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Prince Cluster Announcement

Our new HPC cluster Prince, is now in production and available for use in research projects and courses!

Prince will replace the existing Mercer cluster which will be decommissioned on Friday May 19th at 5pm (starting May 1st, we will not create new user accounts or install new software on Mercer)

Please follow this link for a detailed description of the Prince components. In summary, Prince comes with:

  • new Intel CPU

  • new low latency 100Gbps EDR Infiniband interconnects

  • new home directories

  • new batch system (SLURM) - see more below

SLURM is replacing PBS as the batch system to manage user jobs on the Prince cluster. We have prepared this tutorial to help with the migration from PBS to SLURM. Also, please check this HPC training offerings calendar for regularly scheduled HPC trainings which provide an introduction to SLURM and the Prince cluster.

Important Mercer to Prince transition instructions:

  • The “scratch” and “archive” file systems are available on both clusters. There is no need to transfer your files on “scratch” or “archive” to Prince.

  • Home directories on Prince are different than those on Mercer. You need to transfer any files that you need from your Mercer home directory to your Prince home directory.

  • The “work” file system will be retired and will not be available on Prince.

  • If you are using Globus to transfer files, please use the Globus endpoint on the Prince cluster.

If you have any questions, please do not hesitate to email us:

Welcome to High Performance Computing (HPC) at New York University 

NYU HPC, within IT, operates and supports high performance computing resources and assists the NYU research community in their use. HPC resources are open to NYU faculty and staff, and faculty-sponsored students, and may be used for class instruction. IT is also available to partner with faculty as they seek funding for research with substantial technology components - see HPC Stakeholders and also ITS Research Computing. We can also assist in access to and collaboration with a number of national and state HPC facilities.

Getting and Renewing Access

 Click here to expand...

For how to log in, see Logging in to the NYU HPC Clusters

Who is eligible for an HPC account?

NYU HPC resources are available at no charge to full-time NYU faculty (other than NYU Medical School) and to all other NYU staff and students with full-time NYU faculty sponsorship (more...)

Getting an account on the NYU HPC clusters

First you need a valid NYU NetID. Your HPC sponsor can request one for you here. You also need a valid NYU Google account to receive emails, as does your HPC sponsor - contact us if you need assistance with this.

Next you need a faculty sponsor.

Finally, log into the NYU Identity Management service and follow the link to "Request HPC account". We have a walkthrough of the process here.

Renewing your HPC account

Each year, non-faculty users must renew their HPC account by filling in the account renewal form from the NYU Identity Management service. See  Renewing your HPC account with IIQ for a walk-through of the process.

Information for faculty who sponsor HPC users

You can request a NetID for your student or collaborator here. The request form has additional information about affiliates.

Each year, your sponsored users must renew their account. You will need to approve the renewal by logging into the NYU Identity Management service. We have a walkthrough of the process, with screenshots, here.

Pre-approving a list of netids for class HPC accounts

Faculty (who can sponsor HPC accounts) can pre-approve requests in bulk - this is intended to streamline the process of registering a class to use the HPC facilities. Faculty can set this up via the NYU Identity Management service. We also have a walkthrough of the process here.

Getting an account with one of NYU partners

NYU partners with many state and national facilities with a variety of HPC systems and expertise. Contact us for assistance setting up a collaboration with any these.

The Open Science Data Cloud
Provides 1TB free storage for science data. We encourage researchers to publish datasets associated with published research as "Public Data" on OSDC

The NY State High Performance Computing Consortium (hpc^2)
Provides  high performance computing resources for New York State industry and academic institutions:

Rensselaer Polytechnic Institute
Stony Brook University - Dave Ecker
University at Buffalo
Brookhaven National Lab

The Extreme Science and Engineering Discovery Environment (XSEDE)
The most advanced, powerful, and robust collection of integrated advanced digital resources and services in the world; a single virtual system that scientists can use to interactively share computing resources, data, and expertise.

