dwarf-p-cloudsc

dwarf-p-cloudsc is intended to test the CLOUDSC cloud microphysics scheme of the IFS.

This package is made available to support research collaborations and is not officially supported by ECMWF

Contact

Michael Lange (michael.lange@ecmwf.int), Willem Deconinck (willem.deconinck@ecmwf.int), Balthasar Reuter (balthasar.reuter@ecmwf.int)

Licence

dwarf-p-cloudsc is distributed under the Apache Licence Version 2.0. See LICENSE file for details.

Prototypes available

• dwarf-P-cloudMicrophysics-IFSScheme: The original cloud scheme from IFS that is naturally suited to host-type machines and optimized on the Cray system at ECMWF.
• dwarf-cloudsc-fortran: A cleaned up version of the CLOUDSC prototype that validates runs against platform and language-agnostic off-line reference data via HDF5 or the Serialbox package. The kernel code also is slightly cleaner than the original version.
• dwarf-cloudsc-c: Standalone C version of the kernel that has been generated by ECMWF tools. This relies exclusively on the Serialbox validation mechanism.
• dwarf-cloudsc-gpu-kernels: GPU-enabled version of the CLOUDSC dwarf that uses OpenACC and relies on the !$acc kernels directive to offload the computational kernel.
• dwarf-cloudsc-gpu-claw (deprecated!): GPU-enabled and optimized version of CLOUDSC that is based on an auto-generated version of CLOUDSC based on the CLAW tool. The kernel in this demonstrator has been further optimized with gang-level loop blocking to demonstrate potential performance gains. This variant is defunct on current Nvidia GPUs and therefore deactivated by default, requiring explicit --with-claw flag to build.
• dwarf-cloudsc-gpu-scc: GPU-enabled and optimized version of CLOUDSC that utilises the native blocked IFS memory layout via a "single-column coalesced" (SCC) loop layout. Here the outer NPROMA block loop is mapped to the OpenACC "gang" level and the kernel uses an inverted loop-nest where the outer horizontal loop is mapped to OpenACC " vector" parallelism. This variant lets the CUDA runtime manage temporary arrays and needs a large PGI_ACC_CUDA_HEAPSIZE (eg. PGI_ACC_CUDA_HEAPSIZE=8GB for 160K columns.)
• dwarf-cloudsc-gpu-scc-hoist: GPU-enabled and optimized version of CLOUDSC that also uses the SCC loop layout, but promotes the inner "vector" loop to the driver and declares the kernel as sequential. The block array arguments are fully dimensioned though, and multi-dimensional temporaries have been declared explicitly at the driver level.
• dwarf-cloudsc-gpu-scc-k-caching: GPU-enabled and further optimized version of CLOUDSC that also uses the SCC loop layout in combination with loop fusion and temporary local array demotion.
• dwarf-cloudsc-gpu-scc-cuf: GPU-enabled and optimized version of CLOUDSC that uses the SCC loop layout in combination with CUDA-Fortran (CUF) to explicitly allocate temporary arrays in device memory and move parameter structures to constant memory. To enable this variant, a suitable CUDA installation is required and the --with-cuda flag needs to be passed at the build stage.
• dwarf-cloudsc-gpu-scc-cuf-k-caching: GPU-enabled and further optimized version of CLOUDSC that uses the SCC loop layout in combination with loop fusion and temporary local array demotion, implemented using CUDA-Fortran (CUF). To enable this variant, a suitable CUDA installation is required and the --with-cuda flag needs to be passed at the build stage.
• CUDA C prototypes: To enable these variants, a suitable CUDA installation is required and the --with-cuda flag needs to be pased at the build stage.
  • dwarf-cloudsc-cuda: GPU-enabled, CUDA C version of CLOUDSC.
  • dwarf-cloudsc-cuda-hoist: GPU-enabled, optimized CUDA C version of CLOUDSC including host side hoisted temporary local variables.
  • dwarf-cloudsc-cuda-k-caching: GPU-enabled, further optimized CUDA C version of CLOUDSC including loop fusion and temporary local array demotion.
• dwarf-cloudsc-gpu-scc-field: GPU-enabled and optimized version of CLOUDSC that uses the SCC loop layout, and uses FIELD API (a Fortran library purpose-built for IFS data-structures that facilitates the creation and management of field objects in scientific code) to perform device offload and copyback. The intent is to demonstrate the explicit use of pinned host memory to speed-up data transfers, as provided by the shipped prototype implmentation, and investigate the effect of different data storage allocation layouts. To enable this variant, a suitable CUDA installation is required and the --with-cuda flag needs to be passed at the build stage. This variant lets the CUDA runtime manage temporary arrays and needs a large NV_ACC_CUDA_HEAPSIZE (eg. NV_ACC_CUDA_HEAPSIZE=8GB for 160K columns.)
• cloudsc-pyiface.py: a combination of the cloudsc/cloudsc-driver routines of cloudsc-fortran with the uppermost dwarf program replaced with a corresponding Python script capable of HDF5 data load and verification of computation results. The computation is realized by the Fortran subprogram, mimicking cloudsc-fortran and equipped with only minor modifications (i.e. derived types/global paramters handling). Turned off by default, activate at the build stage with --cloudsc-fortran-pyiface=ON.
• dwarf-cloudsc-fortran-atlas: A version of dwarf-cloudsc-fortran which uses the Atlas library and its Field and FieldSet data stuctures. There are two storage settings for variables. If the environment variable CLOUDSC_ATLAS_MULTIFIELD is "0", "OFF", or "FALSE", the variables are managed as atlas::FieldSet, which is an array of atlas::Fields. For other values of CLOUDSC_ATLAS_MULTIFIELD, a batching of variables is used as (BLK_IDX, LEV, VAR_ID, BLK_ID).

