Re: [Discuss-gnuradio] SVD from LAPACK takes too long time

[Marcus Müller]

Hello Hoang,

exciting :)
Actually, if you're already using LAPACK, I doubt there's much potential for further optimization on the same platform -- if you, however, know your stuff is going to only be run on Intel Xeons or so, than maybe have a look at the Intel math kernel library lapack examples. Maybe you'd want to accelerate by using GPUs, then you'd have a look at OpenCl or CUDA implementation, or theano (however, I don't know how readily available things like SVD are for theano).

One more thing: Since you're doing SVD using LAPACK yourself, I trust you've already chosen the right routine (general, fully equipped complex-valued matrix). I'm not completely convinced, though:

Have you had a look at [1]? It seems SVD $A=V \Sigma U^H$  is a two step process: First, the input matrix is decomposed into left and right unitary matrixes $U_1$ and $V_1^H$ and an bidiagonal matrix $B$ using CGEBRD, and after that, $B$ is SVD'ed, yielding $B=U_2 \Sigma V_2^H$; the product $V_1 V_2$ then is $V$. Maybe for your application $V1$ is sufficient, because you can rearrange your problem mathematically?

Greetings,
Marcus

[1] http://www.netlib.org/lapack/lug/node53.html

On 03/03/2015 08:13 AM, Hoang Ngo Khac wrote:

Dear all,

I got trouble with Singular Value Decomposition computation in GNU Radio using LAPACK. I use SVD to build a precoder for a secondary transmitter in a cognitive radio model. It takes too long times to handle a 128x144 matrix with complex entries, and therefore, the data stream to be transmitted seem to be really slow in rate. 

Is there any other way to compute SVD faster? FYI, I just need the V* matrix within the result (U, S, V*).

Thanks,

Hoang

--

Hoang Ngo-Khac

Research Assistant - Signal and System Lab, FET, UET

Vietnam National University-Hanoi (VNU-H)

G2-206, 144 Xuan Thuy, Cau Giay, Hanoi, Vietnam

Alternative email:  address@hidden

Mobilephone:  +84.163.682.7874

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