Open Science Grid
A national, distributed computing grid for data-intensive research.

The Common Solutions Group
for cooperative exploration of common solutions to IT challenges in higher education

The Open Science Project
is dedicated to writing and releasing free and Open Source scientific software. 

is a private not-for-profit corporation created to foster science and education in New York State

The National Science Foundation
An independent federal agency created by Congress in 1950 "to promote the progress of science; to advance the national health, prosperity, and welfare; to secure the national defense."

Oak Ridge National Laboratory
The Department of Energy's largest science and energy laboratory.

Argonne National Laboratory
One of the U.S. Department of Energy's largest research centers. It is also the nation's first national laboratory, chartered in 1946.

TOP500 Supercomputer Sites
A project started in 1993 to provide a reliable basis for tracking and detecting trends in high-performance computing. 

HPC Stakeholders

 Click here to expand...

NYU Research Technology Services (RTS) supports and encourages a model of hosting and managing clusters for research groups or departments in return for making unused cluster cycles available to the general NYU research community. These research groups and departments are our HPC Stakeholders, for whom NYU HPC manages hardware and provides priority access. Our current stakeholders are CGSBCNSCDSKussell Lab and CAOS.

If you are interested in becoming a stakeholder, please contact us at for details before you purchase your cluster. We can discuss your needs and work with you in the planning and purchase of hardware.


Compute and Storage Facilites

 Click here to expand...

[ Prince ] [ Mercer  ] [ Dumbo ]

The NYU HPC team currently maintains three clusters: The HPC cluster Prince, the HPC cluster Mercer, and the Hadoop cluster Dumbo.

HPC user accounts

An HPC User account provides access to all three clusters maintained by the NYU HPC team. If you don't have an account, you may apply for an HPC user account.

Old HPC clusters

NYU HPC team has retired its older clusters (Union Square, Cardiac, and Bowery). The current production HPC cluster is Mercer.

  • Prince

    Prince is the new HPC cluster that is currently being deployed. Prince will replace the HPC Mercer Cluster.

  • Mercer 

    Mercer has 4 login and 394 compute nodes:

    Number of nodesCPU type and speedNumber of cores per nodeGPUs per nodeTotal memory per nodeMemory available to jobsNode namesNode set name 
    112Intel Xeon E-2690v2 (Ivy Bridge) x86_64 3.0GHz (2014)20 64GB62GBcompute-14-* to compute-20-*


    48Ivy Bridge x86_64 3.0GHz (2014)20 192GB189GBcompute-21-* to compute-23-*ivybridge_20p_192GB_3000MHz
    68Westmere x86_64 2.67GHz (2010)12 24GB23GBcompute-4-* to compute-8-7westmere_12p_24GB_2670MHzdell_westmere
    8Westmere x86_64 2.67GHz (2010)12 48GB46GBcompute-8-8 to compute-8-15westmere_12p_48GB_2670MHz
    16Westmere x86_64 2.67GHz (2010)12 96GB93GBcompute-9-*westmere_12p_96GB_2670MHz
    64Westmere x86_64 3.07GHz (2011)12 48GB46GBcompute-12-* and compute-13-*westmere_12p_48GB_3070MHz 
    64Nehalem x86_64 2.67GHz (2009)8 24GB23GBcompute-0-* to compute-3-*nehalem_8p_24GB_2670MHz 
    1Nehalem x86_64 2.27GHz (2009)16 256GB250GBcompute-10-0nehalem_16p_256GB_2270MHz 
    1Westmere x86_64 2.67GHz (2011)32 1TB1000GBcompute-10-1westmere_32p_1024GB_2670MHz 
    4Westmere x86_64 2.67GHz (2011)121 x NVidia Tesla M702024GB23GBcompute-11-*westmere_12p_24GB_2670MHz_Teslatesla
    9Sandy Bridge x86_64 2.0GHz (2014)164 x NVidia Titan128GB126GBgpu-23-*sandybridge_16p_128GB_2000MHz_Titantitan

    To restrict a job to a specific subset of nodes, you can request the node set name as a feature, eg:

    #PBS -l feature=ivybridge_20p_64GB_3000MHz

    You can see a map of nodes and usage with pbstop. The diagram below indicates which nodes belong to which of the above nodesets:

  • Dumbo

    Dumbo is a 44 data node Hadoop cluster running Cloudera Distribution of Hadoop (CDH).