Download and Installation

The code is written in Fortran 2003 and it has been tested using the various compilers, including:

GCC 7.3, 9.3, 11.2
Cray 8.7.7
NVHPC 20.9, 22.1
Intel (classic)

This application does not need MPI nor BLAS libraries for performance. Just a compiler that understands OpenMP directives. Fortran must be at least level F2003.

Inside the dwarf directory you can find some example of outputs inside the example-outputs/ directory.

In addition, to run the dwarf it is necessary to use an input file that can be found inside the config-files/ directory winthin the dwarf folder.

The preferred method to install the CLOUDSC dwarf uses the bundle definition shipped in the main repository. For this please install the bundle via:

./cloudsc-bundle create  # Checks out dependency packages
./cloudsc-bundle build [--build-type=debug|bit|release] [--arch=./arch/ecmwf/machine/compiler/version]

The individual prototype variants of the dwarf are managed as ECBuild features and can be enable or disabled via --cloudsc-<feature>=[ON|OFF] arguments to cloudsc-bundle build.

The use of the boost library or module is required by the Serialbox utility package for filesystem utilities. If boost is not available on a given system, Serialbox's internal "experimental filesystem" can be used via the --serialbox-experimental=ON argument, although this has proven difficult with certain compiler toolchains.

GPU versions of CLOUDSC

The GPU-enabled versions of the dwarf are by default disabled. To enable them use the --with-gpu flag. For example to build on the ECMWF's ATOS A100 nodes:

./cloudsc-bundle create  # Checks out dependency packages
./cloudsc-bundle build --clean --with-gpu --arch=./arch/ecmwf/hpc2020/nvhpc/22.1

MPI-enabled versions of CLOUDSC

Optionally, dwarf-cloudsc-fortran and the GPU versions can be built with MPI support by providing the --with-mpi flag. For example on ATOS:

./cloudsc-bundle create
./cloudsc-bundle build --clean --with-mpi --with-gpu --arch=./arch/ecmwf/hpc2020/nvhpc/22.1

Running with MPI parallelization distributes the columns of the working set among all ranks. The specified number of OpenMP threads is then spawned on each rank. Results are gathered from all ranks and reported for the global working set. Performance numbers are also gathered and reported per thread, per rank and total.

Important: If the total size of the working set (2nd argument, see "Running and testing") exceeds the number of columns in the input file (the input data in the repository consists of just 100 columns), every rank derives its working set by replicating the columns in the input file, starting with the first column in the file. This means, all ranks effectively work on the same data set. If the total size of the working set is less than or equal to the number of columns in the input file, these are truly distributed and every rank ends up with a different working set.

When running with multiple GPUs each rank needs to be assigned a different device. This can be achieved using the CUDA_VISIBLE_DEVICES environment variable:

mpirun -np 2 bash -c "CUDA_VISIBLE_DEVICES=\${OMPI_COMM_WORLD_RANK} bin/dwarf-cloudsc-gpu-claw 1 163840 8192"

Choosing between HDF5 and Serialbox input file format

The default build configuration relies on HDF5 input and reference data for dwarf-cloudsc-fortran as well as GPU and Loki versions. The original dwarf-P-cloudMicrophysics-IFSScheme always uses raw Fortran binary format.

