WRF on the Cloud

Objectives

LADCO is seeking to understand the best practices for submitting and managing multiprocessor computing jobs on a cloud computing platform. In particular, LADCO would like to develop a WRF production environment that utilizes cloud-based computing. The goal of this project is to prototype a WRF production environment on a public, on-demand high performance computing service in the cloud to create a WRF platform-as-a-service (PaaS) solution. The WRF PaaS must meet the following objectives:

• Configurable computing and storage to scale, as needed, to meet that needs of different WRF applications
• Configurable WRF options to enable changing grids, simulation periods, physics options, and input data
• Flexible cloud deployment from a command line interface to initiate computing clusters and spawn WRF jobs in the cloud

Call Notes

November 28, 2018

WRF Benchmarking

• Emulating WRF 2016 12/4/1.3 grids
• Purpose for estimating costing for CPUs, RAM and Storage
• CPU: 8 Cores: 5.5 day run = 4 days; 24 Cores: 3 days
• RAM: ~22 Gb RAM/run (2.5 Gb/core)
• Storage
  • test netCDF4 and netCDF with no compression
  • with compression saves a lot of space (1/3 of the output) relative to uncompressed NCF (~70% compression)
  • need to link in the HDF and NC4 libraries with compression to downstream programs
  • estimate about 5.8 Tb for the year, goes to 16.9 Tb without compression

Conceptual Approach to WRF on the Cloud

• Cluster management would launch a head node and compute nodes
• 77 5.5 day chunks, 20 computers for 16 days (or 80 computers for 4 days)
• Head node running constantly
• Compute nodes running over the length of project
• Memory optimized machines performed better than compute optimized for CAMx

Cost Analysis

• Analysis Spreadsheet

Storage Analysis

• AWS
  • Don't want to use local because it will need to be moved/migrated
  • Put the data on a storage appliance (S3) while running, and then push off to longer term storage (Glacier)
  • Glacier is archived and need to submit access through the console, response times listed as 1-5 minutes
• Azure
  • Fast and slower lake storage for offline
  • Managed disks for online

Data Transfer Analyis

• estimate based on 5.8 Gb
• AWS
  • Internet transfer will cost ~ $928 for 5.5 Gb
  • Snowball 10 days to get data off a disk, costs $200 for entire WRF run (smallest was 50 Tb)
• Azure
  • Online transfer
  • Databox option (like snowball)

Cluster Management Tools (interface analysis)

• 3-4 seemed to work best across several cloud solutions
• Alsys Flight (works on AWS and Azure), used to bring up 40 nodes; set up a Tor queuing system; trouble with using an AMI, need to pay for an AMI with this solution; can use Docker if we want to use containers, but Ramboll not positioned to use containers for this project
• CFN: slower development, but now has an AWS parallel cluster (CFN reincarnated), improved tools and built in the Python package index (can be installed with PIP); let's you spin everything up from the command line and could be scripted
• Haven't yet explored AWS Parallel Cluster/CFN in detail; similar to experience with Star Cluster; seems to be the best solution because you can use your own custom AMI; instance types are independent of the cluster management tools

Next Steps

• LADCO to create a WRF AMI on AWS: WRF 3.9.1, netCDF4 with compression, MPICH2, PGI compiler, AMET
• LADCO to create a login for Ramboll in our AWS organization
• Ramboll to explore AWS Parallel cluster and then prototype with LADCO WRF AMI
• Next call 12/5 @ 3 Central

Recommendations

WRF

• Use netCDF4 with compression
• Use 8 cores per 5.5-day segment and submit all segments for annual run to cluster at once

Cloud Service

• Costs are equivalent between Azure and AWS so use AWS because of familiarity
• Use one memory optimized instance (EC2-r5.2xlarge, 8 cores, 64 GB RAM) for each segment
• Use Standard S3 storage for the lifetime of the project and migrate to Infrequent S3 or Glacier for longterm storage
• Use Snowball to transfer completed project to local site

HPC Platforms

• Use AWS ParallelCluster (formerly CfnCluster)
  • Provides CLI-interface, allowing for linux-script automation
  • Allows for custom AMIs
  • Provides a variety of schedulers: sge, torque, slurm, or awsbatch
  • Is actively being developed and enhanced
  • Additional investigation/test of WRF/CAMx test cases needed to verify tool integrity and performance
• Other HPC have demonstrated issues
  • StarCluster: Problematic auto-scaling; outdated and inactive
  • AlcesFlight: Fee-based ability to use custom AMIs, problems with auto-scaling for large instance counts

WRF on the AWS: User's Guide

How to configure/optimize AWS for running WRF.

Summary

• AWS pcluster v2.1.0 instance using ALinux
• WRFv3.9.1 compiled with netCDF4 (compression)
• PGI compiler 2018 with OpenMPI3.1.3
• NetCDF C 4.6.2, Fortran 4.2

Cluster Configuration

ondemand pcluster config

[aws] aws_region_name = us-east-2

[cluster ladcowrf] vpc_settings = public ebs_settings = ladcowrf scheduler = sge master_instance_type = m4.large compute_instance_type = m5a.4xlarge placement = cluster placement_group = DYNAMIC master_root_volume_size = 40 cluster_type = ondemand base_os = alinux key_name = ***** min_vcpus = 0 max_vcpus = 64 desired_vcpus = 0

1. Base AMI for pcluster v2.1.0

custom_ami = ami-0381cb7486cdc973f

[ebs ladcowrf] shared_dir = data volume_type = gp2 volume_size = 1500 volume_iops = 1500 encrypted = false

[vpc public] master_subnet_id = subnet-****** vpc_id = vpc-******

[global] update_check = true sanity_check = true cluster_template = ladcowrf

[aliases] ssh = ssh -Y {CFN_USER}@{MASTER_IP} {ARGS}