Skip Nav

IBM Power9 (SCOUT)
User Guide

Table of Contents

1. Introduction

1.1. Document Scope and Assumptions

This document provides an overview and introduction to the use of the IBM Power9 (SCOUT) located at the ARL DSRC, along with a description of the specific computing environment on SCOUT. The intent of this guide is to provide information that will enable the average user to perform computational tasks on the system. To receive the most benefit from the information provided here, you should be proficient in the following areas:

  • Use of the UNIX operating system
  • Use of an editor (e.g., vi or emacs)
  • Remote usage of computer systems via network or modem access
  • A selected programming language and its related tools and libraries

1.2. Policies to Review

Users are expected to be aware of the following policies for working on SCOUT.

1.2.1. Login Node Abuse Policy

Memory or CPU intensive programs running on the login nodes can significantly affect all users of the system. Therefore, only small applications requiring a minimal amount of runtime and memory are allowed on the login nodes. Any job running on the login nodes that affects their overall interactive performance may be unilaterally terminated.

1.2.2. Workspace Purge Policy

The /work1 directory is subject to a 21-day purge policy. A system "scrubber" monitors scratch space utilization, and if available space becomes low, files not accessed within 21 days are subject to removal, although files may remain longer if the space permits. There are no exceptions to this policy.

Note! If it is determined as part of the normal purge cycle that files in your $WORKDIR directory must be deleted, you WILL NOT be notified prior to deletion. You are responsible to monitor your workspace to prevent data loss.

1.3. Obtaining an Account

The process of getting an account on the HPC systems at any of the DSRCs begins with getting an account on the HPCMP Portal to the Information Environment, commonly called a "pIE User Account." If you do not yet have a pIE User Account, please visit HPC Centers: Obtaining An Account and follow the instructions there. Once you have an active pIE User Account, visit the ARL accounts page for instructions on how to request accounts on the ARL DSRC HPC systems. If you need assistance with any part of this process, please contact the HPC Help Desk at

1.4. Requesting Assistance

The HPC Help Desk is available to help users with unclassified problems, issues, or questions. Analysts are on duty 8:00 a.m. - 8:00 p.m. Eastern, Monday - Friday (excluding Federal holidays).

You can contact the ARL DSRC directly in any of the following ways for support services not provided by the HPC Help Desk:

For more detailed contact information, please see our Contact Page.

2. System Configuration

2.1. System Summary

SCOUT is an IBM Power9. The login nodes are populated with two IBM Power9 processors. SCOUT uses the Expanded Data Rate InfiniBand interconnect in a Non-Blocking Fat Tree configuration as its high-speed network for MPI messages and IO traffic. SCOUT has 22 training nodes each with 2 Power9 processors, 512GB memory and 6 nVidia V100 GPUs, 128 inference nodes with 2 Power9 processors, 256GB memory and 4 nVidia T4 GPUs, 2 vis nodes each with 2 Power9 processors, 512GB memory and 2 nVidia V100 GPUs (SRD not yet available).

SCOUT is intended to be used as a batch-scheduled HPC system. Its login nodes are not to be used for large computational (memory, IO, long executions) work. All executions that require large amounts of system resources must be sent to the training or inference nodes by batch job submission.

Node Configuration
Login Nodes Compute Nodes
Training Inference Visualization
Total Cores | Nodes 160 | 4 880 | 22 5,120 | 128 80 | 2
Operating System RHEL
Cores/Node 40 40 + 6 GPUs
(6 x 5,120 CUDA cores,
6 x 640 Tensor cores)
40 + 4 GPUs
(4 x 2,500 CUDA cores,
4 x 320 Tensor cores)
40 + 2 GPUs
(2 x 5,120 CUDA cores,
2 x 640 Tensor cores)
Core Type IBM Power9 IBM Power9
+6 NVIDIA Volta V100
IBM Power9
IBM Power9
+2 NVIDIA Volta V100
Core Speed 2.55 GHz
Memory/Node 512 GBytes 700 GBytes 256 GBytes 512 GBytes
Accessible Memory/Node 502 GBytes 690 GBytes 246 GBytes 502 GBytes
Memory Model Shared on node. Shared on node.
Distributed across cluster.
Interconnect Type Ethernet / InfiniBand

File Systems on SCOUT
Path Capacity Type
155 TBytesGPFS
1.045 PBytesGPFS
90 TBytesGPFS

2.2. Processors

SCOUT uses 2.6-GHz Power9 processors on its nodes. There are 2 processors per node, each with 20 cores, for a total of 40 cores per node. In addition, these processors have a last level cache of 110 MBytes.