Please note: The HDF55 installation needs to have the f03 interfaces installed (default with HDF5 1.10+).

As an alternative to HDF5, the Serialbox library can be used to load input and reference data. This, however, requires certain boost libraries or its own internal experimental filesystem, both of which proved difficult on certain compiler toolchains or more exotic hardware architectures.

The original input is provided as raw Fortran binary in prototype1, but input and reference data can be regenerated from this variant by running

CLOUDSC_WRITE_INPUT=1 ./bin/dwarf-P-cloudMicrophysics-IFSScheme 1 100 100
CLOUDSC_WRITE_REFERENCE=1 ./bin/dwarf-P-cloudMicrophysics-IFSScheme 1 100 100

Note that this is only available via Serialbox at the moment. Updates to HDF5 input or reference data have to be done via manual conversion. A small Python script for this with usage instructions can be found in the serialbox2hdf5 directory.

Building on ECMWF's Atos BullSequana XH2000

To build on ECMWF's Atos BullSequana XH2000 supercomputer, run the following commands:

./cloudsc-bundle create
./cloudsc-bundle build --arch arch/ecmwf/hpc2020/compiler/version [--single-precision] [--with-mpi]

Currently available compiler/version selections are:

• gnu/9.3.0 and gnu/11.2.0
• intel/2021.4.0
• nvhpc/22.1 (use with --with-gpu on AC's GPU partition)

A64FX version of CLOUDSC

Preliminary results for CLOUDSC have been generated for A64FX CPUs on Isambard. A set of arch and toolchain files and detailed installation and run instructions are provided here.

SYCL version of CLOUDSC

A preliminary SYCL code variant has been added and tested with a custom DPCPP install on ECMWF's AC partition. To build this, please use the SYCL-specific environment setups:

./cloudsc-bundle build --clean --build-dir=build-sycl --with-gpu --with-sycl --with-serialbox --arch=arch/ecmwf/hpc2020/intel-sycl/2021.4.0

# Then run with
cd build-sycl && . env.sh
./bin/dwarf-cloudsc-scc-sycl 1 240000 128
./bin/dwarf-cloudsc-scc-hoist-sycl 1 240000 128
./bin/dwarf-cloudsc-scc-k-caching-sycl 1 240000 128

Running and testing

The different prototype variants of the dwarf create different binaries that all behave similarly. The basic three arguments define (in this order):

• Number of OpenMP threads
  • 1 : single thread mode, skip multithread MPI init; default value;
  • 2 or higher : force OpenMP thread count, enables multithread MPI;
  • 0 or negative : read OMP_NUM_THREADS variable if present or defaults to CPU count (omp_get_max_threads());
• Size of overall working set in columns
• Block size (NPROMA) in columns

An example:

cd build
./bin/dwarf-P-cloudMicrophysics-IFSScheme 4 16384 32  # The original
./bin/dwarf-cloudsc-fortran 4 16384 32   # The cleaned-up Fortran
./bin/dwarf-cloudsc-c 4 16384 32   # The standalone C version

Running on ECMWF's Atos BullSequana XH2000

On the Atos system, a high-watermark run on a single socket can be performed as follows:

export OMP_NUM_THREADS=64
OMP_PLACES="{$(seq -s '},{' 0 $(($OMP_NUM_THREADS-1)) )}" srun -q np --ntasks=1 --hint=nomultithread --cpus-per-task=$OMP_NUM_THREADS ./bin/dwarf-cloudsc-fortran $OMP_NUM_THREADS 163840 32

For a double-precision build with the GNU 11.2.0 compiler, performance of ~73 GF/s is achieved.

To run the GPU variant on AC, which includes some GPU nodes, allocate an interactive session on a GPU node and run the binary as usual:

srun -N1 -q ng -p gpu --gres=gpu:4 --mem 200G --pty /bin/bash
bin/dwarf-cloudsc-gpu-scc-hoist 1 262144 128

For a double-precision build with NVHPC 22.1, performance of ~340 GF/s on a single GPU is achieved.

A multi-GPU run requires MPI (build with --with-mpi) with a dedicated MPI task for each GPU and (at the moment) manually assigning CUDA devices to each rank, as Slurm is not yet fully configured for the GPU partition.

