Main Page

Mission of CUNY-HPCC

The City University of New York (CUNY) High Performance Computing Center (HPCC) serves as a pivotal research and educational hub for the university. Situated on the campus of the College of Staten Island, located at 2800 Victory Boulevard, Staten Island, New York 10314, the center’s primary objective is to enhance educational opportunities and foster scientific research and discovery within the university. This is achieved through the management of state-of-the-art computing infrastructure and the provision of comprehensive research support services. Notably, CUNY-HPCC offers domain-specific expertise in various aspects of computationally intensive research. Furthermore, CUNY’s membership in the Empire AI (EAI) consortium positions CUNY-HPCC as a stepping stone for CUNY researchers seeking access to EAI advanced facilities.

Empire AI and CUNY-HPCC

The Empire AI Consortium comprises the CUNY Graduate Center, Columbia University, Cornell University, Icahn School of Medicine, New York University, Rochester Institute of Technology, Rensselaer Polytechnic Institute, the State University of New York, University at Buffalo, and University of Rochester. CUNY-HPCC provides support and maintains tickets for all CUNY users with allocation on EAI. Additionally, CUNY-HPCC serves as a stepping stone for CUNY researchers as it operates (on a smaller scale) architectures (including nodes with Hopper) similar to EAI, including extended “Alpha” servers and new “Beta” computers. The latter will consist of 288 B200 GPUs and recently added RTX 6000 Pro nodes. The anticipated cost for EAI is $0.50 per unit (SU), which will provide CUNY PIs with a rate that is significantly lower than a typical AWS rate. One SU corresponds to one hour of H100 compute, and one hour of B200 compute corresponds to two SU. In comparison, the CUNY-HPCC recovery costs for public servers are $0.015 per CPU hour (1 unit) and $0.09 per GPU hour (6 units). For further details, please refer to the section on HPCC access plans.

CUNY-HPCC services

CUNY-HPCC offers a professionally maintained, modern computational environment and architectures, along with advanced storage and fast interconnects. CUNY-HPCC serves the following purposes:

* Supports research computing at CUNY, benefiting faculty, their collaborators at other universities, and their public and private sector partners. It also supports CUNY students and research staff.

* Provides state-of-the-art computing resources and comprehensive research support services, including expertise and full support for users with allocation on EMPIRE-AI.

* Creates opportunities for the CUNY research community to establish new partnerships with the government and private sectors.

* Utilizes HPCC capabilities to acquire additional research resources for its faculty and graduate students in existing and major new programs.

* Maintains tickets for all CUNY users with allocation on EAI.

Organization of systems and data storage (architecture)

All user data and project data are kept on Parallel File System Storage (PFSS) which is mounted only on login node(s) of all servers. It holds both user directories and specific partition called /scratch (see below). The main features of /scratch partition are: 1. It is mounted on all computational nodes and on all login nodes 2. It is fast 3. /scratch partition is temporary space, but is not a home directory for accounts nor can be used for long term data preservation. Users must use "staging" procedure described below to ensure preservation of their data, codes and parameters files. The figure below is a schematic of the environment.

Upon registering with HPCC every user will get 2 directories:

• /scratch/<userid> – this is temporary workspace on the HPC systems. Currently scratch resides on the same file system as global/u.

• /global/u/<userid> – space for “home directory”, i.e., storage space on the DSMS for program, scripts, and data

• In some instances a user will also have use of disk space on the DSMS in /cunyZone/home/<projectid> (IRods).

