Merge pull request #1638 from LLNL/v2024.02.2-RC

V2024.02.2 RC merge to main

Author: rhornung67

.gitlab-ci.yml

@@ -75,7 +75,7 @@ stages:
     include:
       - local: '.gitlab/custom-jobs-and-variables.yml'
       - project: 'radiuss/radiuss-shared-ci'
-        ref: 'v2023.12.3'
+        ref: 'v2024.04.0'
         file: 'pipelines/${CI_MACHINE}.yml'
       - artifact: '${CI_MACHINE}-jobs.yml'
         job: 'generate-job-lists'
@@ -100,9 +100,11 @@ trigger-rajaperf:
     strategy: depend
 
 include:
+  - project: 'lc-templates/id_tokens'
+    file: 'id_tokens.yml'
   # [Optional] checks preliminary to running the actual CI test
   - project: 'radiuss/radiuss-shared-ci'
-    ref: 'v2023.12.3'
+    ref: 'v2024.04.0'
     file: 'utilities/preliminary-ignore-draft-pr.yml'
   # pipelines subscribed by the project
   - local: '.gitlab/subscribed-pipelines.yml'

.gitlab/custom-jobs-and-variables.yml

@@ -19,17 +19,17 @@ variables:
 # Note: We repeat the reservation, necessary when jobs are manually re-triggered.
   RUBY_JOB_ALLOC: "--reservation=ci --nodes=1"
 # Project specific variants for ruby
-  PROJECT_RUBY_VARIANTS: "~shared +openmp +tests"
+  PROJECT_RUBY_VARIANTS: "~shared +openmp +vectorization +tests"
 # Project specific deps for ruby
   PROJECT_RUBY_DEPS: ""
 
 # Poodle
 # Arguments for top level allocation
-  POODLE_SHARED_ALLOC: "--exclusive --time=60 --nodes=1"
+  POODLE_SHARED_ALLOC: "--exclusive --time=120 --nodes=1"
 # Arguments for job level allocation
   POODLE_JOB_ALLOC: "--nodes=1"
 # Project specific variants for poodle
-  PROJECT_POODLE_VARIANTS: "~shared +openmp +tests"
+  PROJECT_POODLE_VARIANTS: "~shared +openmp +vectorization +tests"
 # Project specific deps for poodle
   PROJECT_POODLE_DEPS: ""
 
@@ -39,26 +39,26 @@ variables:
 # Arguments for job level allocation
   CORONA_JOB_ALLOC: "--nodes=1 --begin-time=+5s"
 # Project specific variants for corona
-  PROJECT_CORONA_VARIANTS: "~shared ~openmp +tests"
+  PROJECT_CORONA_VARIANTS: "~shared ~openmp +vectorization +tests"
 # Project specific deps for corona
   PROJECT_CORONA_DEPS: "^blt@develop "
 
 # Tioga
 # Arguments for top level allocation
-  TIOGA_SHARED_ALLOC: "--exclusive --time-limit=60m --nodes=1 -o per-resource.count=2"
+  TIOGA_SHARED_ALLOC: "--exclusive --queue=pci --time-limit=60m --nodes=1 -o per-resource.count=2"
 # Arguments for job level allocation
   TIOGA_JOB_ALLOC: "--nodes=1 --begin-time=+5s"
 # Project specific variants for corona
-  PROJECT_TIOGA_VARIANTS: "~shared ~openmp +tests"
+  PROJECT_TIOGA_VARIANTS: "~shared +openmp +vectorization +tests"
 # Project specific deps for corona
   PROJECT_TIOGA_DEPS: "^blt@develop "
 
 # Lassen and Butte use a different job scheduler (spectrum lsf) that does not
 # allow pre-allocation the same way slurm does.
 # Arguments for job level allocation
-  LASSEN_JOB_ALLOC: "1 -W 30 -q pci"
+  LASSEN_JOB_ALLOC: "1 -W 40 -q pci"
 # Project specific variants for lassen
-  PROJECT_LASSEN_VARIANTS: "~shared +openmp +tests cuda_arch=70"
+  PROJECT_LASSEN_VARIANTS: "~shared +openmp +vectorization +tests cuda_arch=70"
 # Project specific deps for lassen
   PROJECT_LASSEN_DEPS: "^blt@develop "
 

