Hi,
please keep viennacl-devel in CC:
Just to clarify: Do you get incorrect values for a 1-by-1 matrix as
indicated in your sample data? In your previous email you mentioned that
results are fine for small matrices...
I'm afraid I can only guess at the source of the error with the
informations provided. Any chance that you can provide a standalone code
to reproduce the problem with reasonable effort?
Best regards,
Karli
On 07/20/2016 10:16 PM, Andrew Palumbo wrote:
> Thanks so much for your quick answer!
>
>
> I actually am sorry to say that I made a mistake when writing the last
> email, I copied the wrong signature from the VCL documentation, and then
> the mistake propagated through the rest of the e-mail.
>
>
> I am actually using viennacl::backend::memory_read().
>
>
> Eg, for the row_jumpers and column_idx I read use:
>
> @Name("backend::memory_read")
> public static native void memoryReadInt(@Const @ByRef MemHandle src_buffer,
> int bytes_to_read,
> int offset,
> IntPointer ptr,
> boolean async);
>
> and for the Values:
>
>
> @Name("backend::memory_read")
> public static native void memoryReadDouble(@Const @ByRef MemHandle src_buffer,
> int bytes_to_read,
> int offset,
> DoublePointer ptr,
> boolean async);
>
> And then call:
>
>
> memoryReadInt(row_ptr_handle, (m +1) *4,0, row_ptr,false)
> memoryReadInt(col_idx_handle, NNz *4,0,col_idx,false)
> memoryReadDouble(element_handle, NNz *8,0, values,false)
>
>
> and after convetring them to java.nio.Buffers, am getting results like:
>
>
> rowBuff.get(1): 0 colBuff(1): 402653448 valBuff(1): 6.91730177312166E-310
>
>
> Have also tried reading into BytePointers similarly with the same type
> of results. I know that the use of Javacpp obfuscates what the problem
> may be. But I believe the Memorry is properly allocated.
>
>
>
> Sorry for the mistake.
>
>
> Thanks,
>
>
> Andy
>
>
> ------------------------------------------------------------------------
> *From:* Karl Rupp <[email protected]>
> *Sent:* Wednesday, July 20, 2016 3:50:07 PM
> *To:* Andrew Palumbo; [email protected]
> *Subject:* Re: [ViennaCL-devel] Copying Values out of a compressed_matrix
> Hi Andy,
>
> instead of viennacl::backend::memory_copy(), you want to use
> viennacl::backend::memory_read(), which directly transfers the data into
> your buffer(s).
>
> If you *know* that your handles are in host memory, you can even grab
> the values directly via
> viennacl::linalg::host_based::detail::extract_raw_pointer<T>();
> defined in viennacl/linalg/host_based/common.hpp, around line 40.
>
> Please let me know if you still get errors after using that.
>
> Best regards,
> Karli
>
>
>
>
> On 07/20/2016 09:05 PM, Andrew Palumbo wrote:
>> Hello,
>>
>>
>> I'm Having some difficulties with compressed_matrix multiplication.
>>
>>
>> Essentially I am copying three buffers, the CSR conversion of an Apache
>> Mahout SparseMatrix, into two compressed_matrices performing matrix
>> multiplication. I am doing this in scala and Java using javacpp.
>>
>>
>> For example, I have a 5 x 10 matrix of ~20% non-zero values which in CSR
>> format looks like this:
>>
>>
>> NNz: 12
>>
>> Row Pointer: [0, 1, 4, 6, 9, 12, ]
>>
>> Col Pointer: [9, 0, 8, 7, 2, 9, 0, 8, 9, 0, 3, 5, ]
>>
>> element Pointer: [0.4065367203992265, 0.04957158909682802,
>> 0.5205586068847993, 0.3708618354358446, 0.6963900565931678,
>> 0.8330915529787706, 0.32839112750638844, 0.7856168903297948,
>> 0.4265801782090245, 0.14733066454561583, 0.9501663495824946,
>> 0.9710498974366047, ]
>>
>> Multiplied by a similarly Sparse 10 x 5 compressed_matrix
>>
>> I use a CompressedMatrix wrapper which essentially wraps the
>>
>> viennacl:: compressed_matrix (vcl_size_t rows, vcl_size_t cols,
>> vcl_size_t nonzeros=0, viennacl::context ctx=viennacl::context())
>>
>> constructor as well as the
>>
>> compressed_matrix (matrix_expression< const compressed_matrix,
>> const compressed_matrix, op_prod > const &proxy).
>>
>> I have a helper function, /toVclCompressedMatrix/(..) which essentially
>> does the CSR conversion from a Mahout src matrix, calls the constructor
>> and uses viennacl::compressed_matrix::set(...) to set the buffers:
>>
>> val ompA =toVclCompressedMatrix(src = mxA, ompCtx)
>> val ompB =toVclCompressedMatrix(src = mxB, ompCtx)
>>
>>
>> and then create a new viennacl::compressed_matrix from the
>> viennacl::linalg::prod of the 2 matrices i.e.:
>>
>> val ompC =new CompressedMatrix(prod(ompA, ompB))
>>
>> The context in the above case is either the Host or OpenMP (I know that
>> there is some special casting of the row_jumpers and col_idxs that needs
>> to be done in the OpenCL version)
>>
>> The Matrix multiplication completes without error on small Matrices eg.
>> < 300 x 300
>> but seems to overwrite the resulting buffers on larger Matrices.
>>
>> My real problem, though is getting the memory back out of the
>> resulting`ompC` compresed_matrix so that i can write it back to a mahout
>> SparseMatrix.
>>
>> currently I am using:
>>
>> void viennacl::backend::memory_copy (mem_handle const & src_buffer,
>> mem_handle & dst_buffer,
>> vcl_size_t src_offset,
>> vcl_size_t dst_offset,
>> vcl_size_t bytes_to_copy
>> )
>>
>> on ompC.handel1,ompC.handel2 and ompC.handel source handels
>>
>> to copy into pre-allocated row_jumper, col_index and element buffers
>> (of size ompC.size1() + 1, ompC.nnz and ompC.nnz, respectivly).
>>
>> I am getting nonsensical values back that one would expect from memory
>> errors. eg:
>>
>> the Matrix geometry of the result: ompC.size1(), and omp.size2() are
>> correct and ompC.nnz is a reasonable value.
>>
>> It is possible that I have mis-allocated some of the memory on my side,
>> but I am pretty sure that most of the Buffers are allocated correctly
>> (usually JavaCPP does a pretty good job of this).
>>
>>
>> I guess, long story short, my question is am i using the correct method
>> of copying the memory out of a compressed_matrix? is there something
>> glaringly incorrect that i am doing here? Should I be using
>> viennacl::backend::memory_copy or is there a different method that i
>> should be using?
>>
>>
>> Thanks very much,
>>
>> Andy
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
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