The /global/u/<userid> directory has quota (see below for details) while the /scratch/<userid> do not have. However the /scratch space is cleaned up following the rules described below. There are no guarantees of any kind that files in /scratch will be preserved during the hardware crashes or cleaning up. Access to all HPCC resources is provided by bastion host called 'chizen. The Data Transfer Node called Cea allows file transfer from/to remote sites directly to/from /global/u/<userid> or to/from /scratch/<userid>

Computing architectures at HPCC

The HPC Center employs a diverse range of architectures to accommodate intricate and demanding workflows. All computational resources of various types are consolidated into a single hybrid cluster known as Arrow. This cluster comprises symmetric multiprocessor (SMP) nodes equipped with and without GPUs, distributed shared memory (NUMA) node(s), high-memory nodes, and advanced SMP nodes featuring multiple GPUs. The number of GPUs per node varies between two and eight, along with the utilized GPU interface and GPU family. Consequently, the fundamental GPU nodes are equipped with two Tesla K20m GPUs connected via the PCIe interface, while the most advanced nodes support eight Ampere A100 GPUs connected via the SXM interface.

Overview of Computational architectures:

SMP servers have several processors (working under a single operating system) which "share everything". Thus all cpu-cores allocate a common memory block via shared bus or data path. SMP servers support all combinations of memory VS cpu (up to the limits of the particular computer). The SMP servers are commonly used to run sequential or thread parallel (e.g. OpenMP) jobs and they may have or may not have GPU.

Cluster is defined as a single system comprising set of servers interconnected with high performance network. Specific software coordinates programs on and/or across those in order to perform computationally intensive tasks. The most common cluster type is the one that consists of several identical SMP servers connected via fast interconnect. Each SMP member of the cluster is called a node. All nodes run independent copies of the same operating system (OS). Some or all of the nodes may incorporate GPU.

Hybrid clusters combine nodes of different architectures. For instance the main CUNY-HPCC machine is a hybrid cluster called Arrow. Sixty two (62) of its nodes are identical GPU enabled SMP servers each with 2 x GPU K20m, 3 are SMP but with extended memory (fat nodes), one node is distributed shared memory node (NUMA, see below) and 2 are fat SMP servers especially designed to support 8 NVIDIA GPU per node. The latter are connected via SXM interface. In addition HPCC operates the cluster Herbert dedicated only to education.

Distributed shared memory computer is tightly coupled server in which the memory is physically distributed, but it is logically unified as a single block. The system resembles SMP, but the number of cpu cores and the amounts of memory possible is far beyond limitations of the SMP. Because the memory is distributed, the access times across address space are non-uniform. Thus, this architecture is called Non Uniform Memory Access (NUMA) architecture. Similarly to SMP, the NUMA systems are typically used for applications such as data mining and decision support system in which processing can be parceled out to a number of processors that collectively work on a common data. HPCC operates the NUMA node at Arrow named Appel. This node does not have GPU.

Infrastructure systems:

o Master Head Node (MHN/Arrow) is a redundant login node from which all jobs on all servers start. This server is not directly accessible from outside CSI campus. Note that name of main server and its login nodes are the same Arrow. Thus users can access the Arrow login nodes using name Arrow or MHN.

o Chizen is a redundant gateway server which provides access to protected HPCC domain.

o Cea is a file transfer node allowing transfer of files between users’ computers to/from /scratch space or to/from /global/u/<usarid>. Cea is accessible directly (not only via Chizen), but allows only limited set of shell commands.

Table 1 below provides a quick summary of the attributes of each of the sub clusters of the main HPC Center called Arow.

