Spack - A Package Manager for HPC

LLNL-PRES-747560
This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under contract DE-
AC52-07NA27344. Lawrence Livermore National Security, LLC
Spack: A Package Manager for HPC
2019 HPC-AI Advisory Council Stanford Conference
Todd GamblinFebruary 14, 2019
Stanford University
Computer Scientist
@spackpmgithub.com/spack

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Scientific software is becoming extremely complex
R Miner: R Data Mining Librarydealii: C++ Finite Element LibraryNalu: Generalized Unstructured Massively Parallel Low Mach Flow

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 Half of this DAG is external (blue); more than half of it is open source
 Nearly all of it needs to be built specially for HPC to get the best performance
Even proprietary codes are based on many open source libraries

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The Exascale Computing Project is building an entire ecosystem
 Every application has its own stack of dependencies.
 Developers, users, and facilities dedicate (many) FTEs to building & porting.
 Often trade reuse and usability for performance.
80+ software packagesx
5+ target architectures/platforms
Xeon Power KNL
NVIDIA ARM Laptops?
x
Up to 7 compilers
Intel GCC Clang XL
PGI Cray NAG
x
= up to 1,260,000 combinations!
15+ applications
x
10+ Programming Models
OpenMPI MPICH MVAPICH OpenMP CUDA
OpenACC Dharma Legion RAJA Kokkos
2-3 versions of each package +
external dependencies
x
We must make it easier to rely on others’ software!

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How to install software on a Mac laptop, circa 2013

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How to install software on a supercomputer
configuremake
Fightwithcompiler...
make
Tweakconfigureargs...
makeinstall
makeconfigure
configuremake
makeinstall
cmakemakemakeinstall
1. Download all 16
tarballs you need
2. Start building!
3. Run code
4. Segfault!?
5. Start over…

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 Most supercomputers deploy some form of environment modules
— TCL modules (dates back to 1995) and Lmod (from TACC) are the most popular
 Modules don’t handle installation!
— They only modify your environment (things like PATH, LD_LIBRARY_PATH, etc.)
 Someone (likely a team of people) has already installed gcc for you!
— Also, you can only `module load` the things they’ve installed
What about modules?
$ gcc
- bash: gcc: command not found
$ module load gcc/7.0.1
$ gcc –dumpversion
7.0.1

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 Containers provide a great way to reproduce and distribute an
already-built software stack
 Someone needs to build the container!
— This isn’t trivial
— Containerized applications still have hundreds of dependencies
 Using the OS package manager inside a container is insufficient
— Most binaries are built unoptimized
— Generic binaries, not optimized for specific architectures
 Developing with an OS software stack can be painful
— Little freedom to choose versions
— Little freedom to choose compiler options, build options, etc. for packages
What about containers?
We need something more flexible to build the containers

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 How to install Spack (works out of the box):
 How to install a package:
 HDF5 and its dependencies are installed
within the Spack directory.
 Unlike typical package managers, Spack can also
install many variants of the same build.
— Different compilers
— Different MPI implementations
— Different build options
Spack is a flexible package manager for HPC
$ git clone https://github.com/spack/spack
$ . spack/share/spack/setup-env.sh
$ spack install hdf5
@spackpm
github.com/spack/spack
Visit spack.io

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 Each expression is a spec for a particular configuration
— Each clause adds a constraint to the spec
— Constraints are optional – specify only what you need.
— Customize install on the command line!
 Spec syntax is recursive
— Full control over the combinatorial build space
Spack provides the spec syntax to describe custom configurations
$spack installmpileaks unconstrained
$spack installmpileaks@3.3 @ custom version
$spack installmpileaks@3.3%gcc@4.7.3 %custom compiler
$spack installmpileaks@3.3%gcc@4.7.3 +threads +/- build option
$spack installmpileaks@3.3cxxflags="-O3–g3” settingcompiler flags
$spack installmpileaks@3.3os=cnl10target=haswell settingtargetforX-compile
$spack installmpileaks@3.3^mpich@3.2 %gcc@4.9.3 ^ dependencyinformation

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`spack list` shows what packages are available
 Spack has over 3,000 builtin package recipes.
$spacklist
==>3041 packages.
abinit glew nalu py-fastaindex r-cairo r-viridislite
abyss glfmultiples nalu-wind py-fasteners r-callr r-visnetwork
accfft glib namd py-faststructure r-car r-vsn
ack glibmm nano py-filelock r-caret r-webshot
activeharmony glimmer nanoflann py-fiona r-category r-whisker
adept-utils glm nanopb py-fiscalyear r-catools r-withr
adios global nasm py-flake8 r-cdcfluview r-xde
adios2 globalarrays nauty py-flake8-polyfill r-cellranger r-xgboost
adlbx globus-toolkit ncbi-magicblast py-flask r-checkmate r-xlconnect
adol-c glog ncbi-rmblastn py-flask-compress r-checkpoint r-xlconnectjars
aegean gloo ncbi-toolkit py-flask-socketio r-chemometrics r-xlsx
aida glpk nccl py-flexx r-chron r-xlsxjars
albany glproto nccmp py-fn r-circlize r-xmapbridge
albert glvis ncdu py-fparser r-class r-xml
alglib gmake ncftp py-funcsigs r-classint r-xml2
allinea-forge gmap-gsnap ncl py-functools32 r-cli r-xnomial
allinea-reports gmime nco py-future r-clipr r-xtable
allpaths-lg gmodel ncurses py-futures r-cluster r-xts
alquimia gmp ncview py-fypp r-clustergeneration r-xvector
alsa-lib gmsh ndiff py-gdbgui r-clusterprofiler r-yaml
aluminum gmt nek5000 py-genders r-cner r-yapsa
amg gnat nekbone py-genshi r-coda r-yaqcaffy
amg2013 gnu-prolog nekcem py-geopandas r-codetools r-yarn
amp gnupg nektar py-gevent r-coin r-zlibbioc
ampliconnoise gnuplot neovim py-git-review r-colorspace r-zoo
amrex gnutls nest py-git2 r-combinat r3d
amrvis go netcdf py-gnuplot r-complexheatmap racon
andi go-bootstrap netcdf-cxx py-goatools r-compositions raft
angsd gobject-introspection netcdf-cxx4 py-gpaw r-convevol ragel
ant googletest netcdf-fortran py-greenlet r-corhmm raja
antlr gotcha netgauge py-griddataformats r-corpcor randfold
ants gource netgen py-guidata r-corrplot random123
ape gperf netlib-lapack py-guiqwt r-covr randrproto
. ..

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 All the versions coexist!
— Multiple versions of same
package are ok.
 Packages are installed to
automatically find correct
dependencies.
 Binaries work regardless of
user’s environment.
 Spack also generates
module files.
— Don’t have to use them.
`spack find` shows what is installed
$ spack find
==> 103 installed packages.
-- linux-rhel7-x86_64 / gcc@4.4.7 --------------------------------
ImageMagick@6.8.9-10 glib@2.42.1 libtiff@4.0.3 pango@1.36.8 qt@4.8.6
SAMRAI@3.9.1 graphlib@2.0.0 libtool@2.4.2 parmetis@4.0.3 qt@5.4.0
adept-utils@1.0 gtkplus@2.24.25 libxcb@1.11 pixman@0.32.6 ravel@1.0.0
atk@2.14.0 harfbuzz@0.9.37 libxml2@2.9.2 py-dateutil@2.4.0 readline@6.3
boost@1.55.0 hdf5@1.8.13 llvm@3.0 py-ipython@2.3.1 scotch@6.0.3
cairo@1.14.0 icu@54.1 metis@5.1.0 py-nose@1.3.4 starpu@1.1.4
callpath@1.0.2 jpeg@9a mpich@3.0.4 py-numpy@1.9.1 stat@2.1.0
dyninst@8.1.2 libdwarf@20130729 ncurses@5.9 py-pytz@2014.10 xz@5.2.0
dyninst@8.1.2 libelf@0.8.13 ocr@2015-02-16 py-setuptools@11.3.1 zlib@1.2.8
fontconfig@2.11.1 libffi@3.1 openssl@1.0.1h py-six@1.9.0
freetype@2.5.3 libmng@2.0.2 otf@1.12.5salmon python@2.7.8
gdk-pixbuf@2.31.2 libpng@1.6.16 otf2@1.4 qhull@1.0
-- linux-rhel7-x86_64 / gcc@4.8.2 --------------------------------
adept-utils@1.0.1 boost@1.55.0 cmake@5.6-special libdwarf@20130729 mpich@3.0.4
adept-utils@1.0.1 cmake@5.6 dyninst@8.1.2 libelf@0.8.13 openmpi@1.8.2
-- linux-rhel7-x86_64 / intel@14.0.2 -----------------------------
hwloc@1.9 mpich@3.0.4 starpu@1.1.4
adept-utils@1.0.1 boost@1.55.0 libdwarf@20130729 libelf@0.8.13 mpich@3.0.4
adept-utils@1.0.1 callpath@1.0.2 libdwarf@20130729 mpich@3.0.4
boost@1.55.0 hwloc@1.9 libelf@0.8.13 starpu@1.1.4

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Users can query the full dependency configuration
of installed packages.
 Architecture, compiler, versions, and variants may differ between builds.
$ spack find callpath
-- linux-rhel7-x86_64 / clang@3.4 ———————— -- linux-rhel7-x86_64 / gcc@4.9.2 -------------
callpath@1.0.2 callpath@1.0.2
Expand dependencies
with spack find -d
$ spack find -dl callpath
-- linux-rhel7-x86_64 / clang@3.4 ----------- -- linux-rhel7-x86_64 / gcc@4.9.2 -----------
xv2clz2 callpath@1.0.2 udltshs callpath@1.0.2
ckjazss âdept-utils@1.0.1 rfsu7fb âdept-utils@1.0.1
3ws43m4 ^boost@1.59.0 ybet64y ^boost@1.55.0
ft7znm6 ^mpich@3.1.4 aa4ar6i ^mpich@3.1.4
qqnuet3 ^dyninst@8.2.1 tmnnge5 ^dyninst@8.2.1
g65rdud ^libdwarf@20130729 g2mxrl2 ^libdwarf@20130729
cj5p5fk ^libelf@0.8.13 ynpai3j ^libelf@0.8.13
$ spack find -dl callpath
-- linux-rhel7-x86_64 / clang@3.4 ----------- -- linux-rhel7-x86_64 / gcc@4.9.2 -----------
xv2clz2 callpath@1.0.2 udltshs callpath@1.0.2
ckjazss âdept-utils@1.0.1 rfsu7fb âdept-utils@1.0.1
qqnuet3 ^dyninst@8.2.1 tmnnge5 ^dyninst@8.2.1

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Spack packages are templates
They use a simple Python DSL to define how to build a spec
from spack import *
class Dyninst(Package):
"""API for dynamic binary instrumentation.""”
homepage = "https://paradyn.org"
url = "http://www.paradyn.org/release8.1.2/DyninstAPI-8.1.2.tgz"
version('8.2.1', 'abf60b7faabe7a2e’)
version('8.1.2', 'bf03b33375afa66f’)
version('8.1.1', 'd1a04e995b7aa709’)
depends_on("cmake", type="build")
depends_on("libelf", type="link")
depends_on("libdwarf", type="link")
depends_on("boost @1.42: +multithreaded")
def install(self, spec, prefix):
with working_dir('spack-build', create=True):
cmake('-DBoost_INCLUDE_DIR=‘ + spec['boost'].prefix.include,
'-DBoost_LIBRARY_DIR=‘ + spec['boost'].prefix.lib,
'-DBoost_NO_SYSTEM_PATHS=TRUE’
'..')
make()
make("install")
Metadata at the class level
Versions
Install logic in instance methods
Dependencies (note: they use the same spec syntax)
Patches, variants, resources, conflicts, etc.
(not shown)

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 Each unique dependency graph is a unique
configuration.
 Each configuration installed in a unique directory.
— Configurations of the same package can coexist.
 Hash of entire directed acyclic graph (DAG) is
appended to each prefix.
 Installed packages automatically find dependencies
— Spack embeds RPATHs in binaries.
— No need to use modules or set LD_LIBRARY_PATH
— Things work the way you built them
Spack handles combinatorial software complexity.
spack/opt/
linux-x86_64/
gcc-4.7.2/
mpileaks-1.1-0f54bf34cadk/
intel-14.1/
hdf5-1.8.15-lkf14aq3nqiz/
bgq/
xl-12.1/
hdf5-1-8.16-fqb3a15abrwx/
...
Installation Layout
Dependency DAG
Hash
No limit on the number of versions you can have installed.

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 mpi is a virtual dependency
 Install the same package built with two
different MPI implementations:
 Virtual deps are replaced with a valid
implementation at resolution time.
— If the user didn’t pick something and there are
multiple options, Spack picks.
Depend on interfaces (not implementations)
with virtual dependencies
$ spack install mpileaks ^mvapich
$ spack install mpileaks ^openmpi@1.4:
class Mpileaks(Package):
depends_on("mpi@2:")
class Mvapich(Package):
provides("mpi@1” when="@:1.8")
provides("mpi@2” when="@1.9:")
class Openmpi(Package):
provides("mpi@:2.2" when="@1.6.5:")
Virtual dependencies can be versioned:
dependent
provider
provider

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Concretization fills in missing parts of requested specs.
mpileaks ^callpath@1.0+debug ^libelf@0.8.11
Concrete spec is fully constrained
and can be passed to install.
Concretize
 Workflow:
1. Users input only an abstract spec with some constraints
2. Spack makes choices according to policies (site/user/etc.)
3. Spack installs concrete configurations of package + dependencies
 Dependency resolution is an NP-complete problem!
— Different versions/configurations of packages require different
versions/configurations of dependencies
— Concretizer searches for a configuration that satisfies all the
requirements
— This is basically a SAT/SMT solve

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Dependency Resolution is an NP-hard problem!
 Different versions of packages require different
versions of dependencies
— Concretizer searches for a configuration that satisfies all
the requirements
— Can show that SAT/SMT solve is equivalent problem
 Resolution is NP-complete for *just* package and
version metadata
— Concretization also includes compilers, variants,
architecture, optional dependencies, virtual
dependencies
— We have some leeway because multiple stacks can
coexist within Spack (unlike system PMs)
— Even within one DAG there can be issues!
https://research.swtch.com/version-sat
Unsatisfiable!

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Spack is used worldwide!
Over 350 contributors
from labs, academia, industry
Over 3,000 software packages
Over 150,000 downloads in the past year
Over 1,100 monthly active users (on docs site)
Plot shows sessions on
spack.readthedocs.io for one month

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 Started Spack development in 2013
— Paper at SC15
— Tutorials at SC16, SC17, SC18
— GitHub community has grown steadily!
 232 pull requests merged in lead-up to
SC18!
— By 74 contributors
— We’ve been gradually increasing core
contributors
Spack has a very active open source community

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 We try to make it easy to modify a package
— spack edit <package>
— Pull request
 Contributors are HPC software developers
as well as user support teams and admins
 We get contributions in the core as well as
in packages
 LLNL still ha a majority of the core
contributions, with significant help from
others.
Spack has benefitted tremendously from external contributions

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Spack is being used on many of the top HPC systems
 At HPC sites for software stack+ modules
— Reduced Summit deploy time from 2 weeks to 12 hrs.
— EPFL deploys its software stack with Jenkins + Spack
— NERSC, LLNL, ANL, other US DOE sites
— SJTU in China
 Within ECP as part of their software release process
— ECP-wide software distribution
— SDK workflows
 Within High Energy Physics (HEP) community
— HEP (Fermi, CERN) have contributed many features to
support their workflow
 Many others
Summit (ORNL)
Sierra (LLNL)
Cori (NERSC)
SuperMUC-NG (LRZ)

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 New stuff:
1. Spack environments (covered today)
2. spack.yaml and spack.lock files for tracking dependencies (covered today)
3. Custom configurations via command line (covered today)
4. Better support for linking Python packages into view directories (pip in views)
5. Support for uploading build logs to CDash
6. Packages have more control over compiler flags via flag handlers
7. Better support for module file generation
8. Better support for Intel compilers, Intel MPI, etc.
9. Many performance improvements, improved startup time
 Spack is now permissively licensed under Apache-2.0 or MIT
— previously LGPL
 Over 2,900 packages (800 added since last year)
— This is from November; over 3,000 in latest develop branch
Spack v0.12.1 was just released

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 Allows developers to bundle Spack configuration with their repository
 Can also be used to maintain configuration together with Spack packages.
— E.g., versioning your own local software stack with consistent compilers/MPI
implementations
 Manifest / Lockfile model pioneered by Bundler is becoming standard
— spack.yaml describes project requirements
— spack.lock describes exactly what versions/configurations were installed, allows
them to be reproduced.
Spack has added environments and spack.yaml / spack.lock
Simple spack.yaml file
install build
project
spack.yaml file with
names of required
dependencies
Lockfile describes
exact versions installed
Dependency
packages

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 We recently started providing base images on DockerHub with Spack preinstalled.
 Very easy to build a container with some Spack packages in it:
Spack environments also help with building containers
spack-docker-demo/
Dockerfile
spack.yaml
Base image with Spack in PATH
Copy in spack.yaml
Then run spack install
List of packages to install,
with constraints
Build with docker build .
Run with Singularity
(or another tool)

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 Supporting the U.S. Exascale project with binary builds
— Spack will be used to manage ECP software releases
— In conjunction with ECP CI, start to generate prebuilt binaries for HPC facilities
— Use the same relocatable binary packages for container deployment
 Spack stacks: Build on environments to enable more automated deployment at HPC centers.
— Single YAML-file configuration for entire site stack
— Install massive combinatorial package installations, modules, etc. with one command.
 Spack chains:
— Allow user Spack instances to leverage facility and team installations
— Hierarchical development flow
 Architecture-specific binaries
— Better provenance for builds
— Better support for matching optimized binary packages to machines
 Better dependency resolution
— Handle newer C++ libraries better
— More aggressive concretizer support
— Support for depending on language levels/compiler features (e.g., C++14, lambdas, OpenMP@version)
What’s on the road map?

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 U.S. Exascale Computing Project (ECP)
will release software through Spack
 Software in ECP stack needs to run on ECP platforms,
testbeds, clusters, laptops
— Each new environment requires effort.
 ECP asks us to build a robust, reliable,
and easy-to-use software stack
 We will provide the infrastructure necessary to make this tractable:
1. A dependency model that can handle HPC software
2. A hub for coordinated software releases (like xSDK)
3. Build and test automation for large packages across facility
4. Hosted binary and source software distributions for all ECP HPC platforms
Spack is the delivery platform for the ECP software stack

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 CI at HPC centers is notoriously difficult
— Security concerns prevent most CI tools from being run by staff or by users
— HPC centers really need to deploy trusted CI services for this to work
 We are developing a secure CI system for HPC centers:
— Setuid runners (run CI jobs as users); Batch integration (similar, but parallel jobs); multi-center runner support
 Onyx Point will upstream this support into GitLab CI
— Initial rollout in FY19 at ECP labs: ANL, ORNL, NERSC, LLNL, LANL, SNL
— Upstream GitLab features can be used by anyone!
Through ECP, we are working with Onyx Point to deliver
continuous integration for HPC centers
User checks out / commits
code
Two-factor authentication
Fast mirroring
Setuid runner Batch runner
Trusted runners at HPC facility

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We are building CI infrastructure for source and
binary distribution
User
Amazon S3
source mirror
Source archives
Binary packages
Amazon S3
binary mirror
HPC Centers
Pull requests

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Spack stacks: entire facility deployments in a single YAML file
 Allow users to easily express a huge cross-
product of specs
— All the packages needed for a facility
— Generate modules tailored to the site
— Generate a directory layout to browse the packages
 Build on the environments workflow
— Manifest + lockfile
— Lockfile enables reproducibility
 Relocatable binaries allow the same binary to be
used in a stack, regular install, or container build.
— Difference is how the user interacts with the stack
— Single-PATH stack vs. modules.

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 As an HPC package manager, we want to provide optimized builds
— Code level choices (O2, O3)
— Architecture specific choices (-mcpu=cortex-a7, -march=haswell)
 Architectures vary as to how much they expose features to users
— x86 exposes feature sets in /proc/cpuinfo
— Arm hides many features behind revision number
 Methods for accessing architecture optimizations
— Vary by both compiler and architecture
• Gcc –mcpu vs. –march, for example
• Relies on architectures providing a programmatic way to get information
 We want to expose the names users understand
— Thunderx2, cortex-a7 for arm
— Power8, power9 for IBM
— Haswell, skylake for Intel
Specific target information in specs – In progress

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LLNL-PRES-747560
 Spack simplifies HPC software for:
— Users
— Developers
— Cluster installations
— The largest HPC facilities
 Spack is central to ECP’s software strategy
— Enable software reuse for developers and users
— Allow the facilities to consume the entire ECP stack
 The roadmap is packed with new features:
— Building the ECP software distribution
— Better workflows for building containers
— Stacks for facilities
— Chains for rapid dev workflow
— Optimized binaries
— Better dependency resolution
The Spack community is growing rapidly
@spackpm
github.com/spack/spack
Visit spack.io

Disclaimer
This document was prepared as an account of work sponsored by an agency of the United States government. Neither the United
States government nor Lawrence Livermore National Security, LLC, nor any of their employees makes any warranty, expressed or
implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus,
product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific
commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or
imply its endorsement, recommendation, or favoring by the United States government or Lawrence Livermore National Security,
LLC. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States government
or Lawrence Livermore National Security, LLC, and shall not be used for advertising or product endorsement purposes.

Spack - A Package Manager for HPC

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