Overview
ParaTools Pro for E4S™ - the Extreme-scale Scientific Software Stack, E4S™ hardened for commercial clouds and supported by ParaTools, Inc. provides a platform for developing and deploying HPC and AI/ML applications. It features a performant remote desktop environment (based on DCV) on the login node and compute nodes interconnected by a low-latency, high bandwidth network adapter based on AWS Elastic Fabric Adapter (EFA). ParaTools Pro for E4S™ features a suite of over 100 HPC tools built using the Spack package manager and a proprietary MVAPICH MPI tuned for EFA. It features ready to use HPC applications (such as World Research and Forecasting Model-WRF, LAMMPS, Xyce, CP2K, deal.II, GROMACS, Quantum Espresso) as well as AI/ML tools based on Python (such as TensorFlow, PyTorch, JAX, Horovod, Keras, OpenCV, matplotlib and supports Jupyter notebooks) and the Codium IDE. New packages can be easily installed using Spack and pip and are accessible on the cluster compute and login nodes. It may be used for developing the next generation of generative AI applications using a suite of Python tools and interfaces.
E4S™ has built a unified computing environment for deployment of open-source projects. E4S™ was originally developed to provide a common software environment for the exascale leadership computing systems currently being deployed at DOE National Laboratories across the U.S. Support for ParaTools Pro for E4S™ is available through ParaTools, Inc. This product has additional charges associated with it for optional product support and updates.
This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Office of Advanced Scientific Computing and Research (ASCR), under SBIR Award Number DE-SC0022502 ("E4S: Extreme-Scale Scientific Software Stack for Commercial Clouds").
Note: This product contains repackaged and tuned open source software (e.g., E4S™, Spack and AI/ML tools like Horovod, JAX, Keras etc.) which is configured and linked against a proprietary MVAPICH MPI implementation tuned for EFA.
The full list of E4S applications installed via Spack is as follows:
- adios2
- adios
- alquimia
- aml
- amrex
- arborx
- argobots
- ascent
- axom
- boost
- bricks
- butterflypack
- cabana
- caliper
- chai
- chapel
- charliecloud
- conduit
- cp2k
- cusz
- darshan-runtime
- darshan-util
- datatransferkit
- dealii
- dyninst
- e4s-alc
- e4s-cl
- ecp-data-vis-sdk
- exago
- exaworks
- faodel
- fftx
- flecsi
- flit
- flux-core
- fortrilinos
- fpm
- gasnet
- ginkgo
- globalarrays
- glvis
- gmp
- gotcha
- gptune
- gromacs
- h5bench
- hdf5-vol-async
- hdf5-vol-cache
- hdf5-vol-log
- hdf5
- heffte
- hpctoolkit
- hpx
- hypre
- kokkos-kernels
- kokkos
- laghos
- lammps
- lbann
- legion
- libcatalyst
- libnrm
- libquo
- libunwind
- loki
- mercury
- metall
- mfem
- mgard
- mpark-variant
- mpifileutils
- nccmp
- nco
- nek5000
- nekbone
- netcdf-fortran
- netlib-scalapack
- nrm
- nwchem
- omega-h
- openmpi
- papi
- papyrus
- parallel-netcdf
- paraview
- parsec
- pdt
- petsc
- phist
- plasma
- plumed
- precice
- pruners-ninja
- pumi
- py-cinemasci
- py-h5py
- py-jupyterhub
- py-libensemble
- py-petsc4py
- qthreads
- quantum-espresso
- raja
- rempi
- scr
- slate
- slepc
- stc
- strumpack
- sundials
- superlu-dist
- superlu
- swig
- sz3
- sz
- tasmanian
- tau
- trilinos
- turbine
- umap
- umpire
- unifyfs
- upcxx
- veloc
- visit
- vtk-m
- wannier90
- wps
- wrf
- xyce
- zfp
Highlights
- ParaTools Pro for E4S™ and Machine Learning stacks built and optimized for AWS EFA and AWS Parallel Computing Service (PCS)
- A proprietary MVAPICH MPI implementation that offers lower latency and higher throughput than default OpenMPI implementations for pre-installed applications and user installed applications
- HPC software environment managed using the Spack package manager
Details
Features and programs
Financing for AWS Marketplace purchases
Pricing
Free trial
- ...
