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 Legion runtime. Legate Sparse
interoperates with cuNumeric to
enable writing programs that operate on distributed dense and sparse arrays.
For some examples, take a look at the examples/ directory. We have implemented
a PDE Solver, as well as Geometric
and Algebraic multi-grid solvers using Legate Sparse. More complex
and interesting applications are on the way -- stay tuned!
To use Legate Sparse, you must build legate.core and cuNumeric from source. In particular, the following branch of legate.core and the following branch of cuNumeric must be used as many necessary changes have not yet made their way into the main branches of Legate and cuNumeric.
First, clone quickstart and
checkout the legate-sparse branch. This repository contains several scripts to cover
common machines and use cases. Then clone the following fork of legate.core,
and the official repository of cuNumeric. For legate.core,
check out the legate-sparse-branch-23.09, and the branch-23.09 branch of cuNumeric. Then, clone Legate Sparse.
We recommend the following directory organization:
legate/
quickstart/
legate.core/
cunumeric/
legate.sparse/
Second, set up a conda environment:
quickstart/setup_conda.sh
Running ./quickstart/setup_conda.sh --help will display different options that allow you to customize
the created conda installation and environment.
Third, install legate.core:
cd legate.core/
git clone https://gitlab.com/StanfordLegion/legion/
cd legion && git checkout control_replication && cd ../
LEGION_DIR=legion ../quickstart/build.sh
Fourth, install cunumeric:
cd cunumeric/
../quickstart/build.sh
Finally, install legate.sparse:
cd legate.sparse
../quickstart/build.sh
The quickstart/build.sh script will attempt to auto-detect the machine you are
running on, if it is a common machine that the Legate or Legion developers frequently
use. Otherwise, it will ask for additional information to be specified, such as the
GPU architecture or network interconnect.
To write programs using Legate Sparse, import the sparse module, which
contains methods and types found in scipy.sparse.
import sparse.io as io
mat = io.mmread("testdata/test.mtx").tocsr()
print((mat + mat).todense())
"""
[[4. 0. 0. 6. 0.]
[0. 0. 0. 0. 8.]
[0. 0. 0. 0. 0.]
[6. 0. 0. 0. 0.]
[0. 8. 0. 0. 0.]]
"""