard Template Library (STL). Developers familiar with the STL will recognize many of the Bolt APIs and customization techniques.
Boost.Compute: as @Kyle Lutz said, Boost.Compute provides a STL-like interface for OpenCL. Note that this is not an official Boost library (yet).
SkelCL "is a library providing high-level abstractions for alleviated programming of modern parallel heterogeneous systems". This library relies on skeleton programming, and you can find more information in their research papers.
Although this is not really in the scope of this question, there is also the same kind of support for other programming languages:
If you need to do linear algebra (for instance) or other specific operations, dedicated math libraries are also available for CUDA and OpenCL (e.g. ViennaCL, CUBLAS, MAGMA etc.).
Also note that using these libraries does not prevent you from doing some low-level operations if you need to do some very specific computation.
Finally, we can mention the future of the C++ standard library. There has been extensive work to add parallelism support. This is still a technical specification, and GPUs are not explicitely mentioned AFAIK (although NVIDIA's Jared Hoberock, developer of Thrust, is directly involved), but the will to make this a reality is definitely there.
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The Thrust library provides containers, parallel primitives and algorithms. All of this functionality is nicely wrapped up in a STL-like syntax. So, if you are familiar with STL, you can actually write entire CUDA programs using just Thrust, without having to write a single CUDA kernel. Have a look at the simple examples in the Quick Start Guide to see the kind of high-level programs you can write using Thrust.
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