2.3. Memory

SCOUT uses both shared and distributed memory models. Memory is shared among all the cores on a node, but is not shared among the nodes across the cluster.

Each login node contains 512 GBytes of main memory. All memory and cores on the node are shared among all users who are logged in. Therefore, users should not use excessive amounts of memory at any one time.

Each of the 22 training compute nodes has 6 nVidia V100 GPUs w 32 GB memory and 512 GBytes of user-accessible shared memory. Each of the 128 inference nodes has 4 nVidia T4 GPUs w 16GB memory and 256 GBytes of user-accessible shared memory. Each of the 2 vis nodes has 1 nVidia V100 GPU w 32 GB of Memory and contains 512 GBytes of user-accessible shared memory.

2.4. Operating System

The operating system on SCOUT is RedHat Linux. The operating system supports 7.6 alt software.

2.5. File Systems

SCOUT has the following file systems available for user storage:

2.5.1. /p/home

This file system is locally mounted from SCOUT's GPFS file system. It has a formatted capacity of 155 TBytes. All users have a home directory located on this file system which can be referenced by the environment variable $HOME.

2.5.2. /p/work1

This directory comprises SCOUT's scratch file area and is a locally mounted GPFS file system. /p/work1 has a formatted capacity of 1045 TBytes. All users have a work directory located on /p/work1 which can be referenced by the environment variable $WORKDIR.

2.5.3. /p/app

All center-managed COTS packages are stored in /p/app. This file system is locally mounted from SCOUT's GPFS file system. It has a formatted capacity of 90 TBytes and can be referenced by the environment variable $CSI_HOME. In addition, users may request space in this area under /p/app/unsupported to store user-managed software packages that they wish to make available to other owner-designated users. This area can be referenced by the environment variable $PROJECTS_HOME. To have space allocated in /p/app/unsupported users should submit a request to the ARL DSRC Help Desk. Send e-mail to or call 1-800-ARL-1552 (1-800-275-1552) or (410) 278-1700.

2.5.4. /archive

This NFS-mounted file system is accessible from the login nodes on SCOUT. Files in this file system are subject to migration to tape and access may be slower due to the overhead of retrieving files from tape. It has a formatted capacity of 16 TBytes with a petascale archival tape storage system. The disk portion of the file system is automatically backed up. Users should migrate all large input and output files to this area for long-term storage. Users should also migrate all important smaller files from their home directory area in /p/home to this area for long-term storage. All users have a directory located on this file system which can be referenced by the environment variable $ARCHIVE_HOME.

2.5.5. /tmp or /var/tmp

Never use /tmp or /var/tmp for temporary storage! These directories are not intended for temporary storage of user data, and abuse of these directories could adversely affect the entire system.

2.5.6. /p/cwfs

This path is directed to the Center-Wide File System (CWFS) which is meant for short-term storage (no longer than 120 days). All users have a directory defined in this file system. The environment variable for this is $CENTER. This is accessible from the unclassified HPC system login nodes. The CWFS has a formatted capacity of 2.4 PBytes and is managed by IBM’s Spectrum Scale (formerly GPFS)..

3. Accessing the System

3.1. Kerberos

A Kerberos client kit must be installed on your desktop system to enable you to get a Kerberos ticket. Kerberos is a network authentication tool that provides secure communication by using secret cryptographic keys. Only users with a valid HPCMP Kerberos authentication can gain access to SCOUT. More information about installing Kerberos clients on your desktop can be found at HPC Centers: Kerberos & Authentication.

3.2. Logging In

The system host name for the SCOUT cluster is, which will redirect the user to one of twenty-four login nodes. Hostnames and IP addresses to these nodes are available upon request from the HPC Help Desk.

