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Technical Details

System Component

Configuration

Compute Nodes

CPU Type

AMD EPYC 7352

Sockets

2

Cores/socket

24

Clock speed

2.3 GHz

Memory

256 GB RAM

Local Storage

512GB Micron 1300 SSD (/scratch)

Memory Bandwidth

409.6 GB/s

System

Total Compute Nodes

44

Total Compute Cores

2,112

Total Memory

11.6 TB

Total Storage

342 TB

Interconnect

Mellanox Infiniband EDR

Link bandwidth

100 Gb/s

MPI Latency

1.64 µs

Systems Software Environment

Software

Description

Operating System

CentOS Linux 7.9

Cluster Management

Scyld Clusterware 11.0

Compilers

AOCC, GCC, Clang, Go

Parallel Frameworks

Open MPI, MVAPICH2, Sandia OpenSHMEM

System Access

To use the Lotus cluster, you must first request an account on the system. Rhodes College faculty, students, and staff should submit a request using the following form for gaining access to the cluster:

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Direct SSH access is not permitted from off-campus. Users may either use a VPN to access the cluster (and then SSH in), or can login to a virtual desktop at http://desktops.rhodes.edu and use PuTTY to access the cluster. For more information on using these resources, see the Getting Started information.

Notes:

When you login to lotus.rhodes.edu you will be directed to either lotus-login01 or lotus-login02. These machines are identical in hardware and software configuration.

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Hosts which attempt to connect very frequently (many times per second) may be blocked temporarily in order to improve system security. If you are blocked, wait 15 minutes and try again.

Modules

The cluster provides the modules system for loading specific software packages and environments. Module commands can update your shell environment to automatically find optional tools, compilers, and libraries that you may need to support your application. Modules also provide a flexible mechanism for maintaining several versions of the same software or specific combinations of dependent software packages. New modules can be added upon request.

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Other useful module commands are listed below:

Command

Description

module list

List the modules that are currently loaded

module avail

List the modules that are available to be loaded

module display <module name>

Show the environment variables modified by the <module_name> module

module load <module name>

Load the module <module_name> into the environment

module unload <module name>

Remove the module <module_name> from the environment

module swap <mod1> <mod2>

Replace <mod1> with <mod2> in the environment

Job Charging and Queue Limits

Currently, the cluster is operating under a free use billing model. There are no explicit time allocations for the cluster or enforced limits on overall usage of the system. This use model is subject to change depending on how usage evolves over time.

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  • Maximum wall clock time for a single job is 48 hours

  • Jobs may request up to the max number of cores on the system (2,112)

  • Jobs may request up to the max number of nodes on the system (44)

  • Users may have at most 128 jobs queued at a time

  • Queued jobs may be preempted to support priority jobs (e.g. a paper deadline) or for emergency maintenance.

Compiling

All hosts in the cluster have access to GNU, AOCC (AMD), and Clang compilers along with multiple MPI implementations (OpenMPI and MVAPICH2). The default compiler is GCC 10.2.0, which has been compiled with AMD Rome specific optimizations (-march=znver2). GCC and AOCC compilers can be configured to generate Advanced Vector Extensions 2 (AVX2). Using AVX2, up to eight floating point operations can be executed per-cycle per-core. AVX2 is not enabled by default and is enabled by setting the appropriate compiler flags.

Using GCC

The GNU GCC compiler family can be loaded with the module system (it is loaded by default):

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To compile a program with the GNU toolchain use the following commands:

Serial

MPI

OpenMP

MPI+OpenMP

Fortran

gfortran

mpif90

gfortran -fopenmp

mpif90 -fopenmp

C

gcc

mpicc

gcc -fopenmp

mpicc -fopenmp

C++

g++

mpicxx

g++ -fopenmp

mpicxx -fopenmp

To compile your programs with AVX extensions, compile with the -march=core-avx2 compiler flag. You will probably want to use this in conjunction with normal optimization flags (i.e. -O3)

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man gcc or man g++ or man gfortran

Using AOCC (AMD compiler)

The AMD Optimizing C/C++ Compiler (AOCC) is available and can be loaded with the module system:

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To compile a program with the AMD toolchain use the following commands:

Serial

MPI

OpenMP

MPI+OpenMP

Fortran

flang

mpif90

flang -mp

mpif90 -mp

C

clang

mpicc

clang -mp

mpicc -mp

C++

clang++

mpicxx

clang++ -mp

mpicxx -mp

Running Jobs on Lotus

Running programs on the cluster is done by interacting with the job scheduling system. Lotus uses the SLURM job scheduler for managing both batch jobs and interactive runs. You should not run computationally intensive tasks on the login nodes – use the compute nodes.

