Copyright (C) 2011-2015 Biomedizinische NMR Forschungs GmbH
Aura is a modern, header-only C++ library for accelerator development. Aura works with both OpenCL and CUDA backends. The Aura API is not stable yet (alpha version).
Aura has only a few core concepts:
The device type is the starting point for all interactions with accelerators. It represents the device context known from OpenCL and CUDA.
There are two layers of abstraction for memory management. A typed pointer to device memory device_ptr<T> is the low level memory abstraction. The type contains a raw pointer that represents a location in accelerator memory and a reference to a device. To enable pointer arithmetic, the OpenCL implementation of device_ptr<T> also contains an offset from the base pointer.
device d(0);
device_ptr<int> ptr = device_malloc<int>(16, d);
device_free(ptr);
The second memory management layer provides containers. A device_array<T> extends the device_ptr<T> type with the bounds type. bounds defines a multidimensional discrete space. A device_array<T> represents a continuous block of device memory of size 
device d(0);
device_array<int> array1(40, d);
device_array<int> array2(bounds(40, 20, 10), d);
Commands such as memory transfers and kernel invocations are issued to an accelerator through feeds. A feed is a queue of commands that specifies both location and order of execution of commands. A feed is always associated with a single device. There can exists multiple feeds for each device. Commands issued to different feeds of the same device execute concurrently and in the same memory address space. If supported by the accelerator, concurrent commands can execute in parallel. The wait_for function and wait method block the caller until all commands issued to a feed have finished.
std::vector<float> src(product(bounds(40, 20)));
device d(0);
device_array<float> dst(bounds(40, 20), d);
feed f(d);
copy(src, dst, f);
f.wait(); /* blocking until copy finished */
wait_for(f); /* alternative to f.wait(); */
The copy function is the only function required to transfer data between host and accelerator and between accelerators. Since the compiler can discriminate between accelerator and host memory, the correct copy function is dispatched at compile time. Both an iterator/pointer based and a range based interface are supported.
copy(src.begin(), src.end(), dst.begin(), f);
copy(src, dst, f);
Both CUDA and OpenCL define the number of accelerator threads for each kernel invocation. These threads can be partitioned into groups that communicate among themselves to some degree. How the total number of running threads is calculated is different in CUDA and OpenCL. The following table shows that CUDA calculates the size of the kernel space as level 1 partitioning times level 2 partitioning. In OpenCL, the kernel space and the level 3 partitioning are equivalent. The table further shows the nomenclature proposed in the Aura library.
| OpenCL | CUDA | Aura
-----------------|--------------|---------------|------------
smallest entity | work item | thread | fiber
level 1 | local work | block | bundle
level 2 | global work | grid | mesh
kernel space | global work | grid * block | mesh
The invoke function parameterizes and enqueues kernels in a feed. Its first argument is a kernel object created from a module. The second argument specifies the number of fibers that should be launched using a mesh. The third argument partitions the mesh in bundles of fibers. The fourth argument is a tuple
containing arguments that should be passed to the kernel. The last argument specifies the feed the kernel should be enqueued in.
int xdim = 128; int dimy = 64;
device d(0);
module m = create_module_from_file("k.cl", d);
kernel k = create_kernel(m, "simple_add");
device_array<int> v(bounds(xdim, dimy), d);
feed f(d);
invoke(k, mesh(dimy, dimx), bundle(dimx), args(vec.data()), f);
wait_for(f);
If the space of fibers can be partitioned arbitrarily, that is, if the kernel contains no synchronization points, kernel invocation can be simplified. The invoke function can determine how to best partition the fiber-space based on platform properties and heuristics.
invoke(k, bounds(dimx, dimy), args(v.data()), f);
/* or: */
invoke(k, v.get_bounds(), args(v.data()), f);
A mark allows orchestrating and synchronizing multiple feeds. A mark is inserted into a feed. It can either be waited for from the calling thread or another feed can be instructed to wait for a mark through its continue_when member.
device d(0);
feed f1(d);
feed f2(d);
mark m1;
insert(f1, m1);
/* or: */
mark m2(f2);
wait_for(m1);
f1.continue_when(m2);
Aura provides a wrapper for the cuFFT and clFFT libraries.
bounds b(128, 128);
std::vector<std::complex<float>> hv0(product(b), std::complex<float>(41., 42.));
// upload to device
device_array<cfloat> dv0(b, d);
device_array<cfloat> dv1(b, d);
copy(hv0, dv0, f);
// calculate inverse transform
fft_initialize();
fft fh(d, f, b, fft::type::c2c);
fft_inverse(dv0, dv1, fh, f);
fft_terminate();
// download from device
std::vector<cfloat> hv1(product(b), cfloat(0.0, 0.0));
copy(dv1, hv1, f);
wait_for(f);
Aura contains also a wrapper for FFTW. The code to call FFTW is very similar to the accelerator FFT library wrapper:
bounds b(128, 128);
std::vector<std::complex<float>> in(product(b), std::complex<float>(41., 42.));
std::vector<std::complex<float>> out(product(b), std::complex<float>(0., 0.));
// calculate inverse transform
fftw::fft_initialize();
fftw::fft fh(b, fftw::fft::type::c2c);
fftw::fft_inverse(in, out, fh);
fftw::fft_terminate();
Authors: Sebastian Schaetz seb.schaetz@gmail.com, Christian Holme and Tilman Sumpf.
Contributors: Andreas Merrem, Dirk Voit, Jakob Klosowski, Maaike van Zalk, Markus Untenberger, Oleksandr Kalentev, Volkert Roeloffs, Xiaoqing Wang, Zhengguo Tan, Zhongshuai Zhang
Distributed under the Boost Software License, Version 1.0. (See accompanying file LICENSE.md or copy at boost.org/LICENSE_1_0.txt)