    • For a detailed description of dumbo and how to access it, please see the dumbo wiki pages.

The NYU HPC clusters have five filesystems for users' files. Each filesystem is configured differently to serve a different purpose:




Space Purpose


Backed up?



Cost for Additional

Total Size

File System


Program development space; storing small files you
want to keep long term , e.g. source code, scripts.

login and compute nodes.

Starting with the installation of Mercer we have a unified /home filesystem served from same 7420 storage system as /archive and /work


ASCII filenames only 


20GB (unified /home, mounted on Mercer)



600TB (unified /home, space shared with /archive and /work)




Long-term storage, mounted only on login nodes.

Best for large files, please tar collections of small files when archiving.

Groups may request a common aggregate archive space.

login nodes only.

Common to all clusters.


ASCII filenames only



$500/year for 1TB


shared with /work and unified /home



Computational work space. Best suited to large, infrequent reads and writes.

Files are deleted after 60 days without use.

login and compute nodes.

Common to all clusters.


Files not accessed
for 60 days

inode quota: 1 million





Medium term, non-backed up storage mounted on login and compute nodes.

login and compute nodes.

shared with /archive and unified /home

Small, node-local filesystem cleaned up at the end of each Torque job. For small, frequent reads and writes.

Environment variable is defined in batch jobs (via qsub wrapper)

compute nodes only. Local to each compute node.

NoEnd of each jobVaries. Generally >100GBN/AVariesext3
 $PBS_MEMDISKOptional, node-local memory filesystem. Like $PBS_JOBTMP but smaller and faster. See here for usage.compute nodes only. Local to each compute node.NoEnd of each jobDefault 8GB. Specific amount can be requested (but must fit within node memory)N/AVariestmpfs or ramfs

 Only files and directories with ASCII-only filenames are backed up. Our backup system does not handle unicode in file or directory names, such files and directories (including all files and directories under them) will be bypassed.

Important: Of all the space, only /scratch should be used for computational purposes. Please do not write to /home when running jobs as it can easily be filled up.

*Note:  Capacity of the /home file system varies from cluster to cluster. Unlike /scratch and /archive, the /home file system is not mounted across clusters. Each cluster has its own /home, its own user base and /home allocation policy.   

To purchase additional storage, send email to

See Clusters and Storage for more information.

Logging in to the NYU HPC Clusters 

 Click here to expand...

The HPC clusters (Prince, Mercer and Dumbo) are not directly visible to the internet (outside the NYU Network). If you are outside NYU's Network (off-campus) you must first login to a bastion host named or

The diagram below illustrates the login path.

NOTE: The clusters can still access the internet directly. This may be useful when copying data from servers outside the NYU Network - see: How to copy files to and from the HPC clusters.

NOTE: Alternatively, instead of login to the bastion host, you can use VPN to get inside NYU's network and access the HPC clusters directly. Instructions on how to install and use the VPN client are available here.

NOTE: You can't do anything on the bastion host, except ssh to the HPC clusters.


