matrix-multiplication | Some scripts in Python , Java and C for matrix | Math library
kandi X-RAY | matrix-multiplication Summary
kandi X-RAY | matrix-multiplication Summary
Some scripts in Python, Java and C++ for matrix multiplication. Read this blogpost for some explanations:
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Top functions reviewed by kandi - BETA
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matrix-multiplication Key Features
matrix-multiplication Examples and Code Snippets
Lines of Code : 225License : Non-SPDX (Apache License 2.0)
Copydef matmul(a,
b,
transpose_a=False,
transpose_b=False,
adjoint_a=False,
adjoint_b=False,
a_is_sparse=False,
b_is_sparse=False,
output_type=None,
name=N def matmul(a: ragged_tensor.RaggedOrDense,
b: ragged_tensor.RaggedOrDense,
transpose_a=False,
transpose_b=False,
adjoint_a=False,
adjoint_b=False,
a_is_sparse=False,
b_is_sp def _SparseMatrixMatMulGrad(op, grad):
"""Gradient for sparse_matrix_mat_mul op."""
# input to sparse_matrix_mat_mul is (A, B) with CSR A and dense B.
# Output is dense:
# C = opA(A) . opB(B) if transpose_output = false
# C = (opA(A) . Community Discussions
Trending Discussions on matrix-multiplication
QUESTION
Im trying to use an AMM-Algorithm (approximate-matrix-multiplication; on Apple's M1), which is fully based on speed and uses the x86 built-in functions listed below. Since using a VM for x86 slows down several crucial processes in the algorithm, I was wondering if there is another way to run it on ARM64.
I also could not find a fitting documentation for the ARM64 built-in functions, which could eventually help mapping some of the x86-64 instructions.
Used built-in functions:
...ANSWER
Answered 2022-Mar-18 at 18:59Normally you'd use intrinsics instead of the raw GCC builtin functions, but see https://gcc.gnu.org/onlinedocs/gcc/ARM-C-Language-Extensions-_0028ACLE_0029.html. The __builtin_arm_... and __builtin_aarch64_... functions like __builtin_aarch64_saddl2v16qi don't seem to be documented in the GCC manual the way the x86 ones are, just another sign they're not intended for direct use.
See also https://developer.arm.com/documentation/102467/0100/Why-Neon-Intrinsics- re intrinsics and #include . GCC provides a version of that header, with the documented intrinsics API implemented using __builtin_aarch64_... GCC builtins.
As far as portability libraries, AFAIK not from the raw builtins, but SIMDe (https://github.com/simd-everywhere/simde) has portable implementations of immintrin.h Intel intrinsics like _mm_packs_epi16. Most code should be using that API instead of GNU C builtins, unless you're using GNU C native vectors (__attribute__((vector_size(16))) for portable SIMD without any ISA-specific stuff. But that's not viable when you want to take advantage of special shuffles and stuff.
And yes, ARM does have narrowing with saturation with instructions like vqmovn (https://developer.arm.com/documentation/dui0473/m/neon-instructions/vqmovn-and-vqmovun), so SIMDe can efficiently emulate pack instructions. That's AArch32, not 64, but hopefully there's an equivalent AArch64 instruction.
QUESTION
im not sure where is the best place to ask this but I am currently working on using ARM intrinsics and am following this guide: https://developer.arm.com/documentation/102467/0100/Matrix-multiplication-example
However, the code there was written assuming that the arrays are stored column-major order. I have always thought C arrays were stored row-major. Why did they assume this?
EDIT: For example, if instead of this:
...ANSWER
Answered 2021-May-30 at 17:23C is not inherently row-major or column-major.
When writing a[i][j], it's up to you to decide whether i is a row index or a column index.
While it's somewhat of a common convention to write the row index first (making the arrays row-major), nothing stops you from doing the opposite.
Also, remember that A × B = C is equivalent to Bt × At = Ct (t meaning a transposed matrix), and reading a row-major matrix as if it was column-major (or vice versa) transposes it, meaning that if you want to keep your matrices row-major, you can just reverse the order of the operands.
QUESTION
I was trying to understand how matrix multiplication works over 2 dimensions in DL frameworks and I stumbled upon an article here. He used Keras to explain the same and it works for him. But when I try to reproduce the same code in Pytorch, it fails with the error as in the output of the following code
Pytorch Code:
...ANSWER
Answered 2021-Jan-10 at 07:10Matrix multiplication (aka matrix dot product) is a well defined algebraic operation taking two 2D matrices.
Deep-learning frameworks (e.g., tensorflow, keras, pytorch) are tuned to operate of batches of matrices, hence they usually implement batched matrix multiplication, that is, applying matrix dot product to a batch of 2D matrices.
The examples you linked to show how matmul processes a batch of matrices:
QUESTION
I'm trying to create a matrix-multiplication-with-scalar function, without any libraries. It has to include list comprehension:
...ANSWER
Answered 2020-Nov-18 at 13:25This is a possible solution:
QUESTION
I have a scipy.sparse.csr matrix X which is n x p. For each row in X I would like to compute the intersection of the non zero element indices with each row in X and store them in a new tensor or maybe even a dictionary. For example, X is:
...ANSWER
Answered 2020-Jun-02 at 16:27One first easy solution is to notice that the output matrix is symmetrical:
QUESTION
I need to compute a second power of a square matrix A (A*A^T), but I am only interested in the values around the diagonal of the result. In other words, I need to compute dot products of neighboring rows, where the neighborhood is defined by some window of fixed size and ideally, I want to avoid computation of the remaining dot products. How to do this in numpy without running the full matrix multiplication with some masking? The resulting array should look as follows:
...ANSWER
Answered 2020-Jun-24 at 15:09Have a look into sparse matrices with scipy (where numpy is also from).
