Using Chapel on Amazon Web Services

This page contains information on how to use AWS ParallelCluster to run Chapel in the cloud. ParallelCluster is a commandline tool that helps you create and manage High Performance Computing (HPC) clusters in the AWS cloud. It uses a simple configuration file to create a cluster that can be customized to your needs. The following steps will guide you through the process of setting up a cluster and running Chapel programs on it.

Before getting started, you will need an AWS account, which can be created here: https://aws.amazon.com/

This guide assumes you have the ParallelCluster CLI installed and configured. If you do not, follow the steps here.

Note

This document was last updated for ParallelCluster v3.8.0. Other versions may not have the same features and adjustments may be necessary.

Configuring a ParallelCluster

ParallelCluster uses a configuration file to define the cluster. Running the pcluster configure command walks through the process of creating a configuration file for a cluster. The command requires the flag -c CONFIG_NAME, where CONFIG_NAME is the path to the YAML file where the generated config file will go. Listed below are a few key options to consider when configuring a cluster for Chapel.

  • EC2 Key Pair Name

    Make sure to have an EC2 key pair created in the same region you are creating the cluster. This key pair will be used to access the instances in the cluster.

    Note

    If you plan to use ubuntu, make sure the key pair type is ED25519.

  • Scheduler

    The default scheduler is slurm. AWS Batch is also available, but not currently supported by Chapel.

  • Operating System

    The default operating system is alinux2. We recommend using either alinux2 or ubuntu2204.

    Note

    The default AMI for alinux2 does not have the necessary drivers for GPUs. If you plan to use GPUs, we recommend using ubuntu2204.

    Note

    Amazon Linux 2023 is preferred, but is not yet available in ParallelCluster. If a later version of ParallelCluster includes Amazon Linux 2023, we recommend using it.

  • Head node instance type

    The default instance type is t2.micro. This is the node that will be compiling Chapel programs. We recommend using an instance type with more memory, such as t2.medium or m5.large.

  • Number of queues

    This is number of slurm queues that will be created. The default is 1 and most users should not need to change this. After selecting the number of queues, you will be prompted to enter the name of each queue.

  • Number of compute resources for queue

    This is the number of different types of nodes that will be created for this queue. The default is 1 and most users should not need to change this.

  • Compute instance type for compute resource in queue

    This is the type of instance that will be created for this queue. It is recommended to use the same architecture as the head node instance type (i.e. don’t use an x86 head node and an ARM compute node).

  • Maximum instance count

    This is the maximum number of nodes that will be created for this queue. It also determines the maximum number of locales that can be used in a Chapel program. Four compute nodes is the minimum needed to successfully run make check.

  • Automate VPC creation? and Automate Subnet creation?

    ParallelCluster runs in a Virtual Private Network (VPC). If you do not have a VPC created, you can have ParallelCluster create one for you. If you already have a VPC, you can choose to use it. ParallelCluster can also configure the head node and compute nodes in several subnet configurations. Chapel works well with many of these configurations, but we recommend using a public subnet for the head node and a private subnet for the compute nodes.

Following pcluster configure, the generated configuration file will look something like this:

Region: us-west-1
Image:
  Os: alinux2
HeadNode:
  InstanceType: t2.medium
  Networking:
    SubnetId: SUBNETID
  Ssh:
    KeyName: KEYNAME
Scheduling:
  Scheduler: slurm
  SlurmQueues:
  - Name: queue1
    ComputeResources:
    - Name: c5n18xlarge
      Instances:
      - InstanceType: c5n.18xlarge
      MinCount: 0
      MaxCount: 4
    Networking:
      SubnetIds:
      - SUBNETID

Note

ParallelCluster will use whatever the default region is for the AWS CLI. If that is not set or not the desired region, you can set the region at the command line as pcluster configure -r REGION. Note that all cluster resources will be created in the region specified in the configuration file, and that other resources (such as the key pair or additional volumes) must be in the same region.

Performance Notes

For best performance, we recommend the following:

  • Use a placement group for the compute nodes.

    This will reduce the latency between the nodes and improve performance. This requires using an instance type that supports cluster placement, such as c5n.18xlarge.

  • Set MinCount to some non-zero value.

    This will create the compute nodes when the cluster is created, rather than waiting for them to be created when the first job is submitted. Using a MinCount of 0 results in significant overhead when running programs. For best performance, we recommend setting MinCount to the same value as MaxCount, however this will result in AWS charges for the compute nodes even when they are not being used.

  • Enable EFA (Elastic Fabric Adapter) for the compute nodes.

    EFA is a network interface for HPC applications that require low-latency and high-bandwidth communications between nodes. This requires using an instance type that supports EFA, such as c5n.18xlarge.

These additional options can be added to the configuration file:

Region: us-west-1
Image:
  Os: alinux2
HeadNode:
  InstanceType: t2.medium
  Networking:
    SubnetId: SUBNETID
  Ssh:
    KeyName: KEYNAME
Scheduling:
  Scheduler: slurm
  SlurmQueues:
  - Name: queue1
    ComputeResources:
    - Name: c5n18xlarge
      Instances:
      - InstanceType: c5n.18xlarge
      MinCount: 4
      MaxCount: 4
      Efa:
        Enabled: true
    Networking:
      PlacementGroup:
        Enabled: true
      SubnetIds:
      - SUBNETID

It also possible to use instances with GPUs. We recommend using G4dn, G5, P3, or P4 instances. We also recommend not using alinux2 with these instances, as it does not have the necessary drivers for the GPUs. Instead, use ubuntu2204.