In a nutshell

  • From within the NYU network, that is, from an on-campus location, or after you VPN inside NYU's network, you can login to the HPC clusters directly
    To login to the HPC cluster Prince, simply use (replace NYUNetID with your NetId).: 

    To login to the HPC cluster Mercer

    or, to login in to the Hadoop cluster (Dumbo)

  • From an off-campus location (outside NYU-NET), logging in to the HPC clusters is a two-step process:
    1. First login to the bastion host, or From a Mac or Linux workstation, this is a simple terminal command (replace NYUNetID with your NetId). Your password is the same password you use for NYU Home:

      You can't do anything on the bastion host, except ssh to the cluster

    2. Next login to the cluster. For Prince, this is done with:

      For Mercer, this is done with:

      For Dumbo, this is done with:



The full story

You need to ensure your workstation has the necessary software and settings to connect to the clusters and to use graphical interfaces. Here are instructions for preparing your workstation and logging in from a Windows /  Linux /  Mac  .

SSH tunneling for easier login and data transfer

The two-stage access can be inconvenient, especially when transferring files to and from the clusters. Secure direct access and file transfer is possible by setting up SSH tunneling from your workstation to the HPC clusters. We have instructions on setting this up for  Windows / Linux / Mac workstations.

What can I do on the login node?

The login nodes (prince, mercer and dumbo) are for preparing, submitting and monitoring scripts, analyzing results, moving data around and code development and simple compilation. Login nodes are Not suitable for running computational workloads! - for Prince use this batch system. For Mercer use this batch system.

Compiling a large source codebase, especially with heavy use of optimization or -ipo (interprocedural optimization), can use much memory and CPU time. In such circumstances it is best to use the batch system for compilation too, perhaps via an interactive batch job. Click here for more info about interactive batch jobs.

Finding and Using Software

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A variety of commercial and open-source software is available on the NYU HPC clusters, and can be accessed via Environment Modules.


The login nodes are not suitable for computational work, they are a limited and shared resource for preparing and submitting computational jobs, developing and compiling software, and managing data. Computational work should instead be run via the batch system.

 Using NYU software on your own computer

NYU HPC hosts licenses for a number of commercial software packages which are suitable for workstation as well as HPC use, such as Matlab, COMSOL and Mathematica.  Contact us about accessing these packages.

 Getting new software installed on the HPC clusters

If you need a free or open source software package which is not currently available on the HPC clusters, contact us. Usually we can install it for you, or suggest an alternative which is already available.

Our ability to buy and install commercial software depends on the cost and on how widely it will be used. We may also be able to host licenses or share costs with you in return for making the software available also to the NYU research community, so if you need a specific commercial package contact us to discuss it.

 Compiling and developing software

Intel and GNU compilers are available on the clusters. For most code, we recommend the Intel compilers 

For debugging we have the GNU debugger gdb, the Intel debugger idb and Totalview by Roguewave. Debugging is best performed with an interactive batch session.

There is more about compiling and debugging on the old wiki pages.

 Usage examples on Mercer

 There are usage examples for many popular software packages in /share/apps/examples on Mercer:

  • batch - An example batch job
  • blcr  - Checkpoint-Restart facility for long jobs
  • comsol  - Computational Fluid Dynamics
  • c-sharp  - Language for the .NET/mono runtime environment
  • fluent  - Computational Fluid Dynamics / Multiphysics package
  • gaussian - Chemistry package
  • matlab  - For mathematical exploration
  • namd  - Molecular dynamics
  • qchem-amber  - Molecular dynamics
  • r  - Interpreted language for statistics work
  • resource-usage  - Shows minute-by-minute CPU and memory usage of a program
  • stata - Statistics package

Managing data: Storage, collaboration and moving data around

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Filesystems, their optimal usage and your space allocation are described under Storage.


On Mercer, enter 'myquota' at the prompt to see how much space you have used and available on each filesystem.  

Security and collaboration: file permissions and ACL on NYU HPC clusters

By default, only you can edit, or even see, your files. You can grant permission for your colleagues to see or edit files with setfacl, and you can check the permissions on a file or directory with getfacl.

