Manual Matlab - C Math Libary Users Guide 1.2

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Use Slurm's sbatch command to submit a job. Specify the resources needed for your job e. This file can contain both shell commands and special statements that include SBATCH options and resource specifications; shell commands other than the initial parser line e. Some of the most common options are described in Table 4 below and in the example job scripts.

Details are available online in man pages e. Options can be passed to sbatch on the command-line or specified in the job script file; we recommend, however, that you avoid using the "—export" flag because there are subtle ways in which it can interfere with the automatic propagation of your environment. As a general rule it is safer and easier to store commonly used SBATCH directives in a submission script that will be reused several times rather than retyping the options at every batch request.

In addition, it is easier to maintain a consistent batch environment across runs if the same options are stored in a reusable job script. All batch submissions MUST specify a time limit, number of nodes, and total tasks. Jobs that do not use the -t time , -N nodes and -n total tasks options will be rejected.

Batch scripts contain two types of statements: scheduler directives and shell commands in that order. Slurm stops interpreting SBATCH directives after the first appearance of a shell command blank lines and comment lines are okay. The UNIX shell commands are interpreted by the shell specified on the first line after the! By default, a job begins execution in the directory of submission with the local submission environment.

If you don't want stderr and stdout directed to the same file, use both " -e " and " -o " options to designate separate output files. The job script below requests an MPI job with 48 cores spread over 2 nodes and 1.

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Five sample batch scripts are below. Request an interactive session to a compute node using TACC's idev utility. Especially useful for development and debugging. TACC's idev provides interactive access to a node and captures the resulting batch environment which is automatically inherited by any addition terminal sessions that ssh to the node. In the sample session below, a user requests interactive access to a single node default for 15 minutes default is 30 in the development queue idev 's default in order to debug the myprog application.

Now the user may open another window to run the newly-compiled application, while continuing to debug in the original terminal session:. Users may also ssh to a compute node from a login node, but only when that user's batch job is running, or the user has an active interactive session, on that node. Once the batch job or interactive session ends, the user will no longer have access to that node. In the following example session user slindsey submits a batch job sbatch , queries which compute nodes the job is running on squeue , then ssh 's to one of the job's compute nodes. The displayed node list, nid0[] , nid is truncated for brevity.

Notice that the user attempts to ssh to compute node nid which is NOT in the node list. Since that node is not assigned to this user for this job, the connection is refused. When the job terminates the connection will also be closed even if the user is still logged into the node. User logs in to an assigned compute node and does work. Once the job has finished running, the connection is automatically closed. Parameter sweeps, where the same executable is run with multiple input data sets, as well as other high throughput scenarios, can be combined into a single job using TACC's launcher and pylauncher utilities.

The Launcher is a simple shell-based utility for bundling large numbers of independent, single process runs into one multi-node batch submission. This allows users to run more simultaneous serial jobs than is permitted directly through the serial queue, and improves turnaround time. See the sample Slurm launcher script above. Lonestar 5 has Hyperthreading HT enabled. When HT is enabled the OS will address two virtual threads per code. These logical threads share core resources and thus may not improve performance for all workloads.

In HT systems it is critical to pay attention to thread binding in multithreaded processes. HPC workloads often benefit from pinning processes to hardware instead of allowing the operating system to migrate them at will. This is particularly important in multicore and heterogeneous systems, where process and thread migration can lead to less than optimal memory access and resource sharing patterns, and thus a significant performance degradation.

This section describes techniques to control process affinity and memory locality that can be used to improve execution performance in Lonestar 5 and other HPC resources. In this section an MPI task is synonymous with a process. For a pure MPI-based job i. Do not use multiple methods to set affinity simultaneously as this can lead to unpredictable results. Since each MPI task is launched as a separate process, numactl can be used to specify the affinity and memory policy for each task.

There are two ways this can be used to exercise numa control when launching a batch executable:. The first command sets the same options for each task. Because the ranks for the execution of each a. The script will have access to the number of tasks per node and the rank of each task, and so it is possible to set individual affinity options for each task using this method.

