47 lines
1.3 KiB
Plaintext
47 lines
1.3 KiB
Plaintext
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#include <thrust/device_vector.h>
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#include <thrust/functional.h>
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#include <thrust/host_vector.h>
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#include <thrust/transform_reduce.h>
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#include <cmath>
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#include <iostream>
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// This example computes the norm [1] of a vector. The norm is
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// computed by squaring all numbers in the vector, summing the
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// squares, and taking the square root of the sum of squares. In
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// Thrust this operation is efficiently implemented with the
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// transform_reduce() algorithm. Specifically, we first transform
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// x -> x^2 and the compute a standard plus reduction. Since there
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// is no built-in functor for squaring numbers, we define our own
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// square functor.
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//
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// [1] http://en.wikipedia.org/wiki/Norm_(mathematics)#Euclidean_norm
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// square<T> computes the square of a number f(x) -> x*x
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template <typename T>
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struct square
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{
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__host__ __device__ T operator()(const T& x) const
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{
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return x * x;
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}
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};
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int main()
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{
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// initialize device vector directly
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thrust::device_vector<float> d_x = {1.0, 2.0, 3.0, 4.0};
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// setup arguments
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square<float> unary_op;
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cuda::std::plus<float> binary_op;
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float init = 0;
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// compute norm
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float norm = std::sqrt(thrust::transform_reduce(d_x.begin(), d_x.end(), unary_op, init, binary_op));
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std::cout << "norm is " << norm << '\n';
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return 0;
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}
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