文件列表:

CMakeLists.txt (3354, 2011-08-09)
LICENSE_1_0.txt (1338, 2011-08-09)
bench/ (0, 2011-08-09)
bench/CMakeLists.txt (1943, 2011-08-09)
bench/algorithm/ (0, 2011-08-09)
bench/algorithm/CMakeLists.txt (258, 2011-08-09)
bench/algorithm/fft.cpp (7191, 2011-08-09)
bench/micro/ (0, 2011-08-09)
bench/micro/CMakeLists.txt (408, 2011-08-09)
bench/micro/devicecopy.cu (6015, 2011-08-09)
bench/micro/invoke.cpp (12194, 2011-08-09)
bench/micro/meminfo.cu (746, 2011-08-09)
bench/micro/reduce.cu (16539, 2011-08-09)
bench/stopwatch.cpp (4727, 2011-08-09)
bench/stopwatch.hpp (650, 2011-08-09)
doc/ (0, 2011-08-09)
doc/Makefile (4582, 2011-08-09)
doc/_static/ (0, 2011-08-09)
doc/_static/nature.css (4095, 2011-08-09)
doc/conf.py (7061, 2011-08-09)
doc/gettingstarted.rst (3090, 2011-08-09)
doc/index.rst (1750, 2011-08-09)
doc/make.bat (4507, 2011-08-09)
doc/rationale.rst (1526, 2011-08-09)
doc/tutorial.rst (9596, 2011-08-09)
example/ (0, 2011-08-09)
example/CMakeLists.txt (1546, 2011-08-09)
example/broadcast.cpp (816, 2011-08-09)
example/environment.cpp (634, 2011-08-09)
example/fft.cpp (1195, 2011-08-09)
example/invoke_kernel.cpp (999, 2011-08-09)
example/simple_seg_dev_vector.cpp (779, 2011-08-09)
example/simple_transfer.cpp (1038, 2011-08-09)
include/ (0, 2011-08-09)
include/boost/ (0, 2011-08-09)
include/boost/assert.hpp (4268, 2011-08-09)
include/boost/thread/ (0, 2011-08-09)
include/boost/thread/barrier.hpp (2376, 2011-08-09)
... ...

MGPU ==== The MGPU library strives to simplify the implementation of high performance applications and algorithms on multi-GPU systems. Its main goal is both to abstract platform dependent functions and vendor specific APIs, as well as simplifying communication between different compute elements. The library is currently an alpha release containing only limited yet already useful functionality. The documentation is available [here](http://sschaetz.github.com/mgpu/). News ---- * 2011-08-04 MGPU v0.1 has been released, download [bz2](https://github.com/sschaetz/mgpu/raw/archives/mgpu_0_1.tar.bz2) or [zip](https://github.com/sschaetz/mgpu/raw/archives/mgpu_0_1.zip) archives or fork the code from github Examples -------- The following example shows how a batched FFT can be calculated in parallel on all available GPUs in a system. ```cpp #include #include #include #include #include using namespace mgpu; int main(void) { environment e; { unsigned int dim = 128; unsigned int batch = 15; std::size_t blocksize = dim*dim; std::size_t size = blocksize*batch; std::vector > host_in(size, std::complex(0)); std::vector > host_out(size, std::complex(0)); std::generate(host_in.begin(), host_in.end(), rand); seg_dev_vector > in(size, blocksize); seg_dev_vector > out(size, blocksize); copy(host_in, in.begin()); // plan 2D FFT batch with dimension and batch fft, std::complex > f(dim, dim, batch); f.forward(in, out); f.inverse(out, in); // fetch result copy(in, host_out.begin()); synchronize_barrier(); } } ``` This example shows how a vector can be distributed across all devices and how a kernel can be invoked to operate on the local data. ```cpp #include #include #include #include #include #include #include using namespace mgpu; // generate random number float random_number() { return ((float)(rand()%100) / 100); } // axpy CUDA kernel code __global__ void axpy_kernel( float const a, float * X, float * Y, std::size_t size) { unsigned int i = blockIdx.x * blockDim.x + threadIdx.x; if (i < size) Y[i] = (a * X[i]) + Y[i]; } // axpy CUDA kernel launcher void axpy(float const a, dev_range X, dev_range Y) { int threads = 256; int blocks = (X.size() + T - 1) / T; axpy_kernel<<< blocks, threads >>>(a, X.get_raw_pointer(), Y.get_raw_pointer(), Y.size()); } int main(void) { const std::size_t size = 1024; environment e; { std::vector X(size), Y(size); float const a = .42; std::generate(X.begin(), X.end(), random_number); std::generate(Y.begin(), Y.end(), random_number); seg_dev_vector X_dev(size), Y_dev(size); copy(X, X_dev.begin()); copy(Y, Y_dev.begin()); // calculate on devices invoke_kernel_all(axpy, a, X_dev, Y_dev); copy(Y_dev, Y.begin()); synchronize_barrier(); // result is now in Y } } ``` Please refer to the [documentation](http://sschaetz.github.com/mgpu/>) for further examples and for information on how to get started.

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