The preferred way to login to SCOUT is via ssh, as follows:

% ssh

3.3. File Transfers

File transfers to ARL DSRC systems (except for those to the local archive server) must be performed using the following tools: scp, mpscp, ftp, and sftp.

Windows users may use a graphical file transfer protocol (ftp) client such as FileZilla.

4. User Environment

4.1. User Directories

4.1.1. Home Directory

When you log on to SCOUT, you will be placed in your home directory, /p/home/username. The environment variable $HOME is automatically set for you and refers to this directory. $HOME is visible to both the login and compute nodes, and may be used to store small user files, but it has limited capacity and is not backed up on a daily basis and therefore should not be used for long-term storage.

4.1.2. Work Directory

The path for your working directory on SCOUT's scratch file system is /p/work1/username. The environment variable $WORKDIR is automatically set for you and refers to this directory. $WORKDIR is visible to both the login and compute nodes, and should be used for temporary storage of active data related to your batch jobs.

Note: Although the $WORKDIR environment variable is automatically set for you, the directory itself is not created. You can create your $WORKDIR directory as follows:

mkdir $WORKDIR

The scratch file system provides 1.2 PBytes of formatted disk space. This space is not backed up, however, and is subject to a purge policy.

REMEMBER: This file system is considered volatile working space. You are responsible for archiving any data you wish to preserve. To prevent your data from being "scrubbed," you should copy files that you want to keep into your /home directory (see below) for long-term storage.

4.1.3. Archive Directory

In addition to $HOME and $WORKDIR, each user is also given a directory on the /archive file system. This file system is visible to the login nodes (not the compute nodes) and is the preferred location for long-term file storage. All users have an area defined in /archive for their use. This area can be accessed using the $ARCHIVE_HOME environment variable. We recommend that you keep large computational files and more frequently accessed files in the $ARCHIVE_HOME directory. We also recommend that any important files located in $HOME should be copied into $ARCHIVE_HOME as well.

Because the compute nodes are unable to see $ARCHIVE_HOME, you will need to pre-stage your input files to your $WORKDIR from a login node before submitting jobs. After jobs complete, you will need to transfer output files from $WORKDIR to $ARCHIVE_HOME from a login node. This may be done manually or through the transfer queue, which executes serial jobs on login nodes.

4.1.4. Center-Wide File System Directory

The Center-Wide File System (CWFS) provides file storage that is accessible from SCOUT's login nodes and from the HPC Portal. The CWFS allows for file transfers and other file and directory operations from SCOUT using standard Linux commands. Each user has their own directory in the CWFS. The name of your CWFS directory may vary between machines and between centers, but the environment variable $CENTER will always refer to this directory.

The example below shows how to copy a file from your work directory on SCOUT to the CWFS ($CENTER).

While logged into SCOUT, copy your file from your work directory to the CWFS.

% cp $WORKDIR/filename $CENTER

4.2. Shells

The following shells are available on SCOUT: csh, bash, ksh, tcsh, zsh, and sh. To change your default shell, please email a request to Your preferred shell will become your default shell on the SCOUT cluster within 1-2 working days.

4.3. Environment Variables

A number of environment variables are provided by default on all HPCMP HPC systems. We encourage you to use these variables in your scripts where possible. Doing so will help to simplify your scripts and reduce portability issues if you ever need to run those scripts on other systems. The following environment variables are common to both the login and batch environments:

Common Environment Variables
Variable Description
$ARCHIVE_HOME Your directory on the archive server.
$ARCHIVE_HOST The host name of the archive server.
$BC_HOST The generic (not node specific) name of the system.
$CC The currently selected C compiler. This variable is automatically updated when a new compiler environment is loaded.
$CENTER Your directory on the Center-Wide File System (CWFS).
$CSI_HOME The directory containing the following list of heavily used application packages: ABAQUS, Accelrys, ANSYS, CFD++, Cobalt, EnSight, Fluent, GASP, Gaussian, LS-DYNA, and MATLAB, formerly known as the Consolidated Software Initiative (CSI) list. Other application software may also be installed here by our staff.
$CXX The currently selected C++ compiler. This variable is automatically updated when a new compiler environment is loaded.
$DAAC_HOME The directory containing DAAC-supported visualization tools: ParaView, VisIt, and EnSight.
$F77 The currently selected Fortran 77 compiler. This variable is automatically updated when a new compiler environment is loaded.
$F90 The currently selected Fortran 90 compiler. This variable is automatically updated when a new compiler environment is loaded.
$HOME Your home directory on the system.
$JAVA_HOME The directory containing the default installation of JAVA.
$KRB5_HOME The directory containing the Kerberos utilities.
$PET_HOME The directory containing the tools formerly installed and maintained by the PET staff. This variable is deprecated and will be removed from the system in the future. Certain tools will be migrated to $COST_HOME, as appropriate.
$PROJECTS_HOME A common directory where group-owned and supported applications and codes may be maintained for use by members of a group. Any project may request a group directory under $PROJECTS_HOME.
$SAMPLES_HOME The Sample Code Repository. This is a collection of sample scripts and codes provided and maintained by our staff to help users learn to write their own scripts. There are a number of ready-to-use scripts for a variety of applications.
$WORKDIR Your work directory on the local temporary file system (i.e., local high-speed disk).

4.4. Modules

Software modules are a very convenient way to set needed environment variables and include necessary directories in your path so commands for particular applications can be found. We strongly encourage you to use modules. For more information on using modules, see the Modules User Guide.

4.5. Archive Usage

Archive storage is provided through the /home NFS-mounted file system. All users are automatically provided a directory under this file system. However, it is only accessible from the login nodes. Since space in a user's login home area in /p/home is limited, all large data files requiring permanent storage should be placed in /home. Also, it is recommended that all important smaller files in /p/home for which a user requires long-term access be copied to /home as well. For more information on using the archive system, see the Archive System User Guide.

4.6. Login Files

When an account is created on SCOUT, a default .cshrc, and/or .profile file is placed into your home directory. This file contains the default modules setup to configure modules, LSF and other system defaults. We suggest you customize the following: .cshrc.pers or .profile.pers for your shell with any paths, aliases, or libraries you may need to load. The files should be sourced at the end of your .cshrc and/or .profile file as necessary. For example:

if (-f $HOME/.cshrc.pers) then
source $HOME/.cshrc.pers

If you need to connect to other Kerberized systems within the program, you should use /usr/brl/bin/ssh. If you use Kerberized ssh often, you may want to add an alias in your .cshrc.pers or .profile.pers files in $HOME, as follows:

alias ssh /usr/brl/bin/ssh # .cshrc.pers - csh/tcsh
alias ssh=/usr/brl/bin/ssh # .profile.pers - sh/ksh/bash

Note: the commands krcp, krlogin, and krsh are officially deprecated and will be removed at some point in the future. Users are strongly advised to stop using these three commands as soon as possible.

5. Program Development

5.1. Programming Models

SCOUT supports two programming models: Message Passing Interface (MPI) and Open Multi-Processing (OpenMP). A hybrid (MPI/OpenMP) programming model is also supported. MPI is an example of a message- or data-passing model. OpenMP only uses shared memory on a node by spawning threads. And, the hybrid model combines both models.

5.1.1. Message Passing Interface (MPI)

SCOUT has two MPI-3.0 standard library suites: IBM Spectrum and OpenMPI. The modules for these MPI libraries are mpi/spectrum/10.3 and mpi/openmpi/latest.

5.1.3. Hybrid Processing (MPI/OpenMP)

In hybrid processing, all intranode parallelization is accomplished using OpenMP, while all internode parallelization is accomplished using MPI. Typically, there is one MPI task assigned per node, with the number of OpenMP threads assigned to each node set at the number of cores available on the node.

5.2. Available Compilers

SCOUT has three compiler suites:

  • PGI
  • GNU

All versions of MPI share a common base set of compilers that are available on both the login and compute nodes.