Master Head Node	Sub System	Tier	Type	Type of Jobs	Nodes	CPU Cores	GPUs	Mem/node	Mem/core	Chip Type	GPU Type and Interface
Arrow	Penzias	Advanced	Hybrid Cluster	Sequential & Parallel jobs w/wo GPU	66	16	2	64 GB	4 GB	SB, EP 2.20 GHz	K20m GPU, PCIe v2
				Sequential & Parallel jobs	1	24	-	1500 GB	62 GB	HL, 2.30 GHz	-
						36	-	768 GB	21 GB		-
						24	-	768 GB	32 GB		-
	Appel		NUMA	Massive Parallel, sequential, OpenMP	1	384	-	11 TB	28 GB	IB, 3 GHz	-
	Cryo		SMP	Sequential and Parallel jobs, with GPU	1	40	8	1500 GB	37 GB	SL, 2.40 GHz	V100 (32GB) GPU, SXM
	Blue Moon		Hybrid Cluster	Sequential and Parallel jobs w/wo GPU	24	32	-	192 GB	6 GB	SL, 2.10 GHz	-
					2	32	2				V100(16GB) GPU, PCIe
	Karle		SMP	Visualization, MATLAB/Mathematica	1	36*	-	768 GB	21 GB	HL, 2.30 GHz	-
	Chizen		Gateway	No jobs allowed	-
	CFD	Condo	SMP	Parallel, Seq, OpenMP	1	48	2	768 GB		EM, 4.8 GHz	A40, PCIe, v4
					1	48	-	512 GB		ER, 4.3 GHz	-
	PHYS	Condo	SMP		1	48	2	640 GB		ER, 4 GHz	L40, PCIe, v4
					1	48	-	512 GB		ER, 4.3 GHz	-
	CHEM	Condo	SMP		1	48	2	256 GB		EM, 2.8 GHz	A30, PCIe, v4
					1	128	8	512 GB		ER, 2.0 GHz	A100/40, SXM
	ASRC	Condo	SMP		1	48	2	256 GB		ER, 2.8 GHz	A30, PCIe, v4

Note: SB = Intel(R) Sandy Bridge, HL = Intel (R) Haswell, IB = Intel (R) Ivy Bridge, SL = Intel (R) Xeon(R) Gold, ER = AMD(R) EPYC ROMA, EM = AMD(R) EPYC MILAN, EG = AMD (R) EPYC GENOA

Recovery of operational costs

CUNY-HPCC, a not-for-profit core research facility affiliated with CUNY, is dedicated to supporting a wide range of research endeavors that necessitate advanced computational resources. Notably, CUNY-HPCC’s operations are not directly or indirectly funded by CUNY or the College of Staten Island (CSI). Consequently, CUNY-HPCC employs a cost recovery model that exclusively recoups operational expenses, without generating any profit for the HPCC. The recovered costs are meticulously calculated using comprehensive documentation of actual operational expenditures and are designed to achieve a break-even point for all CUNY users. This methodology is approved by CUNY-RF and is employed in other CUNY research facilities. The cost recovery charging schema is based on unit-hour usage, encompassing both CPU and GPU units. Definitions for these units are provided in the accompanying table.

Definitions of unit-hour

Type of resource	Unit-hour	For V100, A30, A40 or L40	For A100
CPU unit	1 cpu core/hour	--	--
GPU unit	(4 cpu cores + 1 GPU thread )/hour	4 cpu cores + 1 GPU	4 cpu cores and 1/7 A100

Compute on public resources

The cost recovery model for public (non-condominium) servers offers the following options:

1. Minimal Access Plan (MAP)
2. Compute on Demand (CODP)
3. Lease a node(s)

Minimal Access Plan (MAP)

Colleges may participate in any of the Minimal Access Plan tiers. The tiered pricing structure is as follows:

- Tier A: $5,000 per year

- Tier B: $10,000 per year

- Tier C: $25,000 per year

Within each tier, the cost is $0.015 per CPU hour and $0.09 per GPU hour. It is important to note that the Minimum Access Plan (MAP) tiers A, B, and C are not all-inclusive. Therefore, even if a college pays for a higher tier, it does not guarantee unlimited use of all college employees, faculty, and students throughout the year.