For your specific problem:
The diagonal elements are the column-wise sum of the elementwise product of your matrix and its transpose
v = np.sum(np.multiply(A, A.T), axis=0)the off diagonal elements are the same, just with the last row/column deleted and substituted by a zero column/row at the first index:
QUESTION
The aim is to implement a fast version of the orthogonal projective non-negative matrix factorization (opnmf) in R. I am translating the matlab code available here.
I implemented a vanilla R version but it is much slower (about 5.5x slower) than the matlab implementation on my data (~ 225000 x 150) for 20 factor solution.
So I thought using c++ might speed up things but its speed is similar to R. I think this can be optimized but not sure how as I am a newbie to c++. Here is a thread that discusses a similar problem.
Here is my RcppArmadillo implementation.
...ANSWER
Answered 2020-Jun-20 at 15:09Are you aware that this code is "ultimately" executed by a pair of libraries called LAPACK and BLAS?
Are you aware that Matlab ships with a highly optimised one? Are you aware that on all systems that R runs on you can change which LAPACK/BLAS is being used.
The difference matters greatly. Just this morning a friend posted this tweet contrasting the same R code running on the same Windows computer but in two different R environments. The six-times faster one "simply" uses a parallel LAPACK/BLAS implementation.
Here, you haven't even told us which operating system you are on. You can get OpenBLAS (which uses parallelism) for all OSs that R runs on. You can even get the Intel MKL (which IIRC is what Matlab uses too) fairly easily on some OSs. For Ubuntu/Debian I published a script on GitHub that does it in one step.
Lastly, many years ago I "inherited" a fast program running in Matlab on a (then-large-ish) Windows computer. I rewrote the Matlab part (carefully and slowly, it's effort) in C++ using RcppArmadillo leading a few factors of improvement -- and because we could run that (now open source) code in parallel from R on the same computer another few factors. Together it was orders of magnitude turning a day-long simulation into something that ran a few minutes. So "yes, you can".
Edit: As you have access to Ubuntu, you can switch from basic LAPACK/BLAS to OpenBLAS via a single command, though I am no longer that familiar with Ubuntu 16.04 (as I run 20.04 myself).
Edit 2: Picking up the comparison from Josef's tweet, the Docker r-base container I also maintainer (as part of the Rocker Project) can use OpenBLAS. [1] So once we add it, e.g. via apt-get install libopenblas-dev the timing of a simple repeated matrix crossproduct moves from
QUESTION
I had to write some MATLAB code, and at some point I had to apply a function I wrote elementwise to a vector. I considered two different way to do that:
- Loop over the vector
- Use MATLAB elementwise operations
In my case, I have a function group defined like:
ANSWER
Answered 2020-May-14 at 06:20MATLAB loops are quite fast. In fact, even vector-wise calculations are rarely faster than a loop. The problem is (as @Cris Luengo mentioned in the comments) the calling of (self-written) functions. I constructed a little example here (and fixed some issues in your code):
QUESTION
I am trying to learn OpenCL by writing a simple program to add the absolute value of a subtraction of a point's dimensions. When I finished writing the code, the output seemed wrong and so I decided to integrate some printf's in the code and kernel to verify that all the variables are passed correctly to the kernel. By doing this, I learned that the input variables were NOT correctly sent to the kernel, because printing them would return incorrect data (all zeros, to be precise). I have tried changing the data type from uint8 to int, but that did not seem to have any effect. How can I correctly send uint8 variables to the memory buffer in OpenCL? I really cannot seem to identify what I am doing wrong in writing and sending the memory buffers so that they show up incorrectly and would appreciate any opinion, advice or help.
Thank you in advance.
EDIT: Question is now solved. I have updated the code below according to the kind feedback provided in the comment and answer sections. Many thanks!
Code below:
...ANSWER
Answered 2020-May-02 at 11:36There is an error in function where the context is being created - one of the parameters is being passed at wrong position.
Instead:
QUESTION
Looking at the resource monitor during the execution of my script I noticed that all the cores of my PC were working, even if I did not implement any form of multiprocessing. Trying to pinpoint the cause, I discovered that the code is parallelized when using numpy's matmult (or, as in the example below, the binary operator @).
ANSWER
Answered 2020-Jan-28 at 13:08You can try using threadpoolctl. See the README for details. Before using, I recommend to have a look at the "known limitations" section, though.
Citation from that README
Python helpers to limit the number of threads used in the threadpool-backed of common native libraries used for scientific computing and data science (e.g. BLAS and OpenMP).
Fine control of the underlying thread-pool size can be useful in workloads that involve nested parallelism so as to mitigate oversubscription issues.
Code snippet from that README
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Install matrix-multiplication
You can use matrix-multiplication like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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