Frank Mueller
North Carolina State University
PIs & Funding

NSF funding level: $550k

NCSU: $60k (ETF) + $20+k (CSC)

NVIDIA: donations ~$30k

PIs/co-PIs:
— Frank Mueller
— Vincent Freeh
— Helen Gu
— Xuxian Jiang
— Xiaosong Ma

Contributors:
— Nagiza Samatova
— George Rouskas
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ARC Cluster: In the News



“NC State is Home to the Most Powerful Academic HPC in North
Carolina” (CSC News, Feb 2011)
“Crash-Test Dummy For High-Performance Computing” (NCSU,
The Abstract, Apr 2011)
“Supercomputer Stunt Double” (insideHPC, Apr 2011)
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Purpose

Create a mid-size computational infrastructure to support
research in areas such as:
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Researchers Already Active
In the first week of public access:

From groups from within NCSU:
— CSC, ECE, Chem/Bio Engineering, Materials,
Operations Research

ORNL

Tsinghua University, Beijing, China
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System Overview
Head/Login Nodes
PFS Switch Stack
Compute/Spare
GEther Switch Stack
Nodes
SSD+
SATA
Front Tier
I/O Nodes
Storage Array
IB Switch Stack
Interconnect
Mid Tier
Back Tier
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Hardware


108 Compute Nodes
— 2-way SMPs with AMD Opteron 6128
processors with 8 cores per socket
— 16 cores per node!
— 32 GB DRAM per node
1728 compute cores available
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Interconnects

Gigabit Ethernet

40Gbit/s Infiniband (OFEDstack)
— interactive jobs, ssh, service
— MPI Communication
— Home directories
— Open MPI, MVAPICH
— IP over IB
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GPUs
NVIDIA Tesla C2050 (1 login + 36 nodes)
—
448 Compute cores per GPU
—
Peak GigaFLOPS 515 SP/1030 DP
—
Memory Amount 3GB
—
Memory Interface 384-bit
—
Memory Bandwidth (GB/sec) 144
NVIDIA GTX480 (10 nodes)
—
480 Compute cores per GPU
—
Peak GigaFLOPS 1344.96 SP/ 168DP
—
Memory Amount 3GB
—
Memory Interface 384-bit
—
Memory Bandwidth (GB/sec) 177.4
NVIDIA Tesla C2070 (2 nodes)
—
448 Compute cores per GPU
—
Peak GigaFLOPS 515 SP/1030 DP
—
Memory Amount 6GB
—
Memory Interface 384-bit
—
Memory Bandwidth (GB/sec) 144
NVIDIA 1060 GTX (1 node)
NVIDIA 8800 GTX (1 node)
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Solid State Drives

All 108 compute nodes equipped with
OCZ RevoDrive 120GB SSD
—
—
—
—
Read: Up to 540 MB/s
Write: Up to 480 MB/s
Sustained Write: Up to 400 MB/s
Random Write 4KB (Aligned): 75,000 IOPS
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File Systems


Available Today:
— NFS home directories over Gigabit Ethernet
— Local per-node scratch on spinning disks (ext3)
— Local per-node 120GB SSD (ext2)
In the future:
— Parallel File Systems
–Lustre
–Separate dedicated nodes are available for parallel filesystems
–1 MDS + 4 clients
Are you interested in helping us set this up for your research
projects??
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Power Monitoring


Watts Up Pro
— Serial and USB available.
Connected in groups of:
— Mostly 4 nodes (sometimes just 3)
— 2x 1 node
– 1 w/ GPU
– 1 w/o GPU
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Software Stack


Additional packages and libraries
— upon request but…
— Not free?  you need to pay
— License required?  you need to sign it
— Installation required?  you need to
–Test it
–Provide install script
check ARC website  constantly changing
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Base System



64bit Rocks 5.3 (based off of CentOS)
Batch system:
— Torque/Maui (PBS)
All compilers and tools are available on the login nodes.
— Gcc, gfortran, …
— Compute nodes share the same base OS and libraries
as the login nodes.
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MPI

Open MPI
— Operates over Infiniband
— Integrated with BLCR
— Already in your default PATH
–mpicc

MVAPICH
— Infiniband support
— Requires changes to your path. See ARC site.
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OpenMP

The "#pragma omp" directive in C programs works.
gcc -fopenmp -o fn fn.c
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CUDA SDK


Ensure you are using a node with a GPU
— Several types available to fine tune for your applications
needs:
–Well-performing single or double precision devices.
Requires environment changes:
export PATH=".:~/bin:/usr/local/bin:/usr/bin:$PATH“
export PATH="/usr/local/cuda/bin:$PATH“
export LD_LIBRARY_PATH="/usr/local/cuda/lib64:/usr/local/cuda/lib:$LD_LIBRARY_PATH“
export MANPATH="/usr/share/man:$MANPATH“
Or see site to make sure you have the latest paths…
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PGI Compiler (Experimental)

