Comet User Guide

Technical Summary

Comet logo Comet is a dedicated XSEDE cluster designed by Dell and SDSC delivering ~2.0 petaflops with availability in early 2015. It features Intel next-gen processors with AVX2, Mellanox FDR InfiniBand interconnects, and Aeon storage.

The standard compute nodes consist of Intel Xeon E5-2680v3 (formerly codenamed Haswell) processors, 128 GB DDR4 DRAM (64 GB per socket), and 320 GB of SSD local scratch memory. The GPU nodes contain four NVIDIA GPUs each. The large memory nodes contain 1.5 TB of DRAM and four Haswell processors each. The network topology is 56 Gbps FDR InfiniBand with rack-level full bisection bandwidth and 4:1 oversubscription cross-rack bandwidth. Comet has 7 petabytes of 200 GB/second performance storage and 6 petabytes of 100 GB/second durable storage. It also has dedicated gateway/portal hosting nodes and a Virtual Image repository. External connectivity to Internet2 and ESNet is 100Gbps.

Technical Details

System ComponentConfiguration
1944 Standard Compute Nodes
Processor Type Intel Xeon E5-2680v3
Sockets 2
Cores/socket 12
Clock speed 2.5 GHz
Flop speed 960 GFlop/s
Memory capacity

128 GB DDR4 DRAM

Local scratch memory

320 GB SSD

Memory bandwidth 120 GB/s
STREAM Triad bandwidth 104 GB/s
36 GPU Nodes
GPUs 2 NVIDIA K-80
Cores/socket 12
Sockets 2
Clock speed 2.5 GHz
Memory capacity 128 GB DDR4 DRAM
Memory bandwidth 120 GB/s
Flash memory 320 GB
4 Large Memory Nodes
Sockets 4
Cores/socket 16
Clock speed 2.2 GHz
Memory capacity 1.5 TB
Memory bandwidth Tbd GB/s
Flash memory Tbd TB
Full System
Total compute nodes 1984
Total compute cores 47,776
Peak performance ~2.0 PFlop/s
Total memory 247 TB
Total memory bandwidth 228 TB/s
Total flash memory 634 TB
FDR InfiniBand Interconnect
Topology Hybrid Fat-Tree
Link bandwidth 56 Gb/s (bidirectional)
Peak bisection bandwidth TBD Gb/s
MPI latency 1.03-1.97 µs
DISK I/O Subsystem
File Systems NFS, Lustre
Storage capacity (durable) 6 PB
Storage capacity (performance) 7 PB
I/O bandwidth (performance disk) 200 GB/s

Comet supports the XSEDE core software stack, which includes remote login, remote computation, data movement, science workflow support, and science gateway support toolkits.

Systems Software Environment

Software FunctionDescription
Cluster Management Rocks
Operating System CentOS
File Systems NFS, Lustre
Scheduler and Resource Manager SLURM
XSEDE Software CTSS
User Environment Modules
Compilers Intel and PGI Fortran, C, C++
Message Passing Intel MPI, MVAPICH, Open MPI
Debugger DDT
Performance IPM, mpiP, PAPI, TAU

Supported Application Software

by Domain of Science

DomainSoftware

Biochemistry

APBS

Bioinformatics

BamTools, BEAGLE, BEAST, BEAST 2, bedtools, Bismark, BLAST, BLAT, Bowtie, Bowtie 2, BWA, Cufflinks, DPPDiv, Edena, FastQC, FastTree, FASTX-Toolkit, FSA, GARLI, GATK, GMAP-GSNAP, IDBA-UD, MAFFT, MrBayes, PhyloBayes, Picard, PLINK, QIIME, RAxML, SAMtools, SOAPdenovo2, SOAPsnp, SPAdes, TopHat, Trimmomatic, Trinity, Velvet

Compilers

GNU, Intel, Mono, PGI

File format libraries

HDF4, HDF5, NetCDF

Interpreted languages

MATLAB, Octave, R

Large-scale data-analysis frameworks

Hadoop 1, Hadoop 2 (with YARN), Spark, RDMA-Spark

Molecular dynamics

Amber, Gromacs, LAMMPS, NAMD

MPI libraries

MPICH2, MVAPICH2, Open MPI

Numerical libraries

ATLAS, FFTW, GSL, LAPACK, MKL, ParMETIS, PETSc, ScaLAPACK, SPRNG, Sundials, SuperLU, Trilinios

Predictive analytics

KNIME, Mahout, Weka

Profiling and debugging

DDT, IDB, IPM, mpiP, PAPI, TAU, Valgrind

Quantum chemistry

CPMD, CP2K, GAMESS, Gaussian, MOPAC, NWChem, Q-Chem, VASP

Structural mechanics

Abaqus

Visualization

IDL, VisIt

System Access

As an XSEDE computing resource, Comet is accessible to XSEDE users who are given time on the system. To obtain an account, users may submit a proposal through the XSEDE Allocation Request System or request a Trial Account.

