Computing the null space of a matrix as fast as possible - algorithm

Question

I need to compute the nullspace of several thousand small matrices (8x9, not 4x3 as I wrote previously) in parallel (CUDA). All references point to SVD but the algorithm in numerical recipes seems very expensive, and gives me lots of things other than the null space that I don't really need. Is Gaussian elimination really not an option? Are there any other commonly used methods?

Answer 1

To answer your question directly... yes! QR decomposition!
Let A be an m-by-n matrix with rank n. QR decomposition finds orthonormal m-by-m matrix Q and upper triangular m-by-n matrix R such that A = QR. If we define Q = [Q1 Q2], where Q1 is m-by-n and Q2 is m-by-(m-n), then the columns of Q2 form the null space of A^T.
QR decomposition is computed either by Gram-Schmidt, Givens rotations, or Householder reflections. They have different stability properties and operation counts.
You are right: SVD is expensive! I can't speak for what state-of-the-art stuff uses, but when I hear "compute null space" (EDIT: in a way that is simple for me to understand), I think QR.

Answer 2

I don't think the above proposed method always gives the whole null space. To recap: "A = QR, where Q = [Q1 Q2], and Q1 is m-by-n and Q2 is m-by-(m-n). Then the columns of Q2 form the null space of A^T."
Indeed, this may only give a subspace of the null space. Simple counter-example is when A=0, in which case the null space of A^T is the whole R^m.
Therefore, it is necessary to check R too. Based on my experience with Matlab, if a row of R is straight 0, then the corresponding column in Q should also be a basis of the null space of A^T. Clearly this observation is heuristic and hinges on the particular algorithm used for QR decomposition.

Answer 3

Gaussian elimination is plenty fast for 4x3 matrices. IIRC I've done about 5 million per second with Java without parallelism. With such a small problem, your best bet is to code the routine (row reduce etc.) yourself; otherwise you'll waste most of the time putting the data into the right format for the external routine.

Answer 4

In the anwers above, it has been already pointed out how the null space of a matrix can be calculated by using the QR or the SVD approach. SVD should be preferred when accuracy is required, see also Null-space of a rectangular dense matrix.
As of February 2015, CUDA 7 (now in release candidate) makes SVD available through its new cuSOLVER library. Below I report an example on how using cuSOLVER's SVD to calculate the null space of a matrix.
Be aware that the problem you are focusing on concerns the calculation of several small matrices, so you should adapt the example I'm providing below by using streams to make sense for your case. To associate a stream to each task you can use
cudaStreamCreate()
and
cusolverDnSetStream()
kernel.cu
#include "cuda_runtime.h"
#include "device_launch_paraMeters.h"
#include<iostream>
#include<iomanip>
#include<stdlib.h>
#include<stdio.h>
#include<assert.h>
#include<math.h>
#include <cusolverDn.h>
#include <cuda_runtime_api.h>
#include "Utilities.cuh"
/********/
/* MAIN */
/********/
int main(){
// --- gesvd only supports Nrows >= Ncols
// --- column major memory ordering
const int Nrows = 7;
const int Ncols = 5;
// --- cuSOLVE input/output parameters/arrays
int work_size = 0;
int *devInfo; gpuErrchk(cudaMalloc(&devInfo, sizeof(int)));
// --- CUDA solver initialization
cusolverDnHandle_t solver_handle;
cusolverDnCreate(&solver_handle);
// --- Singular values threshold
double threshold = 1e-12;
// --- Setting the host, Nrows x Ncols matrix
double *h_A = (double *)malloc(Nrows * Ncols * sizeof(double));
for(int j = 0; j < Nrows; j++)
for(int i = 0; i < Ncols; i++)
h_A[j + i*Nrows] = (i + j*j) * sqrt((double)(i + j));
// --- Setting the device matrix and moving the host matrix to the device
double *d_A; gpuErrchk(cudaMalloc(&d_A, Nrows * Ncols * sizeof(double)));
gpuErrchk(cudaMemcpy(d_A, h_A, Nrows * Ncols * sizeof(double), cudaMemcpyHostToDevice));
// --- host side SVD results space
double *h_U = (double *)malloc(Nrows * Nrows * sizeof(double));
double *h_V = (double *)malloc(Ncols * Ncols * sizeof(double));
double *h_S = (double *)malloc(min(Nrows, Ncols) * sizeof(double));
// --- device side SVD workspace and matrices
double *d_U; gpuErrchk(cudaMalloc(&d_U, Nrows * Nrows * sizeof(double)));
double *d_V; gpuErrchk(cudaMalloc(&d_V, Ncols * Ncols * sizeof(double)));
double *d_S; gpuErrchk(cudaMalloc(&d_S, min(Nrows, Ncols) * sizeof(double)));
// --- CUDA SVD initialization
cusolveSafeCall(cusolverDnDgesvd_bufferSize(solver_handle, Nrows, Ncols, &work_size));
double *work; gpuErrchk(cudaMalloc(&work, work_size * sizeof(double)));
// --- CUDA SVD execution
cusolveSafeCall(cusolverDnDgesvd(solver_handle, 'A', 'A', Nrows, Ncols, d_A, Nrows, d_S, d_U, Nrows, d_V, Ncols, work, work_size, NULL, devInfo));
int devInfo_h = 0; gpuErrchk(cudaMemcpy(&devInfo_h, devInfo, sizeof(int), cudaMemcpyDeviceToHost));
if (devInfo_h != 0) std::cout << "Unsuccessful SVD execution\n\n";
// --- Moving the results from device to host
gpuErrchk(cudaMemcpy(h_S, d_S, min(Nrows, Ncols) * sizeof(double), cudaMemcpyDeviceToHost));
gpuErrchk(cudaMemcpy(h_U, d_U, Nrows * Nrows * sizeof(double), cudaMemcpyDeviceToHost));
gpuErrchk(cudaMemcpy(h_V, d_V, Ncols * Ncols * sizeof(double), cudaMemcpyDeviceToHost));
for(int i = 0; i < min(Nrows, Ncols); i++)
std::cout << "d_S["<<i<<"] = " << std::setprecision(15) << h_S[i] << std::endl;
printf("\n\n");
int count = 0;
bool flag = 0;
while (!flag) {
if (h_S[count] < threshold) flag = 1;
if (count == min(Nrows, Ncols)) flag = 1;
count++;
}
count--;
printf("The null space of A has dimension %i\n\n", min(Ncols, Nrows) - count);
for(int j = count; j < Ncols; j++) {
printf("Basis vector nr. %i\n", j - count);
for(int i = 0; i < Ncols; i++)
std::cout << "d_V["<<i<<"] = " << std::setprecision(15) << h_U[j*Ncols + i] << std::endl;
printf("\n");
}
cusolverDnDestroy(solver_handle);
return 0;
}
Utilities.cuh
#ifndef UTILITIES_CUH
#define UTILITIES_CUH
extern "C" int iDivUp(int, int);
extern "C" void gpuErrchk(cudaError_t);
extern "C" void cusolveSafeCall(cusolverStatus_t);
#endif
Utilities.cu
#include <stdio.h>
#include <assert.h>
#include "cuda_runtime.h"
#include <cuda.h>
#include <cusolverDn.h>
/*******************/
/* iDivUp FUNCTION */
/*******************/
extern "C" int iDivUp(int a, int b){ return ((a % b) != 0) ? (a / b + 1) : (a / b); }
/********************/
/* CUDA ERROR CHECK */
/********************/
// --- Credit to http://stackoverflow.com/questions/14038589/what-is-the-canonical-way-to-check-for-errors-using-the-cuda-runtime-api
void gpuAssert(cudaError_t code, char *file, int line, bool abort=true)
{
if (code != cudaSuccess)
{
fprintf(stderr,"GPUassert: %s %s %d\n", cudaGetErrorString(code), file, line);
if (abort) { exit(code); }
}
}
extern "C" void gpuErrchk(cudaError_t ans) { gpuAssert((ans), __FILE__, __LINE__); }
/**************************/
/* CUSOLVE ERROR CHECKING */
/**************************/
static const char *_cudaGetErrorEnum(cusolverStatus_t error)
{
switch (error)
{
case CUSOLVER_STATUS_SUCCESS:
return "CUSOLVER_SUCCESS";
case CUSOLVER_STATUS_NOT_INITIALIZED:
return "CUSOLVER_STATUS_NOT_INITIALIZED";
case CUSOLVER_STATUS_ALLOC_FAILED:
return "CUSOLVER_STATUS_ALLOC_FAILED";
case CUSOLVER_STATUS_INVALID_VALUE:
return "CUSOLVER_STATUS_INVALID_VALUE";
case CUSOLVER_STATUS_ARCH_MISMATCH:
return "CUSOLVER_STATUS_ARCH_MISMATCH";
case CUSOLVER_STATUS_EXECUTION_FAILED:
return "CUSOLVER_STATUS_EXECUTION_FAILED";
case CUSOLVER_STATUS_INTERNAL_ERROR:
return "CUSOLVER_STATUS_INTERNAL_ERROR";
case CUSOLVER_STATUS_MATRIX_TYPE_NOT_SUPPORTED:
return "CUSOLVER_STATUS_MATRIX_TYPE_NOT_SUPPORTED";
}
return "<unknown>";
}
inline void __cusolveSafeCall(cusolverStatus_t err, const char *file, const int line)
{
if(CUSOLVER_STATUS_SUCCESS != err) {
fprintf(stderr, "CUSOLVE error in file '%s', line %d\n %s\nerror %d: %s\nterminating!\n",__FILE__, __LINE__,err, \
_cudaGetErrorEnum(err)); \
cudaDeviceReset(); assert(0); \
}
}
extern "C" void cusolveSafeCall(cusolverStatus_t err) { __cusolveSafeCall(err, __FILE__, __LINE__); }

Answer 5

I think the most important thing for CUDA is to find an algorithm that doesn't depend on conditional branching (which is quite slow on graphics hardware). Simple if statements that can be optimized into conditional assignment are much better (or you can use the ?: operator).
If necessary, you should be able to do some form of pivoting using conditional assignment. It might actually be harder to determine how to store your result: if your matrix is rank-deficient, what do you want your CUDA program to do about it?
If you assume your 4x3 matrix is not actually rank-deficient, you can find your (single) null-space vector without any conditionals at all: the matrix is small enough that you can use Cramer's rule efficiently.
Actually, since you don't actually care about the scale of your null vector, you don't have to divide by the determinant -- you can just take the determinants of the minors:
x1 x2 x3
M = y1 y2 y3
z1 z2 z3
w1 w2 w3
|y1 y2 y3| |x1 x2 x3| |x1 x2 x3| |x1 x2 x3|
-> x0 = |z1 z2 z3| y0 = -|z1 z2 z3| z0 = |y1 y2 y3| w0 = -|y1 y2 y3|
|w1 w2 w3| |w1 w2 w3| |w1 w2 w3| |z1 z2 z3|
Note that these 3x3 determinants are just triple products; you can save computation by reusing the cross products.

Answer 6

"seems very expensive" - what data do you have that supports this?
Maybe Block Lanczos is the answer you seek.
Or maybe this.
Both JAMA and Apache Commons Math have SVD implementations in Java. Why not take those and try them out? Get some real data for your case instead of impressions. It won't cost you much, since the code is already written and tested.

Answer 7

I wondered if the matrixes are related rather than just being random, so that the null spaces you are seeking can be considered to be like 1-dimensional tangents to a curve in N-space (N = 9). If so, you may be able to speed things up by using Newton's method to solve successive instances of the system of quadratic equations Ax = 0, |x|^2 = 1, starting from a previous null space vector. Newton's method uses first derivatives to converge to a solution, and so would use Gaussian elimination to solve 9x9 systems. Using this technique would require that you be able to make small steps from matrix to matrix by say varying a parameter.
So the idea is that you initialize using SVD on the first matrix, but thereafter you step from matrix to matrix, using the null space vector of one as the starting point for the iteration for the next one. You need one or two iterations to get convergence. If you don't get convegence you use SVD to restart. If this situation is what you have, it is much faster than starting fresh on each matrix.
I used this a long time ago to map contours in the solutions of sets of 50 x 50 quadratic equations associated with the behavior of electric power systems.