An access control list (or ACL) gives per-file, per-directory and per-user control over who can read, write and execute files. You can see the ACL for a file or directory with the getfacl command:

$ getfacl myfile.txt

To modify permissions for files or directories, use setfacl. For a detailed description, see 'man setfacl'. In the example below, I give read permission on dummy.txt to user bob123:

$ setfacl -m u:bob123:r myfile.txt

 For setting execute permission on files - useful for scripts, and for allowing directories to be entered - chmod is still used.


Transferring files to and from the HPC clusters

To copy data between your workstation and the NYU HPC clusters, you must set up and start an SSH tunnel on the workstation. We have instructions for this for Windows, Mac and Linux workstations.

Once you have an SSH tunnel, you can transfer files to and from the HPC clusters - including  BuTinah at NYUAD.

Submitting jobs with sbatch: How to use the batch system

 Click here to expand...


  • HPC workloads are usually better suited to batch processing than interactive working.
  • A batch job is sent to the system (submitted) with sbatch.
  • Comments at the start of the script, which match a special pattern (#SBATCH) are read as Slurm options

Batch vs interactive

The working pattern we are all familiar with is interactive - I type (or click) something, and the computer performs the associated action. Then I type (or click) the next thing.

You may recall this from the first tutorial.

The trouble with interactive environments

There is another reason why GUIs are less common in HPC environments: point-and-click is necessarily interactive. In HPC environments (as we'll see in session 3) work is scheduled in order to allow exclusive use of the shared resources. On a busy system there may be several hours wait between when you submit a job and when the resources become available, so a reliance on user interaction is not viable. In Unix, commands need not be run interactively at the prompt, you can write a sequence of commands into a file to be run as a script, either manually (for sequences you find yourself repeating frequently) or by another program such as the batch system.

The job might not start immediately, and might take hours or days, so we prefer a batch approach:

  • plan the sequence of commands which will perform the actions we need
  • write them into a script

I can now run the script interactively, which is a great way to save effort if I frequently use the same workflow, or ...

  • submit the script to a batch system, to run on dedicated resources when they become available

Where does the output go?

  • The batch system writes stdout and stderr from a job to a file named "slurm-12345.out"
    • Which you can change, using sbatch options
  • While a job is running, it caches the stdout and stderr in the job working directory
  • You can use redirection (See Tutorial 1) to send output of a specific command into a file

Writing and Submitting a job

There are two aspects to a batch job script:

  • A set of SBATCH directives describing the resources required and other information about the job 
  • The script itself, comprised of commands to setup and perform the computations without additional user interaction

A simple example

A typical batch script on an NYU Prince cluster looks something like these:

Using precompiled third-party software
Using self-developed or built software

We'll work through them more closely in a moment.

You submit the job with sbatch:

$ sbatch myscript.s

And monitor its progress (as is discussed further in here) with:

$ squeue -u $USER

What just happened? Here's an annotated version of the first script:

The second script has the same SBATCH directives, but this time we are using code we compiled ourselves. Starting after the SBATCH directives:


Submitting a job

Jobs are submitted with the sbatch command:

$ sbatch options job-script

The options tell SLURM information about the job, such as what resources will be needed. These can be specified in the job-script as SBATCH directives, or on the command line as options, or both (in which case the command line options take precedence should the two contradict each other). For each option there is a corresponding SBATCH directive with the syntax:

#SBATCH option

For example, you can specify that a job needs 2 nodes and 4 cores on each node (by default one CPU core per task) on each node by adding to the script the directive:


#SBATCH --nodes=2
#SBATCH --ntasks-per-node=4


or as a command-line option to sbatch when you submit the job: 

$ sbatch --nodes=2 --ntasks-per-node=4 my_script.s

Options to manage job output:

  • -J jobname
    Give the job a name. The default is the filename of the job script. Within the job, $SBATCH_JOB_NAME expands to the job name
  • -o path/for/stdout
    Send stdout to path/for/stdout. The default filename is slurm-${SLURM_JOB_ID}.out, e.g. slurm-12345.out, in the directory from which the job was submitted 
  • -e path/for/stderr
    Send stderr to path/for/stderr.
    Send email to when certain events occur.
  • --mail-type=type
    Valid type values are NONE, BEGIN, END, FAIL, REQUEUE, ALL...

