Welcome to the HPC User Wiki of the University of Oldenburg

Note: This is a first, preliminary version (v0.01) of the HPC User Wiki. Its primary purpose is to get you started with our new clusters (FLOW and HERO), enabling you to familiarize with these systems and gather some experience. More elaborate, updated versions will follow, so you may want to check these pages regularly.

Introduction

Presently, the central HPC facilities of the University of Oldenburg comprise three systems:

• FLOW (Facility for Large-Scale COmputations in Wind Energy Research)
  IBM iDataPlex cluster solution, 2232 CPU cores, 6 TB of (distributed) main memory, QDR InfiniBand interconnect (theoretical peak performance: 24 TFlop/s).

• HERO (High-End Computing Resource Oldenburg)
  Hybrid system composed of two components:
  • IBM iDataPlex cluster solution, 1800 CPU cores, 4 TB of (distributed) main memory, Gigabit Ethernet interconnect (theoretical peak performance: 19.2 TFlop/s),
  • SGI Altix UltraViolet shared-memory system ("SMP" component), 120 CPU cores, 640 GB of globally addressable memory, NumaLink5 interconnect (theoretical peak performance: 1.3 TFlop/s).

• GOLEM: older, AMD Opteron-based cluster with 390 cores and 800 GB of (distributed) main memory (theoretical peak performance: 1.6 TFlop/s).

FLOW and HERO use a common, shared storage system (high-performance NAS Cluster) with a net capacity of 130 TB.

FLOW is employed for computationally demanding CFD calculations in wind energy research, conducted by the Research Group TWiST (Turbulence, Wind Energy, and Stochastis) and the ForWind Center for Wind Energy Research. It is, to the best of our knowledge, the largest system in Europe dedicated solely to that purpose.

The main application areas of the HERO cluster are Quantum Chemistry, Theoretical Physics, and the Neurosciences and Audiology. Besides that, the system is used by many other research groups of the Faculty of Mathematics and Science and the Department of Informatics of the School of Computing Science, Business Administration, Economics, and Law.

Hardware Overview

FLOW

• 122 "low-memory" compute nodes: IBM dx360 M3, dual socket (Westmere-EP, 6C, 2.66 GHz), 12 cores per server, 24 GB DDR3 RAM, diskless (host names cfdl001..cfdl122).

• 64 "high-memory" compute nodes: IBM dx360 M3, dual socket (Westmere-EP, 6C, 2.66 GHz), 12 cores per server, 48 GB DDR3 RAM, diskless (host names cfdh001..cfdh064).

• QDR InfiniBand interconnect (fully non-blocking), 198-port Mellanox IS5200 IB switch (can be extended up to 216 ports).

• Gigabit Ethernet for File-I/O etc.

• 10/100 Mb/s Ethernet for management and administrative tasks (IPMI).

HERO

• 130 "standard" compute nodes: IBM dx360 M3, dual socket (Westmere-EP, 6C, 2.66 GHz), 12 cores per server, 24 GB DDR3 RAM, 1 TB SATAII disk (host names mpcs001..mpcs130).

• 20 "big" compute nodes: IBM dx360 M3, dual socket (Westmere-EP, 6C, 2.66 GHz), 12 cores per server, 48 GB DDR3 RAM, RAID 8 x 300 GB 15k SAS (host names mpcb001..mpcb020)

• Gigabit Ethernet II for communication of parallel jobs (MPI, LINDA, ...).

• Second, independent Gigabit Ethernet for File-I/O etc.

• 10/100 Mb/s Ethernet for management and administrative tasks (IPMI).

• SGI Altix UV 100 shared-memory system, 10 CPUs (Nehalem-EX, "Beckton", 6C, 2.66 GHz), 120 cores in total, 640 GB DDR3 RAM, NumaLink5 interconnect, RAID 20 x 600 GB SAS 15k rpm (host uv100).

The 1 Gb/s leaf switches have uplinks to a 10 Gb/s backbone (two switches, redundant). The central management interface of both clusters runs on two master nodes (IBM x3550 M3) in an HA setup. Each cluster has two login nodes (IBM x3550 M3).

Operating system: Scientific Linux 5.5

Cluster management software: Bright Cluster Manager 5.1 by ClusterVision B.V.

Basic Usage

Logging in to the system

From within the University (intranet)

Within the internal net of the University, access to the systems is granted via ssh. Use your favorite ssh client like OpenSSH, PuTTY, etc. For example, on a UNIX/Linux system, users of FLOW may type on the command line (replace "abcd1234" by your own account):

ssh abcd1234@flow.hpc.uni-oldenburg.de

Similarly, users of HERO login by typing:

ssh abcd1234@hero.hpc.uni-oldenburg.de

Use "ssh -X" for X11 forwarding (i.e., if you need to export the graphical display to your local system).