Common Compiler Commands
Compiler PGI GNU Serial/Parallel
C pgcc gcc Serial/Parallel
C++ pgcc g++ Serial/Parallel
Fortran 77 pgf77 gfortran Serial/Parallel
Fortran 90 pgf90 gfortran Serial/Parallel

IBM MPT codes are built using the above compiler commands with addition of "-lmpi" option on the link line. The following additional compiler wrapper scripts are used for building MPI codes:

MPI Compiler Wrapper Scripts
Compiler PGI GNU Serial/Parallel
MPI C mpicc mpicc Parallel
MPI C++ mpicc mpicc Parallel
MPI F77 mpif77 mpif77 Parallel
MPI F90 mpif90 mpif90 Parallel

To select one of these compilers for use, load its associated module. See Relevant Modules (below) for more details.

5.2.1. PGI C, C++, and Fortran Compiler

The latest versions of the PGI compiler suite is also available to provide compatibility and portability of codes from other systems.

Several optimizations and tuning options are available for code developed with all PGI compilers. The table below shows some compiler options that may help with optimization.

Useful PGI Compiler Options
-O0 disable optimization
-g create symbols for tracing and debugging
-O1 optimize for speed with no loop unrolling and no increase in code size
-O2 or -default default optimization, optimize for speed with inline intrinsic and loop unrolling
-O3 level -O2 optimization plus memory optimization (allows compiler to alter code)
-Mipa Enable and specify options for Interprocedural Analysis (IPA)

The following tables contain examples of serial, MPI, and OpenMP compile commands for C, C++, and Fortran.

Example C Compile Commands
Programming ModelCompile Command
Serial pgcc -O3 my_code.c -o my_code.x
IBM Spectrum pgcc -O3 my_code.c -o my_code.x –lmpi
OpenMP pgcc -O3 my_code.c -o my_code.x -mp
Example C++ Compile Commands
Programming ModelCompile Command
Serial pgc++ -O3 my_code.C -o my_code.x
IBM Spectrum pgc++ -O3 my_code.C -o my_code.x –lmpi
OpenMP pgc++ -O3 my_code.C -o my_code.x -mp
Example Fortran Compile Commands
Programming ModelCompile Command
Serial pgf90 -O3 my_code.f90 -o my_code.x
IBM Spectrum pgf90 -O3 my_code.f90 -o my_code.x -lmpi
OpenMP pgf90 -O3 my_code.f90 -o my_code.x -mp
5.2.2. GNU Compiler

The default GNU compilers are good for compiling utility programs, but are probably not appropriate for computationally intensive applications. It is available without loading a separate module. The primary selling point of using GNU compilers is the compatibility between different architectures. They can be executed using the commands in the table above. For GNU compilers, the "-O" flag is the basic optimization setting.

More GNU compiler information can be found in the GNU gcc 4.8.5 manual.

5.3. Relevant Modules

If you compile your own codes, you will need to select which compiler and MPI version you want to use. For example:

module load compiler/pgi/x.x.x mpi/openmpi/x.x.x

These same module commands should be executed in your batch script before executing your program.

SCOUT provides individual modules for each compiler and MPI version. To see the list of currently available modules use the "module avail" command. You can use any of the available MPI versions with each compiler by pairing them together when you load the modules.

The table below shows the naming convention used for various modules.

Module Naming Conventions
Module Module Name
GCC Compilerscompiler/gcc/#.#.#
PGI Compilerscompiler/pgi/#.#
Go Compilerscompiler/go/#.#
IBM Spectrum MPImpi/spectrum/#.#

For more information on using modules, see the Modules User Guide.

5.4. Libraries

5.4.1. BLAS

The Basic Linear Algebra Subprogram (BLAS) library is a set of high quality routines for performing basic vector and matrix operations. There are three levels of BLAS operations:

  • BLAS Level 1: vector-vector operations
  • BLAS Level 2: matrix-vector operations
  • BLAS Level 3: matrix-matrix operations

More information on the BLAS library can be found at

5.4.2. Additional Math Libraries

There is also an extensive set of Math libraries available in the /opt/ibmmath/essl/6.2 directory on SCOUT.