Options to set the job environment:

  • --export=VAR1,VAR2="some value",VAR3
    Pass variables to the job, either with a specific value (the VAR= form) or from the submitting environment (without "=")  
  • --get-user-env[=timeout][mode]
    Run something like "su  -  <username>  -c /usr/bin/env"  and parse the output. Default timeout is 8 seconds. The mode value can be "S", or "L" in which case "su" is executed with "-" option

Options to request compute resources:

  • -t, --time=time
    Set a limit on the total run time. Acceptable formats include  "minutes", "minutes:seconds",  "hours:minutes:seconds",  "days-hours", "days-hours:minutes" and "days-hours:minutes:seconds"
  • --mem=MB
    Maximum memory per node the job will need in MegaBytes
  • --mem-per-cpu=MB
    Memory required per allocated CPU in MegaBytes

  • -N, --nodes=num
    Number of nodes are required. Default is 1 node
  • -n, --ntasks=num
    Maximum number tasks will be launched. Default is one task per node

  • --ntasks-per-node=ntasks
    Request that ntasks be invoked on each node

  • -c, --cpus-per-task=ncpus
    Require ncpus number of CPU cores per task. Without this option, allocate one core per task

    Requesting the resources you need, as accurately as possible, allows your job to be started at the earliest opportunity as well as helping the system to schedule work efficiently to everyone's benefit.

Options for running interactively on the compute nodes with srun:

  • -nnum
    Specify the number of tasks to run, e.g. -n4. Default is one CPU core per task. 
    Don't just submit the job, but also wait for it to start and connect stdoutstderr and stdin to the current terminal
  • -ttime
    Request job running duration, e.g. -t1:30:00
  • --mem=MB
    Specify  the  real  memory  required  per  node in MegaBytes, e.g. --mem=4000
  • --pty
    Execute the first task in pseudo terminal mode, e.g. --pty /bin/bash, to start a bash command shell

  • --x11
    Enable X forwarding, so programs using a GUI can be used during the session (provided you have X forwarding to your workstation set up)
  • To leave an interactive batch session, type exit at the command prompt.

Options for delaying starting a job:

  • -d, --dependency=dependency_list
    For example, --dependency=afterok:12345, to delay starting this job until the job 12345 has completed successfully.
  • --begin=time

    Delay starting this job until after the specified date and time, e.g. --begin=9:42:00, to start the job at 9:42:00 am.

Options for running many similar jobs:

  • -a, --array=indexes
    Submit an array of jobs with array ids as specified. Array ids can be specified as a numerical range, a comma-separated list of numbers, or as some combination of the two. Each job instance will have an environment variable SLURM_ARRAY_JOB_ID and SLURM_ARRAY_TASK_ID. For example:
    --array=1-11, to start an array job with index from 1 to 11
    --array=1-7:2, to submit an array job with index step size 2
    --array=1-9%4, to submit an array job with simultaneously running job elements set to 4
  • The srun command is similar to pbsdsh. It launches tasks on allocated resources.

Tutorials, FAQs and how to get help

 Click here to expand...

For help with any aspect of scientific or high performance computing on the NYU HPC clusters, email us at

We are developing a set of tutorials to help NYU HPC users make the most of the facilities. Tutorials are suitable for self-directed learning and are also periodically run as classes in the library. NYU Data Services also provides tutorials for a range of scientific software - for dates and times of upcoming HPC classes see the calendar on the left, or see NYU Data Services for a wider schedule of classes.

Currently available HPC tutorials are:

Tutorial 0: Introduction to Unix/Linux

Tutorial 1: A Hands-On introduction to Unix/Linux

Tutorial 2: Getting Started in the NYU HPC environment

Tutorial 3: Using NYU HPC Effectively

The NYU HPC qsub tutorial is also available, covering:


Getting Started on Dumbo: How to login

Tutorial 1: MapReduce

Tutorial 2: Hive

Tutorial 3: Spark



Something went wrong!