For security reasons, access to the HPC systems is denied from certain subnets. In particular, you cannot login from the WLAN of the University (uniolwlan) or from "public" PCs (located, e.g., in Libraries, PC rooms, or at other places).

From outside the University (internet)

First, you have to establish a VPN tunnel to the University intranet. After that, you can login to HERO or FLOW via ssh as described above. The data of the tunnel are:

Gateway       : vpn2.uni-oldenburg.de
Group name    : hpc-vpn
Group password: hqc-vqn

Cf. the instructions of the IT Services on how to configure the Cisco VPN client. For the HPC systems, a separate VPN tunnel has been installed, which is only accessible for users of FLOW and HERO. Therefore, you have to configure a new VPN connection and enter the data provided above. For security reasons, you cannot login to FLOW or HERO if you are connected to the intranet via the "generic" VPN tunnel of the University.

Compiling and linking

This section will be elaborated later and then provide more detailed information. For the time being, we only give a very brief overview on how to invoke the compilers and linkers and generate executables.

Documentation

• C/C++ Compiler

• Getting started tutorial

• Fortran compiler User and Reference Guides

Parallel (MPI) programs

Two methods:

• wrapper script (usually preferred method, since it takes of all compiler flags etc.

• using linker options

Job Management (Queueing) System

The queueing system employed to manage user jobs for FLOW and HERO is Sun Grid Engine (SGE). For first-time users (especially those acquainted with PBS-based systems), some features of SGE may seem a little unusual and certainly need some getting-accustomed-to. In order to efficiently use the available hardware resources (so that all users may benefit the most from the system), a basic understanding of how SGE works is indispensable. Some of the points to keep in mind are the following:

• Unlike other (e.g., PBS-based) queueing systems, SGE does not "know" the concept of "nodes" with a fixed number of CPUs (cores) and users specifying the number of nodes they need, along with the number of CPUs per node, in their job requirements. Instead, SGE logically divides the cluster into slots, where each "slot" may be thought of as a single CPU core. The scheduler assigns free slots to pending jobs. Since in the multi-core area each host offers many slots, this will, in general, lead to jobs of different users running concurrently on the same host (provided that there are sufficient resources like memory, disk space etc. to meet all requirements of all jobs, as specified by the users who submitted them) and usually guarantees efficient resource utilization.

• While the scheduling behavior described above may be very efficient in optimally using the available hardware resources, it will have undesirable effects on parallel (MPI, LINDA, ...) jobs. E.g., an MPI job requesting 24 slots could end up running 3 tasks on one host, 12 tasks on another host (fully occupying this host, if it is a server with 2 six-core CPUs, as happens with our clusters), and 9 tasks on a third host. Clearly, such an unbalanced configuration may lead to problems. For certain jobs (multithreaded applications) it is even mandatory that all slots reside on one host (typical examples: OpenMP programs, Gaussian single-node jobs).
  To deal with the specific demands of parallel jobs, SGE offers so-called parallel environments (PEs) which are largely configurable. Even if your job does not need several hosts, but runs on only one host using several or all cores of that host, you must specify a parallel environment. It is of crucial importance to choose the "correct" parallel environment (meeting the requirements of your application/program) when submitting a parallel job.

• Another "peculiarity" of SGE (as compared to its cousins) are the concepts of cluster queues and queue instances. Cluster queues are composed of several (typically, many) queue instances, with each instance associated with one particular host. A cluster queue may have a name like, e.g., standardqueue.q, where the .q suffix is a commonly followed convention. Then the queue instances of this queue has names like, e.g. standardqueue.q@host001, standardqueue.q@host002, ... (note the "@" which acts as a delimiter between the queue name and the queue instance).
  In general, each host will hold several queue instances belonging to different cluster queues. E.g. there may be a special queue for long-running jobs and a queue for shorter jobs, both of which share the same "physical" machines but have different policies. To avoid oversubscription, resource limits can be configure for individual hosts. Since resource limits and other, more complex attributes can also be associated with cluster queues and even queue instances, the system is highly flexible and can be customized for specified needs. On the other hand, the configuration quickly tends to get rather complex, leading to unexpected side effects. E.g., PEs grab slots from all queue instances of all cluster queues they are associated with. Thus, a parallel job may occupy slots on one particular host belonging to different queue instances on that host. While this is usually no problem for the parallel job itself, it blocks resources in both cluster queues which may be unintended. For that reason, it is common practice to associate each PE with one and only one cluster queue and define several (possibly identically configured) PEs in order to avoid that a single PE spans several cluster queues.