5.5. Debuggers

5.5.1. gdb

The GNU Project Debugger (gdb) is a debugger that works similarly to dbx and can be invoked either with a program for execution or a running process id. To use gdb to debug a program during execution, use:

gdb a.out corefile

To debug a process that is currently executing on this node, use:

gdb a.out pid

For more information, the GDB manual can be found at

5.5.2. TotalView

TotalView is a debugger that supports threads, MPI, OpenMP, C/C++, and Fortran, mixed-language codes, advanced features like on-demand memory leak detection, other heap allocation debugging features, and the Standard Template Library Viewer (STLView). Unique features like dive, a wide variety of breakpoints, the Message Queue Graph/Visualizer, powerful data analysis, and control at the thread level are also available.

To start TotalView on a LSF job, you need to run an interactive batch job.

To do this:

Check to see how many cores are free using "qview". You should have "ssh -Y" to get an SSH tunnel to SCOUT from your machine, or if you are using Windows, start a X-Server such as Xming or Cygwin and then in PuTTy set the X11 forwarding in PuTTy's SSH - X11.

Once on SCOUT's login node, test the SSH tunnel with an "xclock".

To get your X DISPLAY sent from your batch job to your desktop, add the LSF option "-X" to the interactive job request as in the line below:

To get an interactive batch session with allocated compute nodes,

## number of cores needs to be as many or fewer than the number of

## available TotalView TeamPlus licenses.

## Here, for example, if there are 16 or more available licenses, try

> bsub -x -P myproject -W 01:00:00 -q debug –m "inf001" -I

-l select=1:ncpus=40:mpiprocs=16 -I

NOTE: You no longer need to use qtunnel or get another Kerberos ticket.

Once the interactive batch session starts...

> cd /p/work1/---/where_work_is


> cd $WORKDIR/.../where_work_is

Now test if your X11 display works from the LSF mom node:

> xclock

Load your TotalView module:

> module load totalview

Now you can run TotalView on your executable, in this example, mpi_test.x

> mpirun –tv8 -np 16 ./mpi_test.x

5.6. Code Profiling and Optimization

Profiling is the process of analyzing the execution flow and characteristics of your program to identify sections of code that are likely candidates for optimization, which increases the performance of a program by modifying certain aspects for increased efficiency.

We provide two profiling tools: gprof and codecov to assist you in the profiling process. A basic overview of optimization methods with information about how they may improve the performance of your code can be found in Performance Optimization Methods (below).

5.6.1. gprof

The GNU Project Profiler (gprof) is a profiler that shows how your program is spending its time and which functions calls are made. To profile code using gprof, use the "-pg" option during compilation. For more information, the gprof manual can be found at

5.6.2. Program Development Reminders

If an application is not programmed for distributed memory, then only the cores on a single node can be used. This is limited to 16 cores on SCOUT.

Check the utilization of the nodes your application is running on to see if it is taking advantage of all the resources available to it. This can be done by finding the nodes assigned to your job by executing "bstatus <JOB_ID>", logging into one of the nodes using the ssh command, and then executing the top command to see how many copies of your executable are being executed on the node.

Keep the system architecture in mind during code development. For instance, if your program requires more memory than is available on a single node, then you will need to parallelize your code so that it can function across multiple nodes.

5.6.3. Performance Optimization Methods

Optimization generally increases compilation time and executable size, and may make debugging difficult. However, it usually produces code that runs significantly faster. The optimizations that you can use will vary depending on your code and the system on which you are running.

Note: Before considering optimization, you should always ensure that your code runs correctly and produces valid output.

In general, there are five main categories of optimization:

  • Global Optimization
  • Loop Optimization
  • Interprocedural Analysis and Optimization(IPA)
  • Function Inlining
  • Profile-Guided Optimizations
Global Optimization

A technique that looks at the program as a whole and may perform any of the following actions:

  • Performed on code over all its basic blocks
  • Performs control-flow and data-flow analysis for an entire program
  • Detects all loops, including those formed by IF and GOTOs statements and performs general optimization.
  • Constant propagation
  • Copy propagation
  • Dead store elimination
  • Global register allocation
  • Invariant code motion
  • Induction variable elimination
Loop Optimization