Interested parties may contact SDSC User Support for help with a Comet proposal (see sidebar for contact information).

Logging in to Comet

Comet supports Single Sign On through the XSEDE User Portal and from the command line using an XSEDE-wide password. To log in to Comet from the command line, use the hostname:

comet.sdsc.edu 

The following are examples of Secure Shell (ssh) commands that may be used to log in to Comet:

ssh <your_username>@comet.sdsc.edu
ssh -l <your_username> comet.sdsc.edu 

Notes and hints

  • When you log in to comet.sdsc.edu, you will be assigned one of the four login nodes comet-ln[1-4].sdsc.edu. These nodes are identical in both architecture and software environment. Users should normally log in through comet.sdsc.edu, but may specify one of the four nodes directly if they see poor performance.
  • Please feel free to append your public RSA key to your ~/.ssh/authorized_keys file to enable access from authorized hosts without having to enter your password. Make sure you have a password on the private key on your local machine. You can use ssh-agent or keychain to avoid repeatedly typing the private key password.

Do not use the login nodes for computationally intensive processes.  These nodes are meant for compilation, file editing, simple data analysis, and other tasks that use minimal compute resources. All computationally demanding jobs should be submitted and run through the batch queuing system.

Modules

The Environment Modules package provides for dynamic modification of your shell environment. Module commands set, change, or delete environment variables, typically in support of a particular application. They also let the user choose between different versions of the same software or different combinations of related codes.

For example, if the Intel module and mvapich2_ib module are loaded and the user compiles with mpif90, the generated code is compiled with the Intel Fortran 90 compiler and linked with the mvapich2_ib MPI libraries.

Several modules that determine the default Comet environment are loaded at login time. These include the MVAPICH implementation of the MPI library and the Intel compilers. We strongly suggest that you use this combination whenever possible to get the best performance.

Useful Modules Commands

Here are some common module commands and their descriptions:

CommandDescription

module list

List the modules that are currently loaded

module avail

List the modules that are available

module display <module_name>

Show the environment variables used by <module name> and how they are affected

module unload <module name>

Remove <module name> from the environment

module load <module name>

Load <module name> into the environment

module swap <module one> <module two>

Replace <module one> with <module two> in the environment

Loading and unloading modules

You must remove some modules before loading others.

Some modules depend on others, so they may be loaded or unloaded as a consequence of another module command. For example, if intel and mvapich2_ib are both loaded, running the command module unload intel will automatically unload mvapich2_ib. Subsequently issuing the module load intel command does not automatically reload mvapich2_ib.

If you find yourself regularly using a set of module commands, you may want to add these to your configuration files (.bashrc for bash users, .cshrc for C shell users). Complete documentation is available in the module(1) and modulefile(4) manpages.

Module: command not found

The error message module: command not found is sometimes encountered when switching from one shell to another or attempting to run the module command from within a shell script or batch job.  The reason that the module command may not be inherited as expected is that it is defined as a function for your login shell. If you encounter this error execute the following from the command line (interactive shells) or add to your shell script (including Torque batch scripts)

source /etc/profile.d/modules.sh 

Managing Your Accounts

The show_accounts command lists the accounts that you are authorized to use, together with a summary of the used and remaining time.

[user@comet-login1 ~]$ show_accounts
ID name   project   used          available    used_by_proj
---------------------------------------------------------------
<user> <project> <SUs by user> <SUs available> <SUs by proj> 

To charge your job to one of these projects, replace  << project >> with one from the list and put this PBS directive in your job script:

  #SBATCH -A << project >>

Many users will have access to multiple accounts (e.g. an allocation for a research project and a separate allocation for classroom or educational use). On some systems a default account is assumed, but please get in the habit of explicitly setting an account for all batch jobs. Awards are normally made for a specific purpose and should not be used for other projects.

Adding Users to an Account

Project PIs and co-PIs can add or remove users from an account. To do this, log in to your XSEDE portal account and go to the Add User page.

