Merge pull request #121 from nv-legate/branch-21.10

Release 21.11.00

.clang-format

@@ -54,7 +54,7 @@ ConstructorInitializerAllOnOneLineOrOnePerLine: true
 ConstructorInitializerIndentWidth: 2
 ContinuationIndentWidth: 2
 Cpp11BracedListStyle: true
-DerivePointerAlignment: true
+DerivePointerAlignment: false
 DisableFormat: false
 ExperimentalAutoDetectBinPacking: false
 FixNamespaceComments: true

.github/workflows/ci.yml

@@ -1,4 +1,4 @@
-name: Build legate.numpy
+name: Build cunumeric
 on: 
   push:
     branches-ignore:
@@ -9,26 +9,36 @@ on:
 env:
   COMMIT: ${{ github.event.pull_request.head.sha || github.sha }}
   PROJECT: github-numpy-ci
+  REF: ${{ github.event.pull_request.head.ref || github.ref }}
+  BASE_REF: ${{ github.event.pull_request.base.ref || github.ref }}
+  EVENT_NAME: ${{ github.event_name }}
+  LABEL: ${{ github.event.pull_request.head.label }}
   # Prevent output buffering
   PYTHONUNBUFFERED: 1
 jobs:
   build:
+    if: ${{ github.repository == 'nv-legate/cunumeric' }}
     runs-on: self-hosted
     steps:
       - name: Run CI build
         run: |
           /data/github-runner/legate-bin/setup.sh
-          cd legate-ci/github-ci/legate.numpy
+          cd legate-ci/github-ci/cunumeric
           rm -rf ngc-artifacts || true
           ./build.sh > ${COMMIT}-build.log 2>&1
+      - name: Process Output
+        run: |
+          cd legate-ci/github-ci/cunumeric
           cat *artifacts/*/*
+        if: always()
       - name: Upload Build Log
         if: always()
         uses: actions/upload-artifact@v2
         with:
           name: build-log
           path: ./**/${{ env.COMMIT }}-build.log.gpg
   test:
+    if: ${{ github.repository == 'nv-legate/cunumeric' }}
     runs-on: self-hosted
     needs: build
     strategy: 
@@ -46,7 +56,7 @@ jobs:
       - name: Prepare
         run: |
           /data/github-runner/legate-bin/setup.sh
-          cd legate-ci/github-ci/legate.numpy
+          cd legate-ci/github-ci/cunumeric
           if [[ ! -d ngc-artifacts ]]
           then
             mkdir ngc-artifacts
@@ -55,12 +65,12 @@ jobs:
           fi
       - name: Test
         run: |
-          cd legate-ci/github-ci/legate.numpy
+          cd legate-ci/github-ci/cunumeric
           ./test.sh ${{ matrix.options }} -j 1 > ${COMMIT}-test-${{ matrix.log }}.log 2>&1
       - name: Process output
         if: always()
         run: |
-          cd legate-ci/github-ci/legate.numpy
+          cd legate-ci/github-ci/cunumeric
           /data/github-runner/legate-bin/encrypt.sh ${COMMIT}-test-${{ matrix.log }}.log
           cat *artifacts/*/*
       - name: Upload Log

.gitignore

@@ -4,6 +4,7 @@
 .#*
 .DS_Store
 *.a
+*.d
 *.o
 *.so
 *.gz
@@ -27,6 +28,6 @@ legion
 gasnet*
 legion_defines.h
 realm_defines.h
-legate/numpy/install_info.py
+cunumeric/install_info.py
 /build/*
-/docs/legate/numpy/build
+/docs/cunumeric/build

README.md

@@ -15,22 +15,22 @@ limitations under the License.
 
 -->
 
-# Legate NumPy 
+# cuNumeric
 
-Legate NumPy is a [Legate](https://github.com/nv-legate/legate.core) library
-that aims to provide a distributed and accelerated drop-in replacement for the 
-[NumPy API](https://numpy.org/doc/stable/reference/) on top of the 
-[Legion](https://legion.stanford.edu) runtime. Using Legate NumPy you do things like run 
+cuNumeric is a [Legate](https://github.com/nv-legate/legate.core) library
+that aims to provide a distributed and accelerated drop-in replacement for the
+[NumPy API](https://numpy.org/doc/stable/reference/) on top of the
+[Legion](https://legion.stanford.edu) runtime. Using cuNumeric you do things like run
 [the final example of the Python CFD course](https://github.com/barbagroup/CFDPython/blob/master/lessons/15_Step_12.ipynb)
 completely unmodified on 2048 A100 GPUs in a [DGX SuperPOD](https://github.com/barbagroup/CFDPython/blob/master/lessons/15_Step_12.ipynb) and achieve good weak scaling.
 