A technique that focuses on loops (for, while, etc.) in your code and looks for ways to reduce loop iterations or parallelize the loop operations. The following types of actions may be performed:

  • Vectorization - rewrites loops to improve memory access performance. Compilers on SCOUT can automatically convert loops to utilize the instructions and registers on processors if they meet certain criteria.
  • Loop unrolling - (also known as "unwinding") replicates the body of loops to reduce loop branching overhead and provide better opportunities for local optimization.
  • Parallelization - divides loop operations over multiple processors where possible.
Interprocedural Analysis and Optimization (IPA)

A technique that allows the use of information across function call boundaries to perform optimizations that would otherwise be unavailable.

Function Inlining

A technique that seeks to reduce function call and return overhead.

  • Used with functions that are called numerous times from relatively few locations.
  • Allows a function call to be replaced by a copy of the body of that function.
  • May create opportunities for other types of optimization
  • May not be beneficial. Improper use may increase code size and actually result in less efficient code.
Profile-Guided Optimizations

Profile-Guided optimizations are available which allow the compiler to make data driven decisions during compilation on branch predictions, increased parallelism, block ordering, register allocation, function ordering, and more. The build for this option takes about three steps though and uses a representative data set to come up with the optimizations.

For example:

  • Step 1: Instrumentation, Compilation, and Linking

    gfortran -prof-gen -prof-dir ${HOME}/profdata -O2 -c a1.f a2.f a3.f
    gfortran -o a1 a1.o a2.o a3.o

  • Step 2: Instrumentation Execution


  • Step 3: Feedback Compilation

    gfortran -prof-use -prof-dir ${HOME}/profdata -ipo a1.f a2.f a3.f

6. Batch Scheduling

6.1. Scheduler

The Load Sharing Facility (LSF) is currently running on SCOUT. It schedules jobs and manages resources and job queues, and can be accessed through the interactive batch environment or by submitting a batch request. LSF is able to manage both single-processor and multiprocessor jobs. The LSF module is automatically loaded by the Master module on SCOUT at login.

6.2. Queue Information

The following table describes the LSF queues available on SCOUT:

Queue Descriptions and Limits on SCOUT
Priority Queue
Max Wall
Clock Time
Max Cores
Per Job
Highest transfer 48 Hours N/A Data transfer jobs
Down arrow for decreasing priority urgent 96 Hours N/A Designated urgent jobs by DoD HPCMP
debug 1 Hour N/A User diagnostic jobs
high 168 Hours N/A Designated high-priority projects by service/agency
frontier 168 Hours N/A Frontier projects only
cots 96 Hours N/A Abaqus and Fluent jobs
HIE 24 Hours N/A Rapid response for interactive work
interactive 12 Hours N/A Interactive jobs
standard 168 Hours N/A Normal user jobs
Lowest background 24 Hours N/A User jobs that will not be charged against the project allocation

6.3. Interactive Logins

When you log in to SCOUT, you will be running in an interactive shell on a login node. The login nodes provide login access for SCOUT and support such activities as compiling, editing, and general interactive use by all users. Please note the Login Node Abuse policy. The preferred method to run resource intensive executions is to use an interactive batch session.

6.4. Interactive Batch Sessions

An interactive session on a compute node is possible using a proper LSF command line syntax from a login node. Once LSF has scheduled your request on the compute pool, you will be directly logged into a compute node, and this session can last as long as your requested wall time.

To submit an interactive batch job, use the following submission format:

bsub -I -x -W HH:MM:SS -n 48 –m “inf[001-128]” –P proj_id -q interactive –V

Example: bsub -x -W 24 -q debug -m "inf001" -I -V

Your batch shell request will be placed in the interactive queue and scheduled for execution. This may take a few minutes or a long time depending on the system load. Once your shell starts, you will be logged into the first compute node of the compute nodes that were assigned to your interactive batch job. At this point, you can run or debug applications interactively, execute job scripts, or start executions on the compute nodes you were assigned. The "-X" option enables X-Windows access, so it may be omitted if that functionality is not required for the interactive job.

