A domain-specific language for fast graph shift operations. This implements mathematical fields on numbers, n-dimensional column vectors, and n-by-n sparse matrices.
License: Apache Software License
Author: Emmanouil (Manios) Krasanakis
Dependencies: numpy
Creating a 5-dimensional vector (can use numpy arrays
as inputs interchangeably with lists everywhere):
from matvec import Vector
x = Vector([1, 2, 3, 4, 5])Creating a 5x5 sparse matrix A in coo-format
with non-zero elements A[1,2]=9 and A[3,0]=21
from matvec import Matrix
A = Matrix([1, 2],
[3, 0],
[9, 21],
5)Print the outcome of matrix-vector multiplication:
print(A*x)Print the outcome of left-multiplying the transpose of x with A:
print(x*A)🚀 Parallelized matrix-vector multiplication.
📉 Memory reuse optimization.
🔍 numpy compatibility.
🏭 Common arithmetic operations.
Benchmarks tested on a machine with 2.6 GHz CPU base clock and up to 4.4 GHz turbo boost, 12 logical cores, and 16GB DDR3 RAM. They span vectors of 1.E4 to 1.E6 elements and matrices with up to 20x the number of non-zeroes. More rigorous evaluation will take place in the future.
| Task | numpy/scipy | matvec |
|---|---|---|
| Create new vector or array | 0.025 sec | 0.014 sec |
| 1000 temp. additions of 1.E6 vectors only | 2.130 sec | 1.061 sec |
| Create matrix | 0.505 sec | 0.183 sec |
| Sparse matrix with vector multiplication | 0.045 sec | 0.020 sec |
- Full arithmetic operations
* + - / == < > <= >=between vectors and other vectors or scalars. - Matrix-vector multiplication
*(both left and right). - Element access and assignment for vectors with
[]. - Masking, such as
y = x[x>0]. matvec.clear()clears cache.