Dimension | Cost/hour |
---|---|
c6gn.16xlarge Recommended | $0.99 |
is4gen.xlarge | $0.99 |
m7gd.12xlarge | $0.99 |
m6g.large | $0.99 |
m6gd.xlarge | $0.99 |
r8g.metal-48xl | $0.99 |
r7g.4xlarge | $0.99 |
x8g.12xlarge | $0.99 |
r7gd.4xlarge | $0.99 |
hpc7g.16xlarge | $0.99 |
Vendor refund policy
Refund Policy: The standard refund policy outlined here will be followed: https://docs.aws.amazon.com/marketplace/latest/userguide/refunds.html
Any additional refund enquiries can be sent to support@paratools.com and will be considered individually on a case by case basis.
How can we make this page better?
Legal
Vendor terms and conditions
Content disclaimer
Delivery details
64-bit (Arm) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
- AWS PCS: 1.1.1
- E4S release: 24.11
- Inter-node communication requirement: EFA compatible instances
- Default MPI: mvapich-plus-4
- OS: Ubuntu 22.04
IMPORTANT:
- The version of MVAPICH installed requires compute node instances to have EFA support for inter-node communication.
If you do not plan to run multi-node MPI jobs you can safely disregard this. The head node may be a non-EFA instance type, but the compute nodes MUST be EFA capable. To get a list of instance types that are EFA compatible you can use the AWS CLI:
aws ec2 describe-instance-types --filters Name=network-info.efa-supported,Values=true \
--query 'InstanceTypes[*].[InstanceType]' \
--output text | sort
Updates in this version:
- Ported AWS Parallel Compute Service (PCS) image to arm64 architecture
ParaTools Pro for E4S (TM) provides a turn-key cloud HPC solution with the E4S Software stack and machine learning tools and SDKs installed and optimized to run on AWS. The key software features and versions installed are:
- Ubuntu 22.04, ARM64
- DCV remote access installed
- Codium IDE
- E4S 24.11 CPU applications installed
- World Research and Forecasting Model (WRF)
- MVAPICH Plus 4.0 with support for AWS EFA
- Python/Machine learning tools, SDKs, Frameworks etc.
- TensorFlow, PyTorch, JAX, Horovod, Keras, OpenCV, OpenAI, Scikit-Learn
- numpy, scipy, matplotlib, pandas, opencv-python
- jupyter notebook
- pywaggle
The full list of E4S applications installed via Spack is as follows:
-- linux-ubuntu22.04-aarch64 / gcc 11.4.0 --------------
- adios2 2.7.1
- adios 1.13.1
- alquimia 1.1.0
- aml 0.2.1
- amrex 24.10
- arborx 1.7
- argobots 1.2
- ascent 0.9.3
- axom 0.9.0
- boost 1.79.0
- bricks 2023.08.25
- butterflypack 2.4.0
- cabana 0.7.0
- caliper 2.11.0
- chai 2024.07.0
- chapel 2.2.0
- charliecloud 0.38
- conduit 0.9.2
- cp2k 2024.3
- cusz 0.6.0
- darshan-runtime 3.4.5
- darshan-util 3.4.5
- datatransferkit 3.1.1
- dealii 9.5.1
- dyninst 13.0.0
- e4s-alc 1.0.2
- e4s-cl 1.0.4
- ecp-data-vis-sdk 1.0
- exago 1.6.0
- exaworks 0.1.0
- faodel 1.2108.1
- fftx 1.2.0
- flecsi 2.3.0
- flit 2.1.0
- flux-core 0.66.0
- fortrilinos 2.3.0