Awaiting site license update.
export PATH=".:~/bin:/usr/local/bin:/usr/bin:$PATH“
export PATH="/usr/local/cuda/bin:$PATH“
export LD_LIBRARY_PATH="/usr/local/cuda/lib64:/usr/local/cuda/lib“
export MANPATH="/usr/share/man“
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Virtualization


Goal: To allow a user to request VMs from the batch system just
like they would any other resource
— User gets full root access to each VM requested with
complete control over that VM.
— VMs will share the same network or may be grouped together
into private networks across single or multiple nodes.
Elegant VM creation scripts in place allow entire machine creation
in a single line.
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Job Submission




cannot SSH to a compute node
must use PBS to submit jobs
— Either as batch
— or interactively
Presently there are “hard” limits for job times and sizes. In
general, please be considerate of other users and do not abuse
the system.
There are special queues for nodes with a GPU
— As we add additional specialized resources even more queues
will become available.
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PBS Basics

On the login node:
— to submit a job: qsub …
— to list your jobs: qstat
— to list everyone’s jobs: qstat –a
— to delete/cancel/stop your job: qdel …
— to check node status: pbsnodes
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qsub Basics

qsub -q cuda ... # job submitted to GPU/CUDA queue

qsub -l ncpus=4 ... # ask for four tasks (processors) -- packed as up to 16 tasks per node

qsub -l nodes=4:ppn=16 ... # job for four nodes with 16 processors on each node (64 tasks)

qsub -l nodes=2:ppn=1 -q cuda ... # job for two tasks on two nodes with GPU/CUDA support

qsub -l nodes=2,cput=00:5:00 ... # job for two tasks + 5 minutes CPU time

to submit interactive: qsub -I # one node, shell will open up

to submit interactive: qsub -I -nodes=20 #two nodes w/ 20 tasks

to submit interactive: qsub -I -l host=compute-0-54.local #specifically on node 54

to submit interactive: qsub -I -l host=compute-0-54.local+compute-0-55.local #on 54+55

to submit interactive with X11: qsub -I -X ...
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Listing your nodes



Once your job begins, $PBS_NODEFILE points to a file that contains a list of your
requested nodes.
Open MPI is already integrated with PBS. Simply using mpirun … will automatically use all
requested processes directly from PBS.
For example, a CUDA programmer that wants to use 4 GPU nodes:
[dfiala@login-0-0 ~]$ qsub -I -lnodes=4:ppn=1 -qcuda
qsub: waiting for job 1774.arcs.csc.ncsu.edu to start
qsub: job 1774.arcs.csc.ncsu.edu ready
[dfiala@compute-0-2 ~]$ cat $PBS_NODEFILE
compute-0-2.local
compute-0-32.local
compute-0-35.local
compute-0-38.local
---SSHing between these nodes FROM the PBS session is allowed--23
Handling problems


If you find a node that is giving you trouble please report it to
the mailing list.
As a workaround, you can keep that node busy by queuing an
empty job:
echo sleep 600 | qsub -l host=compute-0-100,walltime=1000
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Hardware in Action

4 racks in server room
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Running Large Jobs (and keeping cool)

While our new cluster is surely state of the art…
Our “dual action”
cooling solution
for the state of
the art cluster
State of the art
cluster

The cooling system isn’t.
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Temperature Monitoring

It is the user’s responsibility to maintain
room temperatures below 80 degrees while
utilizing the cluster.
— ARC website has links to online browserbased temperature monitors.
— And the building staff have pagers that
will alarm 24/7 when temperatures exceed
the limit.
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Connecting to ARC


ARC access is restricted to on-campus IPs only.
— If you ever are unable to log in (connection gets dropped
immediately before authentication) then this is likely the
cause.
Non-NCSU users may request remote access by providing a
remote machine that their connections must originate from.
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Summary



Your ARC Cluster@Home: What can I do with it?
Primary purpose: Advance Computer Science Research (HPC and beyond)
— Want to run a job over the entire machine?
— Want to replace parts of the software stack?
Secondary purpose: Service to sciences, engineering & beyond
— Vision: Have domain scientists work w/ Computer Scientists on code
http://moss.csc.ncsu.edu/~mueller/cluster/arc/


Equipment donations welcome 
Ideas how to improve ARC?  let us know
— Qs?  send to mailing list (once you have an account)
— request an account: email dfiala<at>ncsu.edu
–Research topic, abstract, and compute requirements/time
– Must include your unity ID
– NCSU Students: Advisor sends email as means of their approval
– Non-NCSU: same + preferred username + hostname(your remote login location.
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Slides provided by David Fiala
Edited by Frank Mueller
Current as of May 11, 2011.
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