If you have special needs for running jobs on the cluster, please contact the cluster support staff to help. Submitting large quantities of jobs (esp. short jobs) can impact overall scheduler response for all users.

Requesting Interactive Resources

You can request an interactive session by using the srun command.

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This command requests two full compute nodes with 48 cores each (for a total of 96 cores) for 30 minutes. When this request is granted, you will automatically be logged into the assigned node and can work normally. If you would like to run a parallel program from within the interactive job you can use srun without any options:

srun myprog

Submitting Batch Jobs

To submit a job using a batch file, create a short text file in the style of the following examples, updating where necessary to reflect your program and parameters. You can add additional SBATCH lines to send email notifications (--mail-user), etc. see man sbatch for more information. To submit your job, use the sbatch command:

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The jobfile is the file you create and contains the SLURM resource specifications and shell commands. Several examples are provided below.

MPI Job

This job runs on 2 compute nodes with 48 cores each (for a total of 96 cores), each core is assigned a single MPI rank.

Code Block
languagebash
#!/bin/bash
#SBATCH --job-name="hellompi"
#SBATCH --output="hellompi.%j.%N.out"
#SBATCH --partition=compute
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=48
#SBATCH --export=ALL
#SBATCH -t 01:30:00

srun ./hello_mpi

OpenMP Job

This job requests a single compute node and uses 48 threads for all OpenMP parallel sections. OpenMP (non-hybrid) will only work when all processes are on the same node (i.e. --nodes must be 1).

Code Block
languagebash
#!/bin/bash
#SBATCH --job-name="hello_openmp"
#SBATCH --output="hello_openmp.%j.%N.out"
#SBATCH --partition=compute
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=48
#SBATCH --export=ALL
#SBATCH -t 01:30:00

export OMP_NUM_THREADS=48
./hello_openmp

Hybrid MPI-OpenMP Job

This job requests 2 nodes and 96 total processors. This will launch 2 MPI ranks per node (total of 4 MPI processes), with each process using 24 OpenMP threads.

Code Block
languagebash
#!/bin/bash
#SBATCH --job-name="hellohybrid"
#SBATCH --output="hello_hybrid.%j.%N.out"
#SBATCH --partition=compute
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=2
#SBATCH --ntasks=4
#SBATCH --export=ALL
#SBATCH -t 01:30:00

export OMP_NUM_THREADS=24
srun --cpus-per-task=$OMP_NUM_THREADS ./hello_hybrid

SLURM No-Requeue Option

SLURM will requeue jobs if there is a node failure of if your job is preempted. In some cases, this may cause input or output files to be overwritten that should be preserved. You may request that your job not be automatically re-queued by adding the following line to your batch file:

Code Block
languagebash
#SBATCH --no-requeue

Monitoring Job Status

Users can monitor their jobs using the squeue command.

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[user1@lotus-login01]$ scancel <jobid>

Storage Considerations

Lotus has a single storage server with a total of 504TB of disk in a ZFS RAID-Z2 filesystem. This filesystem is the primary storage location for all data on the cluster. Programs that perform a lot of file I/O operations in parallel may have poor performance with this storage design. Lotus does not have any storage that uses a parallel filesystem.

Each compute node has access to 512GB of local SSD storage which can be used for check-pointing and programs that will benefit from local fast storage. The latency for local SSD access is several orders of magnitude lower than accessing the shared network filesystem. Users may use the /scratch filesystem on each compute node for temporary storage. Scratch storage space will be reclaimed after your job completes.

Software

Users may request that new software packages be added to the cluster if they may benefit multiple users or research groups. If you would like specific software installed, please contact the research computing support staff.