Why does running "ls" on /scratch take so long?

I can't login

When trying to login, I get warnings about "HOST IDENTIFICATION HAS CHANGED"

What happened to my data on /scratch?

In the library, my wireless connection keeps dropping out. How can I fix it?

I'm getting a "module: command not found" error

Warning: no access to tty (Bad file descriptor), Thus no job control in this shell

I get an error "Warning: no display specified." when I use -X flag with ssh

Who killed my job, and why?

I got an email "Please do not run jobs on login nodes"

Running jobs

What resources can and should I request?

Can I make sure a job gets executed only after another one completes?

How do I log in to a specific node?

How can I make sure my job is running smoothly?

My job will take longer than 48 hours, what should I do?

My job needs (MySQL, some other service) to be running

I want to run a job at 9am every day

Using software

How do I run ... (esp, needs a license)

a STATA job?

a Gaussian job?

a Matlab job?

a parallel, non MPI job (eg Julia)?

I can't find (some software package)

Can you install (some software package)?

How can I view a PDF file on Mercer?

Managing data

How much of my file/space quota have I used?

How do I give my colleague access to my files?

How do I get the best transfer speed to or from BuTinah?

I have a huge amount of data that I want to compress for storage or transfer

Monthly Maintenance Window

 Click here to expand...

To provide the best possible service, ITS must regularly update and perform routine maintenance on its systems and networks. Some of these activities require that the affected systems and networks be shut down. While this work is essential, we also recognize that it presents an inconvenience. To enable those who use these systems to better plan for maintenance, we have guidelines for scheduling routine maintenance and upgrades to the HPC clusters as described below.


Major scheduled maintenance and upgrade activities will take place, if needed, once per month.  These will be scheduled for the first Monday of each month at 8am to noon to start these scheduled maintenance and upgrade activities. The maintenance period may often be brief or not used at all, but can last up to 12 hours if this amount of time is needed to complete the work.

We have chosen early morning on the first Monday of each month for our maintenance work as it has been the time period during the week which has low usage on our clusters.

A notification will be sent to all HPC account holders announcing any scheduled maintenance work in advance.


This time will not be used if not needed.

Featured Research

An Event-Driven Model for Estimation of Phase-Amplitude Coupling at Time Scales of Cognitive Phenomena

Signal processing in neural science includes a wide variety of algorithms and methods of applied measurement that can produce very powerful correlations between the brain's computational ensemble of signals and the neurophysiological mechanisms that generate these signals. The sheer complexity and volume of the brain's electrical and chemical computational environment makes accurate detection of distinct brain wave oscillations a very difficult task for neuroscientists looking to justify analytical correlations of this traffic to any of the brain's computational mechanisms.

Recently, at NYU's Center for Neural Science, Dr. Andre Fenton and Ph.D student Dino Dvorak developed a new approach to phase-amplitude coupling (PAC) estimation between distinct neural oscillations which treats each oscillation as a discrete event rather than continuous time series of phase and amplitude. The approach proposes "oscillation-triggered coupling" (OTC) as a unified framework for PAC estimation that provides a parameter-free, data-driven analysis for time windows that are considerably smaller than current, standard PAC estimation methods. This new framework provides the same information about PAC estimates as current methods (which require analysis windows of at least 10 seconds) while providing new insight toward proper PAC estimates at time scales which are on the order of a single modulation signal cycle. (read more)This diagram shows the schematic decomposition of the global-scale analytical windows used for standard PAC estimation and the local-scale, OTC analysis detailed by Dr. Fenton and Ph.D student Dino Dvorak. The following graphs show the analytical process for interpreting the raw signals and generating PAC estimates within the OTC framework.

More featured research using NYU HPC

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