Charging

The charge unit for all SDSC machines, including Comet, is the Service Unit (SU). This corresponds to the use of one compute core for one hour. Keep in mind that your charges are based on the resources that are tied up by your job and don’t necessarily reflect how the resources are used. Charges are based on either the number of cores or the fraction of the memory requested, whichever is larger. The minimum charge for any job longer than 10 seconds is 1 SU.

Job Charge Considerations

  • A node-exclusive job that runs on a compute node for one hour will be charged 24 SUs (24 cores x 1 hour)
  • A serial job in the shared queue that uses less than 5 GB memory and runs for one hour will be charged 1 SU (1 core x 1 hour)
  • A gpu/gpu-shared job will be charged a premium of 2x (typical GPU performance of codes running on Comet is more than  2x higher then a CPU)
  • A gpu is equivalent to 1/4th of a node which equals 6 cores.
  • Multicore jobs will scale according to resource utilization
  • Each standard compute node has ~128 GB of memory and 24 cores
    • Each standard node core has 5 GB of memory (1/24th of the total memory on a standard compute node)
  • Each large memory node has ~1.5 TB of memory and 64 cores
    • Each large memory core has 24 GB of memory (1/64 of total memory on a large memory node)

Compiling

Comet provides the Intel, Portland Group (PGI), and GNU compilers along with multiple MPI implementations (MVAPICH2, MPICH2, OpenMPI). Most applications will achieve the best performance on Comet using the Intel compilers and MVAPICH2 and the majority of libraries installed on Gordon have been built using this combination. Although other compilers and MPI implementations are available, we suggest using these only for compatibility purposes.

All three compilers now support the Advanced Vector Extensions 2 (AVX2). Using AVX2, up to eight floating point operations can be executed per cycle per core, potentially doubling the performance relative to non-AVX2 processors running at the same clock speed. Note that AVX2 support is not enabled by default and compiler flags must be set as described below.

Using the Intel Compilers (Default/Suggested)

The Intel compilers and the MVAPICH2 MPI implementation will be loaded by default. If you have modified your environment, you can reload by executing the following commands at the Linux prompt or placing in your startup file (~/.cshrc or ~/.bashrc)

module purge
module load gnutools
module load intel mvapich2_ib

For AVX2 support, compile with the -xHOST option. Note that -xHOST alone does not enable aggressive optimization, so compilation with -O3 is also suggested. The -fast flag invokes -xHOST, but should be avoided since it also turns on interprocedural optimization (-ipo), which may cause problems in some instances.

Intel MKL libraries are available as part of the "intel" modules on Comet. Once this module is loaded, the environment variable MKL_ROOT points to the location of the mkl libraries. The MKL link advisor can be used to ascertain the link line (change the MKL_ROOT aspect appropriately).

For example to compile a C program statically linking 64 bit scalapack libraries on Comet:

mpicc -o pdpttr.exe pdpttr.c \
    -I$MKL_ROOT/include ${MKL_ROOT}/lib/intel64/libmkl_scalapack_lp64.a \
    -Wl,--start-group ${MKL_ROOT}/lib/intel64/libmkl_intel_lp64.a \
    ${MKL_ROOT}/lib/intel64/libmkl_core.a ${MKL_ROOT}/lib/intel64/libmkl_sequential.a \
    -Wl,--end-group ${MKL_ROOT}/lib/intel64/libmkl_blacs_intelmpi_lp64.a -lpthread -lm

For more information on the Intel compilers: [ifort | icc | icpc] -help

Serial

MPI

OpenMP

MPI+OpenMP

Fortran

ifort

mpif90

ifort -openmp

mpif90 -openmp

C

icc

mpicc

icc -openmp

mpicc -openmp

C++

icpc

mpicxx

icpc -openmp

mpicxx -openmp

Note for C/C++ users: compiler warning - feupdateenv is not implemented and will always fail. For most users, this error can safely be ignored. By default, the Intel C/C++ compilers only link against Intel's optimized version of the C standard math library (libmf). The error stems from the fact that several of the newer C99 library functions related to floating point rounding and exception handling have not been implemented.