1. The MAP plan is indirectly linked to the number of hours used. Consequently, the definition of “up to 12 users for B tier” should not be interpreted as “all users up to 12 per college receive unlimited access to HPCC resources.”
2. The number of users per tier is determined by statistical analysis of resource usage and statistics for the average duration of a job across all CUNY institutions. This means that if the number of users from a college exceeds the number of hours encoded in the MAP fee, the additional hours will be charged at the preferred rate of $0.015 per CPU hour and $0.09 per GPU thread hour.
3. Furthermore, the MAP fee may fully cover the expenses for individuals from a given college for a year, but it may also not. This depends on the actual usage and type of resources, as well as the number of additional individuals from the same college who appear during the year. It is crucial to understand that allocation on HPCC is not restricted; our focus is solely on the completion of tasks.
4. The free 11,540 CPU hours and 1,440 GPU hours are allocated per PI account and project and are available only for MAP-B and MAP-C (see below). These free hours are intended to facilitate project establishment. Consequently, the PI can request that a member or members of a group utilize time and explore new research opportunities. For instance, upon creating his account the PI X will receive free hours. If he/she/whatever hires a graduate student(s), they may share these free hours if they work on the same project.
5. It is important to note that each GPU requires at least four CPU cores to operate. Therefore, if a user requests one GPU thread, it equates to one GPU thread and four CPU threads, which is equivalent to $0.15 per unit hour for that unit (units are explained above). Note that not all GPU support virtualization, so unit may include the whole GPU depend on used GPU type.

Compute on demand (CODP)

Users from colleges that do not participate in MAP (A,B,C) are charged $0.018 per CPU hour and $0.11 per GPU hour. There is no free time associated with CODP.

Lease a public node(s)

Users may lease a node(s) for project. That ensures them 100% access and no time or job limitations over leased resource. The minimum lease time is 30 days (one month). Longe leases (more than 90 days) have 10% discount. MAP users are charged between $172 and $950 per month (see below), depending on the type of node. Non-MAP users are charged between $249.58 and $1,399 per month, depending on the type of node. Please see below for details and examples.

Condo

Condo users only pay for infrastructure support. The annual fee depends on the type of node and ranges from $1,540 to $4,520 per year.

Lease on condo node(s)

Users may lease condo nodes for a project, ensuring them 100% access and eliminating any time or job limitations on the leased resource. The minimum lease duration is 30 days (one month). Longer leases (more than 90 days) receive a 10% discount. The lease of a condo node is contingent upon the owner of the required node agreeing to lease for a specific time period and duration. Users interested in leasing a condo node of a particular type must contact the HPCC director for options. Lease fees vary depending on the type of node and are currently (note that prices are reviewed once every six months) between $230 and $1100 per month.  

Storage

Storage costs are $60 per TB per year, backup costs are $45 per TB per year, and archive costs are $35 per TB per year. The first 50 GB of scratch storage are free. Prices are calculated at the end of each month.

1. Additionally, note that file transfers from/to HPCC are free, but it is important to consider the CUNY network speed, which is significantly slower than modern standards. This will impact the time required to download large data sets. For large data HPCC utilizes and recommend to use secure parallel download via Globus.
2. All services associated with data and storage provided by HPCC are free for CUNY users.

HPCC access plans details and examples

a.     Minimum access (MAP):

Minimum Access is designed to provide extensive support for research activities across various colleges, foster collaboration between institutions, facilitate the establishment of new research projects, and serve as a testing ground for innovative studies. MAP accounts operate under a stringent fair share policy, which determines the actual waiting time for job allocation in a queue based on the resources utilized by that account in previous cycles. Furthermore, all jobs are subject to strict time constraints. Consequently, extended jobs necessitate the implementation of checkpoints.

The MAP offers three tiers of access:

· A: The Basic tier incurs a yearly fee of $5,000. It is tailored to support users from colleges with limited research activities. The fee covers infrastructure expenses associated with one to two users from these colleges.

· B: The Medium tier incurs a yearly fee of $15,000. It covers infrastructure expenses for up to twelve users from these colleges. Additionally, every account under the Medium tier receives complimentary 11,520 CPU hours and 1,440 GPU hours upon account creation.

