of the new functional forms of the library routines to write
kernel-based algorithms. Given a set of m n-dimensional
observation vectors G_i
G_i = ( g_1, g_2 ... g_n )_i, i=1,2,...m,
in the form of a m x n data matrix:
G = ( G_1, G_2, ... G_m)^T
where ^T denotes transposition, I want to calculate the symmetric
m x m Gaussian kernel matrix
K_ij = exp(-gma ||G_i-G_j||^2 ), i,j = 1,2,...m.
In ordinary IDL:
function kernel_matrix, G, gma=gma
m = n_elements(G[0,*])
K = transpose(total(G^2,1))##(intarr(m)+1)
K = K + transpose(K) - 2*G##transpose(G)
return, exp(-gma*K)
end
WIth GPULib:
function gpukernel_matrix, G_gpu, gma=gma
m = G_gpu.dimensions[1]
n = G_gpu.dimensions[0]
onesm_gpu = gpuputarr(fltarr(m)+1)
onesn_gpu = gpuputarr(fltarr(1,n)+1)
G2_gpu = gpumult(G_gpu,G_gpu)
tmp1_gpu = gpumatrix_multiply(onesn_gpu,G2_gpu)
gpufree,G2_gpu
K_gpu = gpumatrix_multiply(onesm_gpu,tmp1_gpu)
tmp2_gpu = gpumatrix_multiply(tmp1_gpu, $
onesm_gpu,/atranspose,/btranspose)
gpufree,[onesm_gpu,onesn_gpu,tmp1_gpu]
K_gpu = gpuadd(K_gpu,tmp2_gpu,lhs=K_gpu)
gpufree,tmp2_gpu
tmp3_gpu = gpumatrix_multiply(G_gpu,G_gpu, $
/atranspose)
K_gpu = gpuadd(1.0,K_gpu,-2.0,tmp3_gpu,0.0,$
lhs=K_gpu)
gpufree,tmp3_gpu
K_gpu = gpuexp(1.0,-gma,K_gpu,0.0,0.0,$
lhs=K_gpu)
return, K_gpu
end
Since this routine is called many times in the
processing of a multispectral image, a memory
leak would be disasterous. Note the careful use
of the LHS keyword and of the GPUFREE procedure
to avoid this.
The code is now more readable than for the
procedural forms, but would be more readable
still if there were a GPUTRANSPOSE() function and
if GPUTOTAL() had a dimension parameter.