Submitting jobs

Sample job submission scripts for both serial and parallel jobs are provided in the subdirectory Examples of your homedirectory. You may have to adapt these scripts as needed. Note that a job submission script consists of two principal parts:

• SGE directives (lines starting with the "magic" characters #$), which fall into three categories:
  • general options (which shell to use, name of the job, name of output and error files if differing from default, etc.). The directives are passed to the qsub command when the job is submitted.
  • Resource requirements (introduced by the -l flag), like memory, disk space, runtime (wallclock) limit, etc.
  • Options for parallel jobs (parallel environment, number of job slots, etc.)

• Commands to be executed by the job (your program, script, etc.), including the necessary set-up of the environment for the application/program to run correctly (loading of modules so that your programs find the required runtime libraries, etc.).

The job is submitted by the qsub command, e.g. (assuming your submission script is named"myprog.sge):

qsub -l myprog.sge

Specifying job requirements

The general philosophy behind SGE is that you should not explicitly submit your job to a specific queue or queue instance (although this is possible in principle), but rather define your requirements, and then let SGE decide which queue matches these requirements and where your job best runs in (taking into account the current load of the system and other factors). For this "automatic" queue selection to work efficiently, it is important that you specify your job requirements carefully. The following points are relevant to both serial and parallel jobs:

• Maximum (wallclock) runtime is specified by -l h_rt=<hh:mm:ss>. E.g., a maximum runtime of three days is defined by

  $# -l h_rt=72:0:0

  All cluster queues except the "long" queues have a maximum allowed runtime of 8 days. It is highly recommendable that you specify the runtime of your job as closely as possible and reasonable (leaving a margin of error, of course!). If the scheduler knows that, e.g., your pending job is a fast run (requiring, e.g., only a few hours) it is likely that it gets executed much earlier (the so-called backfilling mechanism).

• If your job needs more than 8 days of runtime, your submission script must contain a line like:

  $# -l longrun=TRUE

  It is then automatically transferred to one of the "long" queues, which have no runtime limit. The number of long-running jobs per user is limited.

• Maximum memory usage is defined by the h_vmem attribute, as in

  $# -l h_vmem=4G

  for a job requesting 4 GB of main memory. Note: the above attribute refers to the memory per job slot (CPU core), i.e. it gets multiplied by the number of slots the parallel job requested (see below).

• The standard compute nodes of HERO (mpcs001..mpcs130) offer 23 GB memory in total, whereas the "low-memory" nodes of FLOW (cfdl001..cfdl122) have a limit of 22 GB (these nodes are diskless, therefore the operating systems also resides in the RAM). If your job needs one of the "big" nodes of HERO (mpcb001..mpcb020) offering 46 GB of RAM you need to specify your memory requirements and set the Boolean attribute bignode to TRUE. Assuming, e.g., a parallel job in the SMP PE (see below) requesting 12 slots, which always runs on one host, this may look like:

  #$ -l h_vmem=3G

  $# -l bignode=TRUE

  Similarly, to request one of the "high-memory" nodes of FLOW (cfdh001..cfdh064) offering 46 GB of RAM, you must specify (assuming, e.g., a MPI job running 12 tasks per node):

  #$ -l h_vmem=3G

  $# -l highmem=TRUE

  in your submission script

• Specifying required local disk space of your job (HERO cluster only):

  #$ -l h_fsize=200G

  for requesting 200 GB of scratch space. The standard nodes offer a maximum of 900 GB of local disk space for all jobs running on them. If your job needs more than 900 GB scratch space, you must request one of the big nodes (offering 2100 GB of disk space) as in, e.g.:

  #$ -l h_fsize=1400G

  $# -l bignode=TRUE

Note that several of the above options may possibly have to be combined. For example, for a long job generating huge scratch files you have specify both -l longrun=TRUE and -l bignode=TRUE.

The path to the local scratch directory can be accessed in your job submission script via the $TMPDIR environment variable.

Choosing the parallel environment (PE)

For an MPICH2 parallel job using mpd as task manager and requesting 96 slots (cores), your submission script must contain the line

#$ -pe mpich2_mpd 96

It is also highly recommendable to turn on resource reservation:

#$ -R y

This avoids starving of parallel jobs by serial jobs which "block" required slots on specific hosts.

The following PEs are available:

• Jobs having a walltime less than or equal to 8 days:
  • mpich

Running parallel programs

Running parallel programs

SMP

LINDA

... tbc ...

Monitoring and managing your jobs

qstat

qstat -j <jobid> to get a more verbose output, which is particularly useful when analyzing why your job won't run.

qdel

qalter

qhost

Application Software and Libraries

Computational Chemistry

MOLCAS

not yet installed ... tbc ...

MOLPRO

not yet installed

Advanced Usage

Here will you will find, among others, hints how to analyse and optimize your programs using HPC tools (profiler, debugger, performance libraries), and other useful information.

... tbc ...