在Java中运行可运行的CUDA代码的最简单方法是什么?

IMO? JavaCPP. For example, here is a port to Java of the example displayed on the main page of Thrust's Web site:import com.googlecode.javacpp.*;import com.googlecode.javacpp.annotation.*;@Platform(include={"<thrust/host_vector.h>", "<thrust/device_vector.h>", "<thrust/generate.h>", "<thrust/sort.h>", "<thrust/copy.h>", "<thrust/reduce.h>", "<thrust/functional.h>", "<algorithm>", "<cstdlib>"})@Namespace("thrust")public class ThrustTest { static { Loader.load(); } public static class IntGenerator extends FunctionPointer { static { Loader.load(); } protected IntGenerator() { allocate(); } private native void allocate(); public native int call(); } @Name("plus<int>") public static class IntPlus extends Pointer { static { Loader.load(); } public IntPlus() { allocate(); } private native void allocate(); public native @Name("operator()") int call(int x, int y); } @Name("host_vector<int>") public static class IntHostVector extends Pointer { static { Loader.load(); } public IntHostVector() { allocate(0); } public IntHostVector(long n) { allocate(n); } public IntHostVector(IntDeviceVector v) { allocate(v); } private native void allocate(long n); private native void allocate(@ByRef IntDeviceVector v); public IntPointer begin() { return data(); } public IntPointer end() { return data().position((int)size()); } public native IntPointer data(); public native long size(); public native void resize(long n); } @Name("device_ptr<int>") public static class IntDevicePointer extends Pointer { static { Loader.load(); } public IntDevicePointer() { allocate(null); } public IntDevicePointer(IntPointer ptr) { allocate(ptr); } private native void allocate(IntPointer ptr); public native IntPointer get(); } @Name("device_vector<int>") public static class IntDeviceVector extends Pointer { static { Loader.load(); } public IntDeviceVector() { allocate(0); } public IntDeviceVector(long n) { allocate(n); } public IntDeviceVector(IntHostVector v) { allocate(v); } private native void allocate(long n); private native void allocate(@ByRef IntHostVector v); public IntDevicePointer begin() { return data(); } public IntDevicePointer end() { return new IntDevicePointer(data().get().position((int)size())); } public native @ByVal IntDevicePointer data(); public native long size(); public native void resize(long n); } public static native @MemberGetter @Namespace IntGenerator rand(); public static native void copy(@ByVal IntDevicePointer first, @ByVal IntDevicePointer last, IntPointer result); public static native void generate(IntPointer first, IntPointer last, IntGenerator gen); public static native void sort(@ByVal IntDevicePointer first, @ByVal IntDevicePointer last); public static native int reduce(@ByVal IntDevicePointer first, @ByVal IntDevicePointer last, int init, @ByVal IntPlus binary_op); public static void main(String[] args) { // generate 32M random numbers serially IntHostVector h_vec = new IntHostVector(32 << 20); generate(h_vec.begin(), h_vec.end(), rand()); // transfer data to the device IntDeviceVector d_vec = new IntDeviceVector(h_vec); // sort data on the device (846M keys per second on GeForce GTX 480) sort(d_vec.begin(), d_vec.end()); // transfer data back to host copy(d_vec.begin(), d_vec.end(), h_vec.begin()); // compute sum on device int x = reduce(d_vec.begin(), d_vec.end(), 0, new IntPlus()); }}尽管如此，您在C语言中的代码应该更容易映射.Your code in C should be easier to map though.我们可以使用以下命令来编译此文件并在Linux x86_64上运行，或者通过适当地修改-properties选项在其他受支持的平台上运行:We can get this compiled and running on Linux x86_64 with these commands, or on other supported platforms by modifying the -properties option appropriately:$ javac -cp javacpp.jar ThrustTest.java$ java -jar javacpp.jar ThrustTest -properties linux-x86_64-cuda$ java -cp javacpp.jar ThrustTest 这篇关于在Java中运行可运行的CUDA代码的最简单方法是什么?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！ 上岸，阿里云！