· C: The Advanced tier incurs a yearly fee of $25,000. It covers infrastructure expenses for all users from these colleges. Furthermore, every new account from this tier receives complimentary 11,520 CPU hours and 1,440 GPU hours upon account creation.  Minimum access is designed to provide wide support for research activities in any college, to promote collaboration between colleges, to help establish a new research project, and/or to be testbed for new studies. MAP accounts operate under strict fair share policy so actual waiting time for a job in a que depends on resources used by that account in previous cycles. In addition all jobs have strict time limitations. Therefore long jobs must use check-points.

The MAP users get charged per CPU/GPU hour at low rate of $0.015 per cpu hour and $0.09 per GPU hour.

Cost recovery fees for MAP users example

Job	Cpu cores	GPU	Cost/hour
1 core no GPU	1	0	$0.015/hour
16 cores no GPU	4	0	$0.24/hour
4 cores + 1 GPU	4	1	$0.15/hour
16 cores + 1 GPU	16	1	$0.33/hour
16 cores + 2 GPU	16	2	$0.42/hour
32 cores + 2 GPU	32	2	$0.66/hour
40 cores + 8 GPU	40	8	$1.32/hour

b.     Computing on demand (CODP)

Computing on demand plan (CODP) is open for all users from all CUNY colleges that do not participate in MAP plan, but want to use the HPCC resources. CODP accounts operate under strict fair share policy, so actual waiting time for a job in a que depends on resources previously used. In addition, all jobs have time limitations, so long jobs must use check-points. The users in CODP are charged for the time (CPU and GPU) per hour. The current rates are $0.018 per cpu hour and $0.11 per GPU hour.  In difference to MAP, the new CODP accounts does not come with free time. The invoices are generated and send to users (PI only) at the end of each month.  The examples in following table explain the fees structure:

Cost recovery fees for CODP plan

Job	Cpu cores	GPU	Cost/hour
1 core no GPU	1	0	$0.018/hour
16 cores no GPU	16	0	$0.288/hour
4 cores + 1 GPU	4	1	$0.293/hour
16 cores + 1 GPU	16	1	$0.334/hour
32 cores + 1 GPU	32	1	$0.666/hour
32 cores + 2 GPU	32	2	$0.756/hour

c. Leasing public node(s) (LNP)

Leasing node plan allows the users to lease the node(s) for the duration of the project. The minimum lease time is 30 days (one month), but leases of any length are possible. Discounts of 10% are given to users whose lease is longer than 90 days. Discounts cannot be combined.  In difference to MAP and CODP the LNP users do not compete for resources and have full access to rented resources 24/7.  

Fees for lease node(s) for MAP users

Job (MAP users)	Cpu cores	GPU	Cost/30 days
1 core no GPU	1	0	NA
16 cores no GPU	16	0	$172.80
32 cores no GPU	32	0	$264.96
16 cores + 2 GPU	16	1	$302.40
32 cores + 2 GPU	32	2	$475.20
40 cores + 8 GPU	40	8	$760.0
64 cores + 8 GPU	64	8	$950.40

Fees for lease a node(s) for non -MAP users

Job (non-MAP users)	Cpu cores	GPU	Cost/month
1 core no GPU	1	0	NA
16 cores no GPU	16	0	$249.82
32 cores no GPU	32	0	$497.64
16 cores + 1 GPU	16	2	$443.23
32 cores + 2 GPU	32	2	$886.64
40 cores + 8 GPU	40	8	$1399.68

d.     Condo Ownership (COP)

Condo describes a model when user(s) own a node/server managed by HPCC. Only full time faculty can own condo node. Condo nodes are fully integrated into HPCC infrastructure. The owners pay only HPCC’s infrastructure support operational fee which includes only proportional part of licenses and materials need for day-to-day operations. The fees are reviewed twice a year and currently are $0.003 per CPU hour and $0.02 per GPU hour. Condo owners can “borrow”  (upon agreement) free of charge any node(s) from condo stack and can also lease (for higher fee – see below) their own nodes to non-condo users. The minimum let time is 30 days. The fees collected from non-condo users offset payments of the owner.