Using the PGI Compilers

The PGI compilers can be loaded by executing the following commands at the Linux prompt or placing in your startup file (~/.cshrc or ~/.bashrc)

module purge
module load gnutools
module load pgi mvapich2_ib

For AVX support, compile with -fast

For more information on the PGI compilers: man [pgf90 | pgcc | pgCC]

Serial

MPI

OpenMP

MPI+OpenMP

Fortran

pgf90

mpif90

pgf90 -mp

mpif90 -mp

C

pgcc

mpicc

pgcc -mp

mpicc -mp

C++

pgCC

mpicxx

pgCC -mp

mpicxx -mp

Using the GNU Compilers

The GNU compilers can be loaded by executing the following commands at the Linux prompt or placing in your startup files (~/.cshrc or ~/.bashrc)

module purge
module load gnutools
module load gnu openmpi_ib

For AVX support, compile with -mavx. Note that AVX support is only available in version 4.6 or later, so it is necessary to explicitly load the gnu/4.6.1 module until such time that it becomes the default.

For more information on the GNU compilers: man [gfortran | gcc | g++]

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

MVAPICH2-GDR on Comet GPU Nodes

The GPU nodes on Comet have MVAPICH2-GDR available. MVAPICH2-GDR is based on the standard MVAPICH2 software stack, incorporates designs that take advantage of the new GPUDirect RDMA technology for inter-node data movement on NVIDIA GPUs clusters with Mellanox InfiniBand interconnect. The mvapich2-gdr modules are also available on the login nodes for compiling purposes. An example compile and run script is provided in /share/apps/examples/MVAPICH2GDR.

Notes and Hints

  • The mpif90, mpicc, and mpicxx commands are actually wrappers that call the appropriate serial compilers and load the correct MPI libraries. While the same names are used for the Intel, PGI and GNU compilers, keep in mind that these are completely independent scripts.
  • If you use the PGI or GNU compilers or switch between compilers for different applications, make sure that you load the appropriate modules before running your executables.
  • When building OpenMP applications and moving between different compilers, one of the most common errors is to use the wrong flag to enable handling of OpenMP directives. Note that Intel, PGI, and GNU compilers use the -openmp, -mp, and -fopenmp flags, respectively.
  • Explicitly set the optimization level in your makefiles or compilation scripts. Most well written codes can safely use the highest optimization level (-O3), but many compilers set lower default levels (e.g. GNU compilers use the default -O0, which turns off all optimizations).
  • Turn off debugging, profiling, and bounds checking when building executables intended for production runs as these can seriously impact performance. These options are all disabled by default. The flag used for bounds checking is compiler dependent, but the debugging (-g) and profiling (-pg) flags tend to be the same for all major compilers.

Running Jobs on Regular Compute Nodes

Comet uses the Simple Linux Utility for Resource Management (SLURM) batch environment. When you run in the batch mode, you submit jobs to be run on the compute nodes using the sbatch command as described below. Remember that computationally intensive jobs should be run only on the compute nodes and not the login nodes. Comet has the following partitions available:

Queue NameMax WalltimeMax NodesComments
compute 48 hrs 72 Used for access to regular compute nodes
gpu 48 hrs 4 Used for access to the GPU nodes
gpu-shared 48 hrs 1 Used for shared access to a partial GPU node
shared 48 hrs 1 Single-node jobs using fewer than 24 cores
large-shared 48 hrs 1 Single-node jobs using large memory up to 1.45 TB
debug 30 mins 2 Used for access to debug nodes

Submitting Jobs Using sbatch

Jobs can be submitted to the sbatch partitions using the sbatch command as follows:

[user@comet-ln1]$ sbatch jobscriptfile

where jobscriptfile is the name of a UNIX format file containing special statements (corresponding to sbatch options), resource specifications and shell commands. Several example SLURM scripts are given below:

Basic MPI Job

#!/bin/bash
#SBATCH --job-name="hellompi"
#SBATCH --output="hellompi.%j.%N.out"
#SBATCH --partition=compute
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=24
#SBATCH --export=ALL
#SBATCH -t 01:30:00

#This job runs with 2 nodes, 24 cores per node for a total of 48 cores.
#ibrun in verbose mode will give binding detail

ibrun -v ../hello_mpi 

Basic OpenMP Job

#!/bin/bash
#SBATCH --job-name="hello_openmp"
#SBATCH --output="hello_openmp.%j.%N.out"
#SBATCH --partition=compute
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=24
#SBATCH --export=ALL
#SBATCH -t 01:30:00

#SET the number of openmp threads
export OMP_NUM_THREADS=24

#Run the job using mpirun_rsh
./hello_openmp 

Hybrid MPI-OpenMP Job

#!/bin/bash
#SBATCH --job-name="hellohybrid"
#SBATCH --output="hellohybrid.%j.%N.out"
#SBATCH --partition=compute
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=24
#SBATCH --export=ALL
#SBATCH -t 01:30:00

#This job runs with 2 nodes, 24 cores per node for a total of 48 cores.
# We use 8 MPI tasks and 6 OpenMP threads per MPI task

export OMP_NUM_THREADS=6
ibrun --npernode 4 ./hello_hybrid 

Basic mpirun_rsh Job

#!/bin/bash
#SBATCH --job-name="hellompirunrsh"
#SBATCH --output="hellompirunrsh.%j.%N.out"
#SBATCH --partition=compute
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=24
#SBATCH --export=ALL
#SBATCH -t 01:30:00

#Generate a hostfile from the slurm node list
export SLURM_NODEFILE=`generate_pbs_nodefile`

#Run the job using mpirun_rsh
mpirun_rsh -hostfile $SLURM_NODEFILE -np 48 ../hello_mpi

Using the Shared Partition

#!/bin/bash
#SBATCH -p shared
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=8
#SBATCH --mem=40G
#SBATCH -t 01:00:00
#SBATCH -J HPL.8
#SBATCH -o HPL.8.%j.%N.out
#SBATCH -e HPL.8.%j.%N.err
#SBATCH --export=ALL

export MV2_SHOW_CPU_BINDING=1
ibrun -np 8 ./xhpl.exe

The above script will run using 8 cores and 40 GB of memory. Please note that the performance in the shared partition may vary depending on how sensitive your application is to memory locality and the cores you are assigned by the scheduler. It is possible the 8 cores will span two sockets for example.

SLURM No-Requeue Option

SLURM will requeue jobs if there is a node failure. However, in some cases this might be detrimental if files get overwritten. If users wish to avoid automatic requeue, the following line should be added to their script:

#SBATCH --no-requeue

License Scheduling

#!/bin/bash
#SBATCH --job-name="abaqus"
#SBATCH --output="abaqus.%j.%N.out"
#SBATCH --partition=compute
#SBATCH --nodes=1
#SBATCH --export=ALL
#SBATCH --ntasks-per-node=24
#SBATCH -L abaqus:24
#SBATCH -t 01:00:00
module load abaqus/6.14-1
export EXE=`which abq6141`
$EXE job=s4b input=s4b scratch=/scratch/$USER/$SLURM_JOBID cpus=24 mp_mode=threads memory=120000mb interactive

Example Scripts for Applications

SDSC User Services staff have developed sample run scripts for common applications. They are available in the /share/apps/examples directory on Comet.

Job Dependencies

There are several scenarios (e.g. splitting long running jobs, workflows) where users may require jobs with dependencies on successful completions of other jobs. In such cases, SLURM's --dependency option can be used. The syntax is as follows:

[user@comet-ln1 ~]$ sbatch --dependency=afterok:jobid jobscriptfile

Job Monitoring and Management

Users can monitor jobs using the squeue command.

[user@comet-ln1 ~]$ squeue -u user1

             JOBID PARTITION     NAME     USER ST       TIME  NODES NODELIST(REASON)
            256556   compute raxml_na user1     R    2:03:57      4 comet-14-[11-14]
            256555   compute raxml_na user1     R    2:14:44      4 comet-02-[06-09]

In this example, the output lists two jobs that are running in the "compute" partition. The jobID, partition name, job names, user names, status, time, number of nodes, and the node list are provided for each job. Some common squeue options include:

OptionResult
-i <interval> Repeatedly report at intervals (in seconds)
-i <job_list> Displays information for specified job(s)
-i <part_list> Displays information for specified partitions (queues)
-i <state_list> Shows jobs in the specified state(s)

Users can cancel their own jobs using the scancel command as follows:

[user@comet-ln1 ~]$ scancel <jobid>

Help with ibrun

The options and arguments for ibrun are as follows:

Usage:
    ibrun [options] <executable> [executable args]

     Options:
        -n, -np <n>
            launch n MPI ranks (default: use all cores provided by resource manager)

        -o, --offset <n>
            assign MPI ranks starting at the nth slot provided by the resource 
            manager (default: 0)
    
        -no <n>
            assign MPI ranks starting at the nth unique node provided by the 
            resource manager (default: 0)
    
        --npernode <n>
            only launch n MPI ranks per node (default: ppn from resource manager)

        --tpr|--tpp|--threads-per-rank|--threads-per-process <n>
            how many threads each MPI rank (often referred to as 'MPI process') 
            will spawn.  (default: $OMP_NUM_THREADS (if defined), <ppn>/<npernode>
            if ppn is divisible by npernode, or 1 otherwise)
    
        --switches '<implementation-specific>'
            Pass additional command-line switches to the underlying implementation's
            MPI launcher.  These WILL be overridden by any switches ibrun 
            subsequently enables (default: none)
    
        -bp|--binding-policy <scatter|compact|none>
            Define the CPU affinity's binding policy for each MPI rank.  'scatter' 
            distributes ranks across each binding level, 'compact' fills up a 
            binding level before allocating another, and 'none' disables all 
            affinity settings (default: optimized for job geometry)
    
        -bl|--binding-level <core|socket|numanode|none>
            Define the level of granularity for CPU affinity for each MPI rank.  
            'core' binds each rank to a single core; 'socket' binds each rank to 
            all cores on a single CPU socket (good for multithreaded ranks); 
            'numanode' binds each rank to the subset of cores belonging to a
            numanode; 'none' disables all affinity settings. (default: optimized 
            for job geometry)

        --dryrun
            Do everything except actually launch the application

        -v|--verbose
            Print diagnostic messages
    
        -? 
            Print this message

Info on Globus Endpoints, Data Movers and Mount Points

All of Comet's NFS and Lustre filesystems are acccessible via the Globus endpoint xsede#comet. The servers also mount Gordon's filesystems, so the mount points are a different for each system. The following table shows the mount points on the data mover nodes (that are the backend for xsede#comet and xsede#gordon).

MachineLocation on machineLocation on Globus/Data Movers
Comet, Gordon /home/$USER /home/$USER
Comet, Gordon /oasis/projects/nsf /oasis/projects/nsf
Comet /oasis/scratch/comet /oasis/scratch-comet
Gordon /oasis/scratch /oasis/scratch

Storage Overview

SSD Scratch Space

The compute nodes on Comet have access to fast flash storage. There is 250GB of SSD space available for use on each compute node. The latency to the SSDs is several orders of magnitude lower than that for spinning disk (<100 microseconds vs. milliseconds) making them ideal for user-level check pointing and applications that need fast random I/O to large scratch files. Users can access the SSDs only during job execution under the following directories local to each compute node:

/scratch/$USER/$SLURM_JOB_ID

Parallel Lustre Filesystems

In addition to the local scratch storage, users will have access to global parallel filesystems on Comet. Overall, Comet has 7 petabytes of 200 GB/second performance storage and 6 petabytes of 100 GB/second durable storage.

Users can now access /oasis/projects from Comet. The two Lustre filesystems available on Comet are:

  • Lustre Comet scratch filesystem: /oasis/scratch/comet/$USER/temp_project
  • Lustre NSF projects filesystem: /oasis/projects/nsf

Virtual Clusters

VCs are not meant to replace the standard HPC batch queuing system, which is well suited for most scientific and technical workloads. In addition, a VC should not be simply thought of as a VM (virtual machine). Future XSEDE resources, such as Indiana University’s Jetstream will address this need. VCs are primarily intended for those users who require both fine-grained control over their software stack and access to multiple nodes. With regards to the software stack, this may include access to operating systems different from the default version of CentOS available on Comet or to low-level libraries that are closely integrated with the Linux distribution. Science Gateways serving large research communities and that require a flexible software environment are encouraged to consider applying for a VC, as are current users of commercial clouds who want to make the transition for performance or cost reasons.

Maintaining and configuring a virtual cluster requires a certain level of technical expertise. We expect that each project will have at least one person possessing strong systems administration experience with the relevant OS since the owner of the VC will be provided with "bare metal" root level access. SDSC staff will be available primarily to address performance issues that may be related to problems with the Comet hardware and not to help users build their system images.

All VC requests must include a brief justification that addresses the following:

  • Why is a VC required for this project?
  • What expertise does the PI’s team have for building and maintaining the VC?

Using GPU Nodes

The GPU nodes can be accessed via the "gpu" and "gpu-shared" partitions. In addition to the partition name, the individual GPUs are scheduled as a resource.  For example on the "gpu" partition the following lines are needed to utilize all 4 GPUs:

#SBATCH -p gpu
#SBATCH --gres=gpu:4

Users should always request 6 tasks_per_node for all  "gpu-shared" jobs to ensure proper resource distribution by the scheduler.  The following requests 2 GPUs on a "gpu-shared" partition:

#SBATCH -p gpu-shared
#SBATCH --ntasks-per-node=12 #SBATCH --gres=gpu:2

Please see /share/apps/examples/GPU for more examples.

The shared GPU queue is charged differently from other resource, to reflect fraction of resource used based on # GPUs, memory or CPU(whichever is higher) and the performance.   We charge a 2X premium on GPUs for performance, which is generally 2x higher than on a CPU.   1 GPU it is equivalent to 1/4th of the node or 6 cores. So the charging equation will be:

 =2 (premium for GPU nodes) x 24 x (fraction of node used based on #GPUs, memory or CPUs - whichever is higher) x (wallclock time).

Using Large Memory Nodes

The large memory nodes can be accessed via the "large-shared" partition. Charges are based on either the number of cores or the fraction of the memory requested, whichever is larger.

For example, on the "large-shared" partition, the following job requesting 16 cores and 445 GB of memory (about 31.3% of 1455 GB of one node's available memory) for 1 hour will be charged 20 SUs:

455/1455(memory) * 64(cores) * 1(duration) ~= 20

#SBATCH --ntasks=16
#SBATCH --mem=455G
#SBATCH --partition = large-shared

While there is not a separate 'large' partition, a job can still explicitly request all of the resources on a large memory node. Please note that there is no premium for using Comet's large memory nodes, but the processors are slightly slower (2.2 GHz compared to 2.5 GHz on the standard nodes), Users are advised to request the large nodes only if they need the extra memory.

Software

Package Details

Software Package

Compiler Suites

Parallel Interface

AMBER: Assisted Model Building with Energy Refinement

intel

mvapich2_ib

APBS: Adaptive Poisson-Boltzmann Solver

intel

mvapich2_ib

Car-Parrinello 2000 (CP2K)

intel

mvapich2_ib

DDT

 

 

FFTW: Fastest Fourier Transform in the West

intel,pgi,gnu

mvapich2_ib

GAMESS: General Atomic Molecular Electronic Structure System

intel

native: sockets, ip over ib
vsmp: scalemp mpich2

GAUSSIAN

pgi

Single node, shared memory

GROMACS: GROningen MAchine for Chemical Simulations

intel

mvapich2_ib

HDF4/HDF5: Hierarchical Data Format

intel,pgi,gnu

mvapich2_ib for hdf5

Lammps:Large-scale Atomic/Molecular Massively Parallel Simulator.

intel

mvapich2_ib

NAMD: NAnoscale Molecular Dynamics

intel

mvapich2_ib

NCO: netCDF Operators

intel,pgi,gnu

none

netCDF: Network Common Data Format

Intel,pgi,gnu

none

Python modules (scipy etc)

gnu:ipython,nose,pytz
intel:matplotlib,numpy,scipy,pyfits

None

RDMA-Hadoop 2.x

None

None

RDMA-Spark

None

None

Singularity: User Defined Images

None

None

VisIt Visualization Package

intel

openmpi


Software Package Descriptions

AMBER

AMBER is package of molecular simulation programs including SANDER (Simulated Annealing with NMR-Derived Energy Restraints) and a modified version PMEME (Particle Mesh Ewald Molecular Dynamics) that is faster and more scalable.

APBS

APBS evaluates the electrostatic properties of solvated biomolecular systems.

APBS documentation

CP2K

CP2K is a program to perform simulations of molecular systems. It provides a general framework for different methods such as Density Functional Theory (DFT) using a mixed Gaussian and plane waves approach (GPW) and classical pair and many-body potentials.

CP2K documentation

DDT

DDT is a debugging tool for scalar, multithreaded and parallel applications.

DDT Debugging Guide from TACC

FFTW

FFTW is a library for computing the discrete Fourier transform in one or more dimensions, of arbitrary input size, and of both real and complex data.

FFTW documentation

GAMESS

GAMESS is a program for ab initio quantum chemistry. GAMESS can compute SCF wavefunctions, and correlation corrections to these wavefunctions as well as Density Functional Theory.

GAMESS documentation, examples, etc.

Gaussian 09

Gaussian 09 provides state-of-the-art capabilities for electronic structure modeling.

Gaussian 09 User's Reference

GROMACS

GROMACS is a versatile molecular dynamics package, primarily designed for biochemical molecules like proteins, lipids and nucleic acids.

GROMACS Online Manual

HDF

HDF is a collection of utilities, applications and libraries for manipulating, viewing, and analyzing data in HDF format.

HDF 5 Resources

LAMMPS

LAMMPS is a classical molecular dynamics simulation code.

LAMMPS User Manual

NAMD

NAMD is a parallel, object-oriented molecular dynamics code designed for high-performance simulation of large biomolecular systems.

NAMD User's Guide

NCO

NCO operates on netCDF input files (e.g. derive new data, average, print, hyperslab, manipulate metadata) and outputs results to screen or text, binary, or netCDF file formats.

NCO documentation on SourceForge

netCDF

netCDF is a set of libraries that support the creation, access, and sharing of array-oriented scientific data using machine-independent data formats.

netCDF documentation on UCAR's Unidata Program Center

Python Modules (scipy etc.)

The Python modules under /opt/scipy consist of: node, numpy, scipy, matplotlib, pyfits, ipython and pytz.

Video tutorial from a TACC workshop on Python

Python videos from Khan Academy

The HPC University Python resources

RDMA-Hadoop-2.x

RDMA-based Apache Hadoop 2.x is a high performance derivative of Apache Hadoop developed as part of the High-Performance Big Data (HiBD) project at the Network-Based Computing Lab of The Ohio State University. The installed release on Comet (v0.9.7) is based on Apache Hadoop 2.6.0. The design uses Comet's InfiniBand network at the native level (verbs) for HDFS, MapReduce, and RPC components, and is optimized for use with Lustre.

The design features a hybrid RDMA based HDFS design with in-memory and heterogenous storage including RAM Disk, SSD, HDD, and Lustre. In addition, optimized MapReduce over Lustre (with RDMA based shuffle) is also available. The implementation is fully integrated with SLURM (and PBS) on Comet with scripts available to dynamically deploy hadoop clusters within the SLURM scheduling framework.

Examples for various modes of usage are available in /share/apps/examples/HADOOP/RDMA. Please email help@xsede.org (reference Comet as the machine, and SDSC as the site) if you have any further questions about usage and configuration. Details on the RDMA Hadoop and HiBD project are available at http://hibd.cse.ohio-state.edu.

RDMA-Spark

RDMA-based Apache Spark package is a high performance derivative of Apache Spark developed as part of the High-Performance Big Data (HiBD) project at the Network-Based Computing Lab of The Ohio State University. The installed release on Comet (v0.9.1) is based on Apache Spark 1.5.1. The design uses Comet's InfiniBand network at the native level (verbs) for RDMA based data shuffle, SEDA based shuffle architecture, efficient connection management, non-blocking and chunk based data transfer, and off-JVM-heap buffer management.

The RDMA-Spark cluster setup and usage is managed via the myHadoop framework. An example script is provided in /share/apps/examples/SPARK/sparkgraphx_rdma. Please email help@xsede.org (reference Comet as the machine, and SDSC as the site) if you have any further questions about usage and configuration. Details on the RDMA Spark and HiBD project are available at http://hibd.cse.ohio-state.edu.

Singularity: User Defined Images

Singularity is a platform to support users that have different environmental needs then what is provided by the resource or service provider. While the high level perspective of other container solutions seems to fill this niche very well, the current implementations are focused on network service virtualization rather than application level virtualization focused on the HPC space. Because of this, Singularity leverages a workflow and security model that makes it a very reasonable candidate for shared or multi-tenant HPC resources like Comet without requiring any modifications to the scheduler or system architecture. Additionally, all typical HPC functions can be leveraged within a Singularity container (e.g. InfiniBand, high performance file systems, GPUs, etc.). While Singularity supports MPI running in a hybrid model where you invoke MPI outside the container and it runs the MPI programs inside the container, we have not yet tested this.

Examples for various modes of usage are available in /share/apps/examples/Singularity. Please email help@xsede.org (reference Comet as the machine, and SDSC as the site) if you have any further questions about usage and configuration. Details on theSignularity project are available at http://singularity.lbl.gov/#home.

VisIt Visualization Package

The VisIt visualization package supports remote submission of parallel jobs and includes a Python interface that provides bindings to all of its plots and operators so they may be controlled by scripting.

Getting Started With VisIt tutorial

Publications

View Comet presentations from XSEDE '14.

SR-IOV: Performance Benefits for Virtualized Interconnects

Glenn K. Lockwood, Mahidhar Tatineni, Rick Wagner (SDSC)
XSEDE'14
July 15, Atlanta


Gateways to Discovery: Cyberinfrastructure for the Long Tail of Science

R. L. Moore, C. Baru, D. Baxter, G. Fox (Indiana U), A Majumdar, P Papadopoulos, W Pfeiffer, R. S. Sinkovits, S. Strande (NCAR), M. Tatineni, R. P. Wagner, N. Wilkins-Diehr, M. L. Norman (UCSD/SDSC except as noted)
XSEDE'14
July 16, Atlanta