Launching and Connecting

To launch the cluster, run the following command:

pcluster create-cluster -c CONFIG_NAME -n mycluster

This will start the process of allocating the AWS resources required. To check the process of the cluster creation, run the following command:

pcluster describe-cluster -n mycluster

This will report various details about the cluster, including the status of the cluster. Once the cluster is in the CREATE_COMPLETE state, you can access the head node. To query just the status of the cluster, use pcluster describe-cluster -n mycluster --query clusterStatus.

Connecting to the head node depends on how the VPC was set up. If the head node exists in a public subnet, you can connect to it using the public IP address. If the head node exists in a private subnet, you will need to connect to it using the AWS session manager.

  • Connecting via a public subnet:

    ssh -i /path/to/key.pem ec2-user@`pcluster describe-cluster -n mycluster --query headNode.publicIpAddress | tr -d '"'`

    Note

    The username may be different depending on the AMI used. The default username for Amazon Linux 2 is ec2-user. The default username for Ubuntu 22.04 is ubuntu.

    Note

    key.pem is the private key that corresponds to the public key used when creating the EC2 key pair, specified in the configuration file.

  • Connecting via the AWS session manager:

    Query the instance ID of the head node:

    pcluster describe-cluster -n mycluster --query headNode.instanceId

    Open the AWS console and navigate to the EC2 Instances view. Select the head node instance (with an ID matching the one queried above) and click the “Connect” button. This will open a new window with a list of connection options. Select “Session Manager” and click the “Connect” button. This will open a new window with a terminal that is connected to the head node. After connecting to the node, run sudo su ec2-user to switch to the default user (for Ubuntu, use sudo su ubuntu). Then run cd to go to the home directory.

Building Chapel

Once connected to the instance via ssh, do the following:

  • Install the dependencies as shown on the Installation page.

    If using a GPU instance, install the CUDA toolkit from the NVIDIA website.

  • Download a Chapel release from the Download page.

  • Build the Chapel release with CHPL_COMM=ofi as shown on the Building Chapel page.

    For best results, we recommend running the following prior to building Chapel. Users may wish to add this to their .bashrc:

    # this path may need to be adjusted, depending on where the Chapel release was downloaded
. ~/chapel/util/setchplenv.bash

export CHPL_COMM=ofi
# if using a cluster without EFA, use FI_PROVIDER=tcp instead
export FI_PROVIDER=efa

export CHPL_LAUNCHER=slurm-srun
export CHPL_LIBFABRIC=system
export PKG_CONFIG_PATH=/opt/amazon/efa/lib64/pkgconfig/
export CHPL_COMM_OFI_OOB=pmi2
PMI2_DIR=/opt/slurm/lib/
export CHPL_LD_FLAGS="-L$PMI2_DIR -Wl,-rpath,$PMI2_DIR"
export SLURM_MPI_TYPE=pmi2
export CHPL_RT_COMM_OFI_DEDICATED_AMH_CORES=true
export CHPL_RT_COMM_OFI_CONNECT_EAGERLY=true

# Set this based on the max amount of memory available per-instance
# Note that EFA currently prevents the use of a heap larger than 96G
export CHPL_RT_MAX_HEAP_SIZE=75%

    For best performance, users should also set export FI_EFA_USE_DEVICE_RDMA=1. This enables higher network performance by using the RDMA capabilities of EFA, but it is only available on newer instances. If you are unsure if your instance supports this, try setting it and running a Chapel program. If the program fails with an error about FI_EFA_USE_DEVICE_RDMA, then your instance does not support this feature.

    If using a GPU instance, use the following in addition to the above:

    export CHPL_LOCALE_MODEL=gpu
export CHPL_LLVM=bundled
export CHPL_GPU=nvidia

Running Chapel Programs

If all of the above steps have been completed successfully, you should be able to use your cluster to run Chapel programs. If you have a cluster with 4 or more compute nodes, you can run make check from CHPL_HOME to test the Chapel installation. If you have a cluster with less than 4 nodes, you can test your configuration compile and run the hello program as shown below:

chpl ~/chapel/examples/hello.chpl
./hello -nl 2

Cleanup

When you are done with the cluster, you can delete it with the following command:

pcluster delete-cluster -n mycluster

This will delete all of the resources associated with the cluster, including the storage. If you have data on the cluster that you want to keep, you should back it up before deleting the cluster.

Note

If desired, users can create their own storage volumes and attach them to the cluster at configure time. For example, users can add the following to their configuration file prior to running pcluster create-cluster:

SharedStorage:
  - MountDir: /scratch
    Name: scratch
    StorageType: Ebs
    EbsSettings:
      VolumeId: VOLUMEID

Replace VOLUMEID with the ID of the volume you want to attach. After the cluster is created, the volume will be mounted at /scratch on both the head node and the compute nodes. Users can then use the volume as they see fit. When the cluster is deleted, the volume will be detached but not deleted. Make sure when creating the volume that it is in the same region as the cluster.

For more information on attaching volumes to a cluster, see the ParallelCluster documentation.

Frequently Asked Questions

How do I resolve the following error: virtual memory exhausted: Cannot allocate memory

This is a common error on systems with limited memory resources, such as the free tier of EC2 instances. If you do not wish to launch an instance with more memory resources, you can create a swap file or swap partition.

This can be done on Linux distributions with the following steps:

# Log in as root
sudo -s

# Create a 512MB swap file (1024 * 512MB = 524288 block size)
dd if=/dev/zero of=/swapfile1 bs=1024 count=524288

# Secure swap file
chown root:root /swapfile1
chmod 0600 /swapfile1

# Set up linux swap file
mkswap /swapfile1

# Enable swap file
swapon /swapfile1

Then edit /etc/fstab to include:

/swapfile1 none swap sw 0 0

Enable the new swapfile without rebooting:

swapoff -a
swapon -a

Confirm the swapfile is working:

free -m