Condo owners costs per year

Type of condo node	Cpu cores	GPU	Cost/year
Large hybrid SXM	128	8	$4518.92
Small hybrid	48	2	$1540.54
Medium compute	96	0	$2464.86
Large compute	128	0	$3286.49

Condo owners can contract their node(s) to other non-condo users. Renting period is unlimited with min. length of 30 days. The table below shows the payments the non-condo users recompense the condo owners. These fees are accumulated in owners account(s) and do offset the owner’s duties. Discount of 10% is applied for leases longer than 90 days.

Type of nodes and monthly lease fees for condo nodes

Type of node	Renters cost/month	Long term (90+ days) rent cost/month	CPU/node	CPU type	GPU/node	GPU type	GPU interface
Laghe Hybrid	$602.52	$564.86	128	EPYC, 2.2 GHz	8	A100/80	SXM
Small Hybrid	$205.41	$192.57	48	EPYC, 2.8 GHz	2	A40, A30, L40	PCIe v4
Medium Non GPU	$328.65	$308.11	96	EPYC, 4.11GHz	48	None	NA
Lagre Non GPU	$438.20	$410.81	128	EPYC, 2.0 GHz	128	None	NA

Free time

Any new project from CUNY colleges/centers that participate in MAP-B or MAP-C is entitled to get free 11520 CPU hours and 1440 GPU hours. Users under MAP-A are not entitled for free time. The free compute hours are intended to help to establish a project and thus are shared for all members of the project. Thus compute free hours can be used either by PI or by any number of project's members. It is important to note that free time is per project not per user account, so any project can have free time only once. External collaborators to CUNY are not normally eligible for free time. Additional hours beyond free time are charged with MAP plan rates. Please contact CUNY-HPCC director for further details.

Support for research grants

All proposals dated on Jan 1st 2026 ( 01/01/26 ) and later that require computational resources must include budget for cost recovery fees at CUNY-HPCC. For a project the PI can choose between:

• lease the node(s), That is useful option for well defined projects and those with high computational component requiring 100% availability of the computational resource.
• use "on-demand" resources. That is flexible option good for experimental projects or exploring new areas of study. The downgrade is that resources are shared among all users under fair share policy. Thus immediate access to resource cannot be guaranteed.
• participate in CONDO tier. That is most beneficial option in terms of availability of resources and level of support. It fits best the focused research of group(s) (e.g. materials science).

In all cases the PI can use the appropriate rates listed above to establish correct budget for the proposal. PI should contact the Director of CUNY-HPCC Dr. Alexander Tzanov (alexander.tzanov@csi.cuny.edu) and discuss the project's computational requirements including optimal and most economical computational workflows, suitable hardware, shared or own resources, CUNY-HPCC support options and any other matter concerning correct and optimal computational budget for the proposal.    

Partitions and jobs

The only way to submit job(s) to HPCC servers is through SLURM batch system. Any job despite of its type (interactive, batch, serial, parallel etc.) must be submitted via SLURM. The latter allocates the requested resources on proper server and starts the job(s) according to predefined strict fair share policy. Computational resources (cpu-cores, memory, GPU) are organized in partitions. The table below describes the partitions and their limitations. The users are granted permissions house one or other partition and corresponding QOS key. The table below shows the limitations of the partitions (in progress).

Partition	Max cores/job	Max jobs/user	Total cores/group	Time limits	Tier		GPU types
partnsf	128	50	256	240 Hours	Advanced		K20m, V100/16, A100/40
partchem	128	50	256	No limit	Condo		A100/80, A30
partcfd	96	50	96	No limit	Condo		A40
partsym	96	50	96	No limit	Condo		A30
partasrc	48	16	16	No limit	Condo		A30
partmatlabD	128	50	256	240 Hours	Advanced		V100/16,A100/40
partmatlabN	384	50	384	240 Hours	Advanced		None
partphys	96	50	96	No limit	Condo		L40

• partnsf is the main partition with assigned resources across all sub-servers. Users may submit sequential, thread parallel or distributed parallel jobs with or without GPU.
• partchem is CONDO partition.
• partphys is CONDO partition
• partsym is CONDO partition
• partasrc is CONDO partition
• partmatlabD partition allows to run MATLAB's Distributes Parallel Server across main cluster.
• partmatlabN partition to access large matlab node with 384 cores and 11 TB of shared memory. It is useful to run parallel Matlab jobs with Parallel ToolBox
• partdev is dedicated to development. All HPCC users have access to this partition with assigned resources of one computational node with 16 cores, 64 GB of memory and 2 GPU (K20m). This partition has time limit of 4 hours.

Hours of Operation

In order to maximize the use of resources HPCC applies “rolling” maintenance scheme across all systems. When downtime is needed, HPCC will notify all users a week or more in advance (unless emergency situation occur). Typically, the fourth Tuesday mornings in the month from 8:00AM to 12PM is normally reserved (but not always used) for scheduled maintenance. Please plan accordingly. Unplanned maintenance to remedy system related problems may be scheduled as needed out of above mentioned days. Reasonable attempts will be made to inform users running on those systems when these needs arise. Note that users are strongly encouraged to use checkpoints in their jobs.

User Support

Users are strongly encouraged to read this Wiki carefully before submitting ticket(s) for help. In particular, the sections on compiling and running parallel programs, and the section on the SLURM batch queueing system will give you the essential knowledge needed to use the CUNY HPCC systems. We have strived to maintain the most uniform user applications environment possible across the Center's systems to ease the transfer of applications and run scripts among them.

The CUNY HPC Center staff, along with outside vendors, offer regular courses and workshops to the CUNY community in parallel programming techniques, HPC computing architecture, and the essentials of using our systems. Please follow our mailings on the subject and feel free to inquire about such courses. We regularly schedule training visits and classes at the various CUNY campuses. Please let us know if such a training visit is of interest. In the past, topics have include an overview of parallel programming, GPU programming and architecture, using the evolutionary biology software at the HPC Center, the SLURM queueing system at the CUNY HPC Center, Mixed GPU-MPI and OpenMP programming, etc. Staff has also presented guest lectures at formal classes throughout the CUNY campuses.

If you have problems accessing your account and cannot login to the ticketing service, please send an email to:

 hpchelp@csi.cuny.edu 

Warnings and modes of operation

1. hpchelp@csi.cuny.edu is for questions and accounts help communication only and does not accept tickets unless ticketing system is not operational. For tickets please use the ticketing system mentioned above. This ensures that the person on staff with the most appropriate skill set and job related responsibility will respond to your questions. During the business week you should expect a 48h response, quite often even same day response. During the weekend you may not get any response.

2. E-mails to hpchelp@csi.cuny.edu must have a valid CUNY e-mail as reply address. Messages originated from public mailers (google, hotmail, etc) are filtered out.

3. Do not send questions to individual CUNY HPC Center staff members directly. These will be returned to the sender with a polite request to submit a ticket or email the Helpline. This applies to replies to initial questions as well.

The CUNY HPC Center staff members are focused on providing high quality support to its user community, but compared to other HPC Centers of similar size our staff is extremely lean. Please make full use of the tools that we have provided (especially the Wiki), and feel free to offer suggestions for improved service. We hope and expect your experience in using our systems will be predictably good and productive.

User Manual

The old version of the user manual provides PBS not SLURM batch scripts as examples. Currently CUNY-HPCC uses SLURM scheduler so users must check and use only the updated brief SLURM manual distributed with new accounts or ask CUNY-HPCC for a copy of the latter.