6.5. Batch Request Submission

LSF batch jobs are submitted via the bsub command. The format of this command is:

bsub [ options ] batch_script_file

bsub options may be specified on the command line or embedded in the batch script file by lines beginning with "#LSF".

6.7. Launch Commands

There are different commands for launching MPI executables from within a batch job depending on which MPI implementation your script uses.

To launch an IBM Spectrum executable, mpiexec command as follows:

mpiexec -n #_of_MPI_tasks ./mpijob.exe

To launch an OpenMPI executable, use the openmpi_wrapper command as follows:

openmpi_wrapper -n #_of_MPI_tasks ./mpijob.exe

For OpenMP executables, no launch command is needed.

6.8. Sample Script

The following script is a basic example.

#  Specify job name.
#BSUB -J myjob

#  Specify queue name.
#BSUB -q standard

#BSUB –n 48

#  Specify how MPI processes should be distributed across nodes.
#LSF -R "span[ptile=24]"

#  Specify maximum wall clock time.
#LSF -W 24:00:00

#  Specify Project ID to use. ID may have the form ARLAP96090RAY.

#  Specify that environment variables should be passed to master MPI process.

set JOBID='echo $LSF_JOBID | cut -f1 d.'

#  Create a temporary working directory within $WORKDIR for this job run.
mkdir -p $TMPD

# Change directory to submit directory
# and copy executable and input file to scratch space
cp mpicode.x $TMPD
cp input.dat $TMPD

cd $TMPD

# The following line provides an example of running a code built
#  with the gcc compiler and IBM Spectrum MPI.
module load compiler/gcc/9.1.1  mpi/spectrum/10.03
mpiexec -n 48 ./mpicode.x > out.dat

cp out.dat $LSF_O_WORKDIR


6.9. LSF Commands

The following commands provide the basic functionality for using the LSF batch system:

bsub: Used to submit jobs for batch processing.
bsub [ options ] my_job_script

qstat: Used to check the status of submitted jobs.
qstat $LSF_JOBID ## check one job
qstat -u my_user_name ## check all of user's jobs

bkill: Used to kill queued or running jobs.
bkill $LSF_JOBID

A subset of SCOUT's nodes has been set aside for use as part of the Advance Reservation Service (ARS). The ARS allows users to reserve a user-designated number of nodes for a specified number of hours starting at a specific date/time. This service enables users to execute interactive or other time-critical jobs within the batch system environment. The ARS is accessible via most modern web browsers at Authenticated access is required. The ARS User Guide is available on HPC Centers.

7. Software Resources

7.1. Application Software

All Commercial Off The Shelf (COTS) software packages can be found in the $CSI_HOME (/p/app) directory. The general rule for all COTS software packages is that the two latest versions will be maintained on our systems. For convenience, modules are also available for most COTS software packages.

7.2. Useful Utilities

The following utilities are available on SCOUT:

Useful Utilities
Command Description Usage
archive Perform the basic file-handling function on the archive system. archive put output.tar
node_use Displays memory-use and load-average information for all login nodes of the system on which it is executed. node_use
qpeek Returns the standard output (STDOUT) and standard error (STDERR) messages for any submitted LSF job from the start of execution. qpeek LSF_JOB_ID
qview Lists the status and current usage of all LSF queues on SCOUT. "qview -h" shows all the qview options available.
show_queues Lists the status and current usage of all LSF queues on SCOUT. show_queues
show_storage Provides quota and usage information for the storage areas in which the user owns data on the current system. show_storage
show_usage Lists the project ID and total hours allocated / used in the current FY for each project you have on SCOUT. show_usage
dos2unix Strip DOS end-of-record control characters from a text file. dos2unix myfile

7.3. Sample Code Repository

The Sample Code Repository is a directory that contains examples for COTS batch scripts, building and using serial and parallel programs, data management, and accessing and using serial and parallel math libraries. The $SAMPLES_HOME environment variable contains the path to this area, and is automatically defined in your login environment. Below is a listing of the examples provided in the Sample Code Repository on SCOUT.

8. Links to Vendor Documentation

IBM Home:
IBM Power9:

RedHat Home:

GNU Home:
GNU Compiler:

PGI Home:
PGI Compiler Documentation: