The SLU_SC in SuperLU is defined as "Supernode, column-wise". It's generally the result of running SuperLU on a sparse matrix.
I would like to inspect this matrix instead of solving for a vector so the CSR / COO format would be nice. Any pointer on how to do it ?
If you are fine with using a high level language interface to SuperLU you can use Python together with scipy. You can call the spilu method and then access and convert the L and U of the resulting SuperLU object to all the standard sparse matrix formats.
Related
I am using Intel Fortran with Visual Studio 2008 SP1.
My main question is: I would like to read 2D array from Matlab .mat file into fortran. Also, save the output of Fortran 2D matricies to a preferably .mat file, as currently I can save it to a text file using:
write(unit = #, <linelength>F22.8>),matrixname
This line works, but I am not sure if loose any of my double precision. If I do not loose precision, I can stick to it, otherwise I would need help. And I will only need a way to read from a Matlab file to intel-fortran with keeping the precision. There is no characters in these arrays, they are of numerical values.
I need to conserve the precision, since I am working with spherical functions, and they can be highly divergent.
matlab's internal ".mat" is "maybe" or "maybe not" compressed depending on versions. I think you do not want to use this for portable file transfer. ( Having attempted to find good documentation on the subject I wonder if #HPM was being sarcastic in his comment.. )
A keep it simple approach for a single array is to simply exchange as raw binary.
Example write in matlab:
a=[1. 2. ; 3. 4. ]
fileID = fopen('test.bin','w');
fwrite(fileID,a,'double');
fclose(fileID);
then in fortran
implicit none
double precision a(2,2)
open(unit=100,'test.bin',access='stream',form='unformatted')
read(100)a
note here the data is actually "flat", the reading program needs to know the array dimension. You can of course write the dimensions to the file if you need.
there are of course a number of potential portability issues with binary data, but this will work for most cases assuming you are reading/writing on the same hardware.
I have a fine tuned algorithm in MATLAB that operates on matrices (ofcourse). I've used matlab coder to generate c code for this algorithm and it works as expected.
Here's a function call that I used in Matlab
x = B/A
wherein
B is of size 1*500 (rows * columns)
A is of size 10*500
x, the result is of size 1*10
When this is converted into C source using Matlab Coder. I noticed that the function definition accepts parameters that are same as above sizes.
void myfunction(const double B[500], const double A[5000], double x[10])
For prototype and testing purposes this seems okay. However, in production I prefer to have this function be used for different sizes too. For example 100 instead of 500 in above mentioned variables should also work. How can I remove dependence of matrix dimensions in my algorithm ?
Additionally, there are few lines of code that use hard coded integers. For example, there is code like
if (rankR <= 1.4903363393874656E-8)
// Some internal function calls
else
// Usage of standard sqrt
or
500.0 * fabs(A[0]) * 2.2204460492503131E-16
Could any one explain what are these hard coded integers ? Are these generated from the test data that I've used in MATLAB ?
If the function call you refer to is the entry-point function, you can define the size when setting up Coder. The simplest way to run the Coder is using the GUI from the 'Apps' menu inside MATLAB (or type 'coder' at the console). After specifying the entry-point function, step 2 is to define the type and size for each of the input variables.
For each dimension of your input variable (there can be more than 2 if necessary), you can specify the:
n - dimension is exactly n long
:n - dimension is up to n long
inf - dimension is unbounded
If the function call is not the entry-point function, and is buried inside your code (or if you are running the codegen function from the console), you can explicitly define variables as being of varying size:
coder.varsize('myVariableName');
Bear in mind that some functions can only be used (with Coder) with fixed-sized inputs.
Fuller description here:
http://uk.mathworks.com/help/fixedpoint/ug/defining-variable-size-data-for-code-generation.html#br9t627
Not sure about the random constants unfortunately.
i have a matlab function that reads a big matrix and calculates the Singular Value Decomposition SVD. I however need to run that on a linux system without needing to install matlab on every new system, so id like to have it converted into c source code. The code is realy simple:
function singular(m)
load c:\som\matlab.txt
[U,S,V]=svd(matlab);
m = str2num(m);
U1=U(:,1:floor(sqrt(m)));
V1=V';
Vt=V1(1:floor(sqrt(m)),:);
S1=S(1:floor(sqrt(m)),1:floor(sqrt(m)));
matlab1=U1*S1*Vt;
matlab2=abs(matlab1);
save c:\som\matlab1.txt matlab1 -ascii
save c:\som\matlab2.txt matlab2 -ascii
You can use the Matlab coder, but I advise you to make it manually, because some functions are not convertible, and the performance is not much better that mading it manually.
To make svd manually: SVD
I would like to implement some R package, written in C code.
The C code must:
take an array (of any type) as input.
produce array as output (of unpredictable size).
What is the best practice of implementing array passing?
At the moment C code is called with .C(). It accesses array directly from R, through pointer. Unfortunately same can't be done for output, as output dimensions need to be known in advance which is not true in my case.
Would it make sense to pass array from C to R through a file? For example, in ramfs, if using linux?
Update 1:
Here the exact same problem was discussed.
There, possible option of returning array with unknown dimensions was mentioned:
Splitting external function into two, at the point before calculating the array but after dimensions are known. First part would return dimensions, then empty array would be prepared, then second part would run and populate the array in R.
In my case full dimensions are known only once whole code is executed, so this method would mean running C code twice. Taking a guess on maximal array size isn't optional either.
Update 2: It seems only way to do this is to use .Call() instead, as power suggested. Here are few good examples: http://www.sfu.ca/~sblay/R-C-interface.ppt.
Thanks.
What is the best practice of implementing array passing?
Is the package already written in ANSI C? .C() would then be quick and easy.
If you are writing from scratch, I suggest .Call() and Rcpp. In this way, you can pass R objects to your C/C++ code.
Would it make sense to pass array through a file?
No
Read "Writing R Extensions".
I have a small bit of existing C code that I want to wrap using Cython. I want to be able to set up a number of numpy arrays, and then pass those arrays as arguments to the C code whose functions take standard c arrays (1d and 2d). I'm a little stuck in terms of figuring out how to write the proper .pyx code to properly handle things.
There are a handful of functions, but a typical function in the file funcs.h looks something like:
double InnerProduct(double *A, double **coords1, double **coords2, const int len)
I then have a .pyx file that has a corresponding line:
cdef extern from "funcs.h":
double InnerProduct(double *A, double **coords1, double **coords2, int len)
where I got rid of the const because cython doesn't support it. Where I'm stuck is what the wrapper code should then look like to pass a MxN numpy array to the **coords1 and **coords2 arguments.
I've struggled to find the correct documentation or tutorials for this type of problem. Any suggestions would be most appreciated.
You probably want Cython's "typed memoryviews" feature, which you can read about in full gory detail here. This is basically the newer, more unified way to work with numpy or other arrays. These can be exposed in Python-land as numpy arrays, or you can export them to Python (for example, here). You have to pay attention to how the striding works and make sure you're consistent about e.g. C-contiguous vs. FORTRAN-like arrays, but the docs are pretty clear on how to do that.
Without knowing a bit more about your function it's hard to be more concrete on exactly the best way to do this - i.e., is the C function read-only for the arrays? (I think yes based on the signature you gave, but am not 100% sure.) If so you don't need to worry about making copies if needed to get C-contiguous states, because the C function doesn't need to talk back to the Python-level numpy array. But typed memoryviews will let you do any of this with a minimum of fuss.
The cython interface code should be created according to the tutorial given here.
To get a C pointer to the data in a numpy array, you should use the ctypes attribute of the numpy array, which is described here.