- fpm 0.10.0
- gasnet 2024.5.0
- ginkgo 1.8.0
- globalarrays 5.8.2
- glvis 4.2
- gmp 6.3.0
- gotcha 1.0.7
- gptune 4.0.0
- gromacs 2024.3
- h5bench 1.4
- hdf5-vol-async 1.7
- hdf5-vol-cache v1.1
- hdf5-vol-log 1.4.0
- hdf5 1.12.3
- heffte 2.4.0
- hpctoolkit 2024.01.1
- hpx 1.10.0
- hypre 2.32.0
- kokkos-kernels 4.4.01
- kokkos 4.4.01
- laghos 3.1
- lammps 20240829
- lbann 0.104
- legion 24.09.0
- libcatalyst 2.0.0
- libnrm 0.1.0
- libquo 1.4
- libunwind 1.6.2
- loki 0.1.7
- mercury 2.3.1
- metall 0.28
- mfem 4.7.0
- mgard 2023-12-09
- mpark-variant 1.4.0
- mpifileutils 0.11.1
- nccmp 1.9.1.0
- nco 5.2.4
- nek5000 19.0
- nekbone 17.0
- netcdf-fortran 4.6.1
- netlib-scalapack 2.2.0
- nrm 0.1.0
- nwchem 7.2.3
- omega-h 9.34.13
- openmpi 5.0.5
- papi 7.1.0
- papyrus 1.0.2
- parallel-netcdf 1.12.3
- paraview 5.13.1
- parsec 3.0.2209
- pdt 3.25.2
- petsc 3.22.0
- phist 1.12.1
- plasma 24.8.7
- plumed 2.9.2
- precice 3.1.2
- pruners-ninja 1.0.1
- pumi 2.2.8
- py-cinemasci 1.3
- py-h5py 3.11.0
- py-jupyterhub 1.4.1
- py-libensemble 1.4.2
- py-petsc4py 3.22.0
- qthreads 1.18
- quantum-espresso 7.3.1
- raja 2024.07.0
- rempi 1.1.0
- scr 3.0.1
- slate 2024.05.31
- slepc 3.22.0
- stc 0.9.0
- strumpack 7.2.0
- sundials 7.1.1
- superlu-dist 8.2.1
- superlu 5.3.0
- swig 4.0.2-fortran
- sz3 3.2.0
- sz 2.1.12.5
- tasmanian 8.0
- tau 2.34
- trilinos 16.0.0
- turbine 1.3.0
- umap 2.1.1
- umpire 2024.07.0
- unifyfs 2.0
- upcxx 2023.9.0
- veloc 1.7
- visit 3.3.3
- vtk-m 2.2.0
- wannier90 3.1.0
- wps 4.5
- wrf 4.5.2
- xyce 7.8.0
- zfp 0.5.5
- zfp 1.0.0
Additional details
Usage instructions
The 1-Click Security Group opens port 22 only so that you can access your instance via SSH using login 'ubuntu', you may change this later.
For software development and basic usage:
- Launch the ParaTools Pro for E4S (TM) AMI via 1-Click
- On the 'EC2 Launch an Instance' page pick the key pair you will use to login
- On the 'EC2 Launch an Instance' page optionally edit the network settings by pressing the edit button. Adjust the firewall rules if needed to ensure ssh access and enable Auto-assign public IP if you plan to access the instance remotely from a non-AWS IP address.
- Click 'Launch Instance'
- Find your running instance in the EC2 Instances section of the EC2 dashboard, and connect to the instance via SSH using the username ubuntu and the key pair you previously selected by picking the instance and pressing the connect button.
For more advanced usage, including launching an ParaTools Pro for E4S (TM) cluster with AWS Parallel Computing Service, please see:
Resources
Support
Vendor support
For general support questions, please email support@paratools.com
Paid support contracts and custom AMIs and computing environments are available. Please see https://paratoolspro.com/ for additional details.
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.