CMakeLists.txt

@@ -16,7 +16,7 @@ include(CMakeDependentOption)
 # Set version number
 set(RAJA_VERSION_MAJOR 2024)
 set(RAJA_VERSION_MINOR 02)
-set(RAJA_VERSION_PATCHLEVEL 1)
+set(RAJA_VERSION_PATCHLEVEL 2)
 
 if (RAJA_LOADED AND (NOT RAJA_LOADED STREQUAL "${RAJA_VERSION_MAJOR}.${RAJA_VERSION_MINOR}.${RAJA_VERSION_PATCHLEVEL}"))
   message(FATAL_ERROR "You are mixing RAJA versions. Loaded is ${RAJA_LOADED}, expected ${RAJA_VERSION_MAJOR}.${RAJA_VERSION_MINOR}.${RAJA_VERSION_PATCHLEVEL}")

RELEASE_NOTES.md

@@ -20,6 +20,39 @@ Notable changes include:
   * Bug fixes/improvements:
 
 
+Version 2024.02.2 -- Release date 2024-05-08
+============================================
+
+This release contains a bugfix and new execution policies that improve
+performance for GPU kernels with reductions.
+
+Notable changes include:
+
+  * New features / API changes:
+     * New GPU execution policies for CUDA and HIP added which provide
+       improved performance for GPU kernels with reductions. Please see the 
+       RAJA User Guide for more information. Short summary:
+         * Option added to change max grid size in policies that use the
+           occupancy calculator.
+         * Policies added to run with max occupancy, a fraction of of the
+           max occupancy, and to run with a "concretizer" which allows a 
+           user to determine how to run based on what the occupancy 
+           calculator determines about a kernel.
+         * Additional options to tune kernels containing reductions, such as
+             * an option to initialize data on host for reductions that use
+               atomic operations
+             * an option to avoid device scope memory fences 
+     * Change ordering of SYCL thread index ordering in RAJA::launch to 
+       follow the SYCL "row-major" convention. Please see RAJA User Guide
+       for more information.
+
+  * Build changes/improvements:
+     * NONE.
+
+  * Bug fixes/improvements:
+     * Fixed issue in bump-style allocator used internally in RAJA::launch.
+
+
 Version 2024.02.1 -- Release date 2024-04-03
 ============================================
 

docs/Licenses/rocprim-license.txt

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2017-2024 Advanced Micro Devices, Inc. All rights reserved.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

docs/conf.py

@@ -88,7 +88,7 @@
 # The short X.Y version.
 version = u'2024.02'
 # The full version, including alpha/beta/rc tags.
-release = u'2024.02.1'
+release = u'2024.02.2'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

docs/sphinx/user_guide/cook_book.rst

@@ -0,0 +1,23 @@
+.. ##
+.. ## Copyright (c) 2016-24, Lawrence Livermore National Security, LLC
+.. ## and RAJA project contributors. See the RAJA/LICENSE file
+.. ## for details.
+.. ##
+.. ## SPDX-License-Identifier: (BSD-3-Clause)
+.. ##
+
+.. _cook-book-label:
+
+************************
+RAJA Cook Book
+************************
+
+The following sections show common use case patterns and the recommended
+RAJA features and policies to use with them. They are intended
+to provide users with complete beyond usage examples beyond what can be found in other parts of the RAJA User Guide. In particular, the examples and discussion provide guidance on RAJA execution policy selection to improve performance of user application codes.
+
+.. toctree::
+   :maxdepth: 2
+
+   cook_book/reduction
+