To use four GPUs on one node, allocate the relevant resources

salloc -N 1 --tasks-per-node 4 -q ng -p gpu --gres=gpu:4 --mem 200G

and then run the binary like this:

srun bash -c "CUDA_VISIBLE_DEVICES=\$SLURM_LOCALID bin/dwarf-cloudsc-gpu-scc-hoist 1 \$((\$SLURM_NPROCS*262144)) 128"

In principle, the same should work for multi-node execution (-N 2, -N 4 etc.) once interconnect issues are resolved.

GPU runs: Timing device kernels and data transfers

For GPU-enabled runs two internal timer results are reported:

• The isolated compute time of the main compute kernel on device (where #BLKS == 1)
• The overall time of the execution loop including data offload and copyback

It is important to note that due to the nature of the kernel, data transfer overheads will dominate timings, and that most supported GPU variants aim to optimise compute kernel timings only. However, a dedicated variant dwarf-cloudsc-gpu-scc-field has been added to explore host-side memory pinning, which improves data transfer times and alternative data layout strategies. By default, this will allocate each array variable individually in pinned memory. A runtime flag CLOUDSC_PACKED_STORAGE=ON can be used to enable "packed" storage, where multiple arrays are stored in a single base allocation, eg.

NV_ACC_CUDA_HEAPSIZE=8G CLOUDSC_PACKED_STORAGE=ON ./bin/dwarf-cloudsc-gpu-scc-field 1 80000 128

Loki transformations for CLOUDSC

Loki is an in-house developed source-to-source translation tool that allows us to create bespoke transformations for the IFS to target and experiment with emerging HPC architectures and programming models. We use the CLOUDSC dwarf as a demonstrator for targeted transformation capabilities of physics and grid point computations kernels, including conversion to C and GPU, directly or via downstream tools like CLAW.

The following build flags enable the demonstrator build targets on the ECMWF Atos HPC facility's GPU partition:

./cloudsc-bundle build --clean [--with-gpu] --with-loki --loki-frontend=fp --arch=./arch/ecmwf/hpc2020/nvhpc/22.1

The following Loki modes are included in the dwarf, each with a bespoke demonstrator build:

• cloudsc-loki-idem: "Idempotence" mode that performs a full parse-unparse cycle of the kernel and performs various housekeeping transformations, including the driver-level source injection mechanism currently facilitated by Loki.
• cloudsc-loki-sca: Pure single-column mode that strips all horizontal vector loops from the kernel and introduces an outer "column-loop" at the driver level.
• cloudsc-loki-claw-cpu (deprecated): Same as SCA, but also adds the necessary CLAW annotations. The resulting cloudsc.claw.F90 file is then processed by CLAW to re-insert vector loops for optimal CPU execution.
• cloudsc-loki-claw-gpu (deprecated): Creates the same CLAW-ready kernel file, but triggers the GPU-specific optimizations in the CLAW compiler to insert OpenACC-offload instructions in the driver and an OpenACC parallel loop inside the kernel for each block. This needs to be run with large block sizes (eg. NPROMA=1024-8192).
• cloudsc-loki-c: A prototype C transpilation pipeline that converts the kernel to C and calls it via iso_c_bindings interfaces from the driver.

To enable the deprecated and, on GPU, defunct CLAW variants, the build-flag --with-claw needs to be specified explicitly.

Python-driven CLOUDSC variants

The following partly or fully Python-based CLOUDSC are available:

• cloudsc-python: GT4PY based Python-only implementation. Refer to src/cloudsc_python for information on how to bootstrap/execute this variant
• cloudsc-pyiface: Fortran-based CLOUDSC variant driven by the Python script. Activate with:

./cloudsc-bundle build --clean --cloudsc-fortran-pyiface=ON

These variants are disabled by default. Refer to README.md in corresponding subdirectories for further information.

A note on frontends

Loki currently supports three frontends to parse the Fortran source code:

• FParser (loki-frontend=fp): The preferred default; developed by STFC for PsyClone.
• OMNI frontend (loki-frontend=omni): Generates the same AST as used by CLAW.
• OFP, a Python wrapper around the ROSE frontend (loki-frontend=ofp): Supported, but bugged in some places and slow; use with care.

For completeness, all three frontends are tested in our CI, which means we require the .xmod module description files for utility routines in src/common for processing the CLOUDSC source files with the OMNI frontend. These are stored in the source under src/cloudsc_loki/xmod.

Benchmarking

To automate parameter space sweeps and ease testing across various platforms, a JUBE benchmark definition is included in the directory benchmark. See the included README for further details and usage instructions.