Legate Sparse is a Legate library
that aims to provide a distributed and accelerated drop-in replacement for the
scipy.sparse library
on top of the Legate runtime.
Legate Sparse interoperates with
cuPyNumeric,
a distributed and accelerated drop-in replacement
for NumPy, to
enable writing programs that operate on distributed dense and sparse arrays.
Take a look at the examples directory for some applications that can
use Legate Sparse. We have implemented
an explicit partial-differential equation (PDE) solver
and Geometric multi-grid solver.
More complex and interesting applications are on the way -- stay tuned!
Legate Sparse is currently in alpha and supports a subset of APIs and options from scipy.sparse, so if you need an API, please open an issue and give us a summary of its usage.
To use Legate Sparse, legate and cupynumeric libraries have to be installed.
They can be installed either by pulling the respective conda packages
or by manually building from source. For more information,
see build instructions for Legate
and cuPyNumeric.
Follow the steps in this section.
The legate-sparse conda package already depends on legate and cupynumeric,
and it will install these dependencies automatically.
To create a new environment and install:
conda create -n myenv -c legate -c conda-forge legate-sparse
or to install in an existing environment:
conda install -c legate -c conda-forge legate-sparse
To write programs using Legate Sparse, import the legate_sparse module, which
contains methods and types found in scipy.sparse. Note that the module is imported as legate_sparse
and not legate.sparse. Here is an example program saved as main.py.
For more details on how to run legate programs, check our documentation. To run the application on a single GPU, use this command:
legate --gpus 1 ./main.py
import legate_sparse as sparse
import cupynumeric as np
# number of diagonals in the matrix (including main diagonal)
n_diagonals = 3
# number of rows in the matrix
nrows = 5
# generate two tridiaonal matrices (n_diagonals=3) and multiply them
A = sparse.diags(
[1] * n_diagonals,
[x - (n_diagonals // 2) for x in range(n_diagonals)],
shape = (nrows, nrows),
format="csr",
dtype=np.float64,
)
B = sparse.diags(
[3] * n_diagonals,
[x - (n_diagonals // 2) for x in range(n_diagonals)],
shape = (nrows, nrows),
format="csr",
dtype=np.float64,
)
# spGEMM operation: multiplication of two sparse matrices
C = A @ B
print(C.todense())
print()
# spMV operation: multiplication of a sparse matrix and a dense vector
x = np.ones(nrows)
C = A @ x
print(C)
assert np.array_equal(A.todense().sum(axis=1), C)
"""
[[6. 6. 3. 0. 0.]
[6. 9. 6. 3. 0.]
[3. 6. 9. 6. 3.]
[0. 3. 6. 9. 6.]
[0. 0. 3. 6. 6.]]
[2. 3. 3. 3. 2.]
"""
For more examples, check the examples directory.