Thrust vectorized search: Efficiently combine lower_bound and binary_search to find both position and existence

Question

I'm trying to use Thrust to detect if each element of an array can be found in another array and where (both arrays are sorted). I came across the vectorized search routines (lower_bound and binary_search).

lower_bound will return for each value the index where it could be inserted in a list respecting its ordering.

I also need to know if the value is found or not (which can be done with binary_search), not just its position.

Is it possible to achieve both efficiently without making two searches (calling binary_search and then lower_bound)?

I know in the scalar case, lower_bound will return a pointer to end of the array if a value cannot be found, but this does not happens in the vectorized version.

Thanks!

Carrillo

Answer 1

You can check that the element that lower_bound returns is the same as the one you searched for. E.g. given a = {1,3,5} and searching for b = {1,4}, the result will be c = {0,2}. We have a[c[0]] == b[0], so b[0] is in a, but a[c[1]] != b[1] so b[1] is not in a.

(Note that you will need to ensure that you don't make any out-of-bounds memory accesses, since lower_bound can return an index that is beyond the end of the array.)

Phosphide

Answer 2

@tat0: you can also play around with Arrayfire: vectorized search using lower_bound() does not give you the answer immediately while with setintersect() in arrayfire, you get the "intersection" of two arrays directly:

float A_host[] = {3,22,4,5,2,9,234,11,6,17,7,873,23,45,454};
int szA = sizeof(A_host) / sizeof(float); 

float B_host[] = {345,5,55,6,7,8,19,2,63}; 
int szB = sizeof(B_host) / sizeof(float); 

// initialize arrays from host data
array A(szA, 1, A_host);
array B(szB, 1, B_host);

array U = setintersect(A, B); // compute intersection of 2 arrays

int n_common = U.elements();
std::cout << "common: ";     
print(U);

the output is: common: U = 2.0000 5.0000 6.0000 7.0000

to get the actual locations of these elements in array A, you can use the following construct (provided that elements in A are unique):

int n_common = U.elements();
array loc = zeros(n_common); // empty array      

gfor(array i, n_common) // parallel for loop
     loc(i) = sum((A == U(i))*seq(szA));
print(loc);

then: loc = 4.0000 3.0000 8.0000 10.0000

Furthermore, thrust::lower_bound() seems to be slower than setintersect(), i benchmarked it with the following program:

int *g_data = 0;
int g_N = 0;

void thrust_test() {
 thrust::device_ptr<int> A = thrust::device_pointer_cast((int *)g_data),
     B = thrust::device_pointer_cast((int *)g_data + g_N);
 thrust::device_vector<int> output(g_N);
 thrust::lower_bound(A, A + g_N, B, B + g_N, 
                  output.begin(),
                  thrust::less<int>());
 std::cout << "thrust: " << output.size() << "\n";
}
void af_test() 
{   
  array A(g_N, 1, g_data, afDevicePointer);
  array B(g_N, 1, g_data + g_N, afDevicePointer);
  array U = setintersect(A, B);
  std::cout << "intersection sz: " << U.elements() << "\n";
}
int main()
{
  g_N = 3e6; // 3M entries
  thrust::host_vector< int > input(g_N*2);
  for(int i = 0; i < g_N*2; i++) {  // generate some input
    if(i & 1)
       input[i] = (i*i) % 1131;
    else
       input[i] = (i*i*i-1) % 1223 ;
 }
 thrust::device_vector< int > dev_input = input;
 // sort the vector A
 thrust::sort(dev_input.begin(), dev_input.begin() + g_N);
 // sort the vector B
 thrust::sort(dev_input.begin() + g_N, dev_input.begin() + g_N*2);
 g_data = thrust::raw_pointer_cast(dev_input.data());
 try {
    info();
    printf("thrust:  %.5f seconds\n", timeit(thrust_test));
    printf("af:  %.5f seconds\n", timeit(af_test));
 } catch (af::exception& e) {
     fprintf(stderr, "%s\n", e.what());
 }
return 0;
}

and the results:

CUDA toolkit 4.2, driver 295.59

GPU0 GeForce GT 650M, 2048 MB, Compute 3.0 (single,double)

Memory Usage: 1937 MB free (2048 MB total)

thrust: 0.13008 seconds

arrayfire: 0.06702 seconds

Roesler