docs/sphinx/user_guide/cook_book/reduction.rst

@@ -0,0 +1,110 @@
+.. ##
+.. ## Copyright (c) 2016-24, Lawrence Livermore National Security, LLC
+.. ## and other RAJA project contributors. See the RAJA/LICENSE file
+.. ## for details.
+.. ##
+.. ## SPDX-License-Identifier: (BSD-3-Clause)
+.. ##
+
+.. _cook-book-reductions-label:
+
+=======================
+Cooking with Reductions
+=======================
+
+Please see the following section for overview discussion about RAJA reductions:
+
+ * :ref:`feat-reductions-label`.
+
+
+----------------------------
+Reductions with RAJA::forall
+----------------------------
+
+Here is the setup for a simple reduction example::
+
+  const int N = 1000;
+
+  int vec[N];
+
+  for (int i = 0; i < N; ++i) {
+
+    vec[i] = 1;
+
+  }
+
+Here a simple sum reduction is performed in a for loop::
+
+  int vsum = 0;
+
+  // Run a kernel using the reduction objects
+  for (int i = 0; i < N; ++i) {
+
+    vsum += vec[i];
+
+  }
+
+The results of these operations will yield the following values:
+
+ * ``vsum == 1000``
+
+RAJA uses policy types to specify how things are implemented.
+
+The forall *execution policy* specifies how the loop is run by the ``RAJA::forall`` method. The following discussion includes examples of several other RAJA execution policies that could be applied.
+For example ``RAJA::seq_exec`` runs a C-style for loop sequentially on a CPU. The
+``RAJA::cuda_exec_with_reduce<256>`` runs the loop as a CUDA GPU kernel with
+256 threads per block and other CUDA kernel launch parameters, like the
+number of blocks, optimized for performance with reducers.::
+
+  using exec_policy = RAJA::seq_exec;
+  // using exec_policy = RAJA::omp_parallel_for_exec;
+  // using exec_policy = RAJA::omp_target_parallel_for_exec<256>;
+  // using exec_policy = RAJA::cuda_exec_with_reduce<256>;
+  // using exec_policy = RAJA::hip_exec_with_reduce<256>;
+  // using exec_policy = RAJA::sycl_exec<256>;
+
+The reduction policy specifies how the reduction is done and must match the
+execution policy. For example ``RAJA::seq_reduce`` does a sequential reduction
+and can only be used with sequential execution policies. The
+``RAJA::cuda_reduce_atomic`` policy uses atomics, if possible with the given
+data type, and can only be used with cuda execution policies. Similarly for other RAJA execution back-ends, such as HIP and OpenMP. Here are example RAJA reduction policies whose names are indicative of which execution policies they work with::
+
+  using reduce_policy = RAJA::seq_reduce;
+  // using reduce_policy = RAJA::omp_reduce;
+  // using reduce_policy = RAJA::omp_target_reduce;
+  // using reduce_policy = RAJA::cuda_reduce_atomic;
+  // using reduce_policy = RAJA::hip_reduce_atomic;
+  // using reduce_policy = RAJA::sycl_reduce;
+
+
+Here a simple sum reduction is performed using RAJA::
+
+  RAJA::ReduceSum<reduce_policy, int> vsum(0);
+
+  RAJA::forall<exec_policy>( RAJA::RangeSegment(0, N),
+    [=](RAJA::Index_type i) {
+
+    vsum += vec[i];
+
+  });
+
+The results of these operations will yield the following values:
+
+ * ``vsum.get() == 1000``
+
+
+Another option for the execution policy when using the cuda or hip backends are
+the base policies which have a boolean parameter to choose between the general
+use ``cuda/hip_exec`` policy and the ``cuda/hip_exec_with_reduce`` policy.::
+
+  // static constexpr bool with_reduce = ...;
+  // using exec_policy = RAJA::cuda_exec_base<with_reduce, 256>;
+  // using exec_policy = RAJA::hip_exec_base<with_reduce, 256>;
+
+Another option for the reduction policy when using the cuda or hip backends are
+the base policies which have a boolean parameter to choose between the atomic
+``cuda/hip_reduce_atomic`` policy and the non-atomic ``cuda/hip_reduce`` policy.::
+
+  // static constexpr bool with_atomic = ...;
+  // using reduce_policy = RAJA::cuda_reduce_base<with_atomic>;
+  // using reduce_policy = RAJA::hip_reduce_base<with_atomic>;