 <img src="docs/figures/cfd-demo.png" alt="drawing" width="500"/>
 
-Legate NumPy works best for programs that have very large arrays of data 
-that cannot fit in the memory of a single GPU or a single node and need 
-to span multiple nodes and GPUs. While our implementation of the current 
-NumPy API is still incomplete, programs that use unimplemented features 
-will still work (assuming enough memory) by falling back to the 
+cuNumeric works best for programs that have very large arrays of data
+that cannot fit in the memory of a single GPU or a single node and need
+to span multiple nodes and GPUs. While our implementation of the current
+NumPy API is still incomplete, programs that use unimplemented features
+will still work (assuming enough memory) by falling back to the
 canonical NumPy implementation.
 
 If you have questions, please contact us at legate(at)nvidia.com.
@@ -49,58 +49,58 @@ If you have questions, please contact us at legate(at)nvidia.com.
 
 Pre-built docker images containing all Legate libraries are available on the
 [quickstart](https://github.com/nv-legate/quickstart) repo. The next sections
-describe how to build Legate NumPy from source.
+describe how to build cuNumeric from source.
 
 ## Dependencies
 
-Users must have a working installation of the 
+Users must have a working installation of the
 [Legate Core](https://github.com/nv-legate/legate.core)
-library prior to installing Legate NumPy. 
+library prior to installing cuNumeric.
 
-Legate NumPy requires Python >= 3.6. We provide a 
-[conda environment file](conda/legate_numpy_dev.yml) that
+cuNumeric requires Python >= 3.6. We provide a
+[conda environment file](conda/cunumeric_dev.yml) that
 installs all needed dependencies in one step. Use the following command to
 create a conda environment with it:
 ```
-conda env create -n legate -f conda/legate_numpy_dev.yml
+conda env create -n legate -f conda/cunumeric_dev.yml
 ```
 
 ## Building from Source
 
-Installation of Legate NumPy is done with either `setup.py` for simple 
-uses cases or `install.py` for more advanced use cases. The most common 
+Installation of cuNumeric is done with either `setup.py` for simple
+uses cases or `install.py` for more advanced use cases. The most common
 installation command is:
 
 ```
 python setup.py --with-core <path-to-legate-core-installation>
 ```
 
-This will build Legate NumPy against the Legate Core installation and then 
-install Legate NumPy into the same location. Users can also install Legate NumPy 
+This will build cuNumeric against the Legate Core installation and then
+install cuNumeric into the same location. Users can also install cuNumeric
 into an alternative location with the canonical `--prefix` flag as well.
 
 ```
 python setup.py --prefix <install-dir> --with-core <path-to-legate-core-installation>
 ```
 
-Note that after the first invocation of `setup.py` this repository will remember 
-which Legate Core installation to use and the `--with-core` option can be 
+Note that after the first invocation of `setup.py` this repository will remember
+which Legate Core installation to use and the `--with-core` option can be
 omitted unless the user wants to change it.
 
-Advanced users can also invoke `install.py --help` to see options for 
-configuring Legate NumPy by invoking the `install.py` script directly.
+Advanced users can also invoke `install.py --help` to see options for
+configuring cuNumeric by invoking the `install.py` script directly.
 
-Of particular interest to Legate NumPy users will likely be the option for
+Of particular interest to cuNumeric users will likely be the option for
 specifying an installation of [OpenBLAS](https://www.openblas.net/) to use.
-If you already have an installation of OpenBLAS on your machine you can 
+If you already have an installation of OpenBLAS on your machine you can
 inform the `install.py` script about its location using the `--with-openblas` flag:
 ```
 python setup.py --with-openblas /path/to/open/blas/
 ```
 
 ## Usage and Execution
 
-Using Legate NumPy as a replacement for NumPy is easy. Users only need
+Using cuNumeric as a replacement for NumPy is easy. Users only need
 to replace:
 
 ```
@@ -110,59 +110,59 @@ import numpy as np
 with:
 
 ```
-import legate.numpy as np
+import cunumeric as np
 ```
 
 These programs can then be run by the Legate driver script described in the
 [Legate Core](https://github.com/nv-legate/legate.core) documentation.
 
 ```
-legate legate_numpy_program.py
+legate cunumeric_program.py
 ```
 
 For execution with multiple nodes (assuming Legate Core is installed with GASNet support)
-users can supply the `--nodes` flag. For execution with GPUs, users can use the 
+users can supply the `--nodes` flag. For execution with GPUs, users can use the
 `--gpus` flags to specify the number of GPUs to use per node. We encourage all users
 to familiarize themselves with these resource flags as described in the Legate Core
 documentation or simply by passing `--help` to the `legate` driver script.
 
 ## Supported and Planned Features
 
-Legate NumPy is currently a work in progress and we are gradually adding support for 
+cuNumeric is currently a work in progress and we are gradually adding support for
 additional NumPy operators. Unsupported NumPy operations will provide a
 warning that we are falling back to canonical NumPy. Please report unimplemented
 features that are necessary for attaining good performance so that we can triage
-them and prioritize implementation appropriately. The more users that report an 
+them and prioritize implementation appropriately. The more users that report an
 unimplemented feature, the more we will prioritize it. Please include a pointer
 to your code if possible too so we can see how you are using the feature in context.
 
 ## Supported Types and Dimensions
 
-Legate NumPy currently supports the following NumPy types: `float16`, `float32`, `float64`,
-`int16`, `int32`, `int64`, `uint16`, `uint32`, `uint64`, `bool`, `complex64`, and `complex128`. 
+cuNumeric currently supports the following NumPy types: `float16`, `float32`, `float64`,
+`int16`, `int32`, `int64`, `uint16`, `uint32`, `uint64`, `bool`, `complex64`, and `complex128`.
 Legate currently also only works on up to 3D arrays at the moment. We're currently working
 on support for N-D arrays. If you have a need for arrays with more than three
 dimensions please let us know about it.
 
 ## Documentation
 
 A complete list of available features can is provided in the [API
-reference](https://nv-legate.github.io/legate.numpy/api.html).
+reference](https://nv-legate.github.io/cunumeric/api.html).
 
 ## Future Directions
 
-There are three primary directions that we plan to investigate 
-with Legate NumPy going forward: 
+There are three primary directions that we plan to investigate
+with cuNumeric going forward:
 
 * More features: we plan to identify a few key lighthouse applications
-  and use the demands of these applications to drive the addition of 
-  new features to Legate NumPy.
+  and use the demands of these applications to drive the addition of
+  new features to cuNumeric.
 * We plan to add support for sharded file I/O for loading and
   storing large data sets that could never be loaded on a single node.
   Initially this will begin with native support for [h5py](https://www.h5py.org/)
   but will grow to accommodate other formats needed by our lighthouse
   applications.
-* Strong scaling: while Legate NumPy is currently implemented in a way that
+* Strong scaling: while cuNumeric is currently implemented in a way that
   enables weak scaling of codes on larger data sets, we would also like
   to make it possible to strong-scale Legate applications for a single
   problem size. This will require leveraging some of the more advanced
@@ -172,9 +172,9 @@ We are open to comments, suggestions, and ideas.
 
 ## Known Bugs
 
- * Legate NumPy can exercise a bug in OpenBLAS when it is run with
+ * cuNumeric can exercise a bug in OpenBLAS when it is run with
    [multiple OpenMP processors](https://github.com/xianyi/OpenBLAS/issues/2146)
- * On Mac OSX, Legate NumPy can trigger a bug in Apple's implementation of libc++.
+ * On Mac OSX, cuNumeric can trigger a bug in Apple's implementation of libc++.
    The [bug](https://bugs.llvm.org/show_bug.cgi?id=43764) has since been fixed but
    likely will not show up on most Apple machines for quite some time. You may have
    to manually patch your implementation of libc++. If you have trouble doing this

conda/legate_numpy_dev.yml → conda/cunumeric_dev.yml

@@ -1,12 +1,12 @@
-name: legate_numpy_dev
+name: cunumeric_dev
 channels:
   - conda-forge
   - defaults
 dependencies:
   - clang=8.0.1
   - clang-tools=8.0.1
   - cffi
-  - numpy>=1.20
+  - numpy
   - packaging
   - python>=3.6
   - flake8=3.8.3

legate/numpy/__init__.py → cunumeric/__init__.py

@@ -13,16 +13,24 @@
 # limitations under the License.
 #
 
-from __future__ import absolute_import, division, print_function
+"""
+cuNumeric
+=====
+
+Provides a distributed task-parallel implementation of the Numpy interface
+with GPU acceleration.
+
+:meta private:
+"""
 
 import sys as _sys
 
 import numpy as _np
-from legate.numpy import linalg, random
-from legate.numpy.array import ndarray
-from legate.numpy.module import *
-from legate.numpy.ufunc import *
-from legate.numpy.utils import (
+from cunumeric import linalg, random
+from cunumeric.array import ndarray
+from cunumeric.module import *
+from cunumeric.ufunc import *
+from cunumeric.utils import (
     add_missing_attributes as _add_missing_attributes,
 )