In general any execution using more than one task should employ the second method to set affinity so that tasks can be properly pinned to the hardware. In threaded applications, the same numactl command may be used, but its scope is limited globally to all threads, because every forked process or thread inherits the affinity and memory policy of the parent. This behavior can be modified from within a program using the numa API to control affinity. Note, on the login nodes the core numbers for masking are assigned round-robin to the sockets cores 0, 2, 4,… are on socket 0 and cores 1, 3, 5, … are on socket 1 while on the compute nodes they are assigned contiguously cores are on socket 0 and are on socket 1.

Use this script as a template for implementing your own affinity script if a custom affinity script is needed for your jobs. After job submission, users may monitor the status of their jobs in several ways. While the job is in the waiting state the system is continuously monitoring the number of nodes that become available and applying a fair share algorithm and a backfill algorithm to determine a fair, expedient scheduling to keep the machine running at optimum capacity. The latest queue information can be displayed several different ways using the showq and squeue commands.

TACC's " showq " job monitoring command-line utility displays jobs in the batch system in a manner similar to PBS' utility of the same name. See Table 7 for more showq options. Note that the number of cores reported is always 48 x Number of nodes , independently of how many cores are actually requested per node. The exceptions are the vis and gpu queues which will report 20 x Number of nodes. Use the " -U " option with showq to display information for a single user:. Both the showq -U and squeue -u username commands display similar information:.

The showq command displays cores and time requested, while the squeue command displays the partition queue , the state ST of the job along with the node list when allocated. In this case, all three jobs are in the Pending PD state awaiting "Resources", nodes to free up. Table 8 details common squeue options and Table 9 describes the command's output fields.

Table 9. Columns in the squeue Command Output. Using the squeue command with the --start and -j options can provide an estimate of when a particular job will be scheduled:. Even more extensive job information can be found using the " scontrol " command. The output shows quite a bit about the job: job dependencies, submission time, number of codes, location of the job script and the working directory, etc.

See the man page for more details. The scancel command is used to remove pending and running jobs from the queue. Include a space-separated list of job IDs that you want to cancel on the command-line:. They include details for launching large jobs, running multiple executables with different MPI stacks, executing hybrid applications, and other operations. Viewing queue status may reveal jobs in a pending PD state. Jobs submitted to Slurm may be, and remain, in a pending state for many reasons such as:. The Reason Codes summarized below identify the reason a job is awaiting execution.

If a job is pending for multiple reasons, only one of those reasons is displayed. For a full list, view the squeue man page. In addition to the environment variables that can be inherited by the job from the interactive login environment, Slurm provides environment variables for most of the values used in the SBATCH directives. These are listed at the end of the sbatch man page. Table 11 below lists some important Slurm-provided environment variables.

For example, the following directive will NOT work as expected:. Some workflows may have job dependencies, for example a user may wish to perform post-processing on the output of another job, or a very large job may have to be broken up into smaller pieces so as not to exceed maximum queue runtime. Slurm will not process this option within a job script. The following command submits a job script that will run only upon successful completion of another previously submitted job:. The " sinfo " command gives a wealth of information about the status of the queues, but the command without arguments might give you more information than you want.

Use the print options in in the snippet below with sinfo for a more readable listing that summarizes each queue on a single line:. See " man sinfo " for more information. Users are welcome to install packages in their Home or Work directories. No super-user privileges are needed, simply use the " --prefix " option when configuring then making the package. Lonestar 5 supports both interactive and batch visualization on any compute node using the Software-Defined Visualization stack sdvis.

Remote desktop access to Lonestar 5 is provided through a virtual network connection VNC to one or more nodes. Users must first connect to a Lonestar login node see Accessing Lonestar 5 and from there submit a job that:. Once the vncserver process is running, the viewer establishes a secure SSH tunnel to the specified vncserver access port, and starts a VNC viewer application on their local system which presents the virtual desktop to the user.

This should NOT be your login password! This mechanism only deters unauthorized connections; it is not fully secure, as only the first eight characters of the password are saved. Submit this job with the sbatch command:. All arguments after the job script name are sent to the vncserver command. For example, to set the desktop resolution to x, use:. The vnc. Watch for the "To connect via VNC client" message at the end of the output file, or watch the output stream in a separate window with the commands:.

The spartan window manager twm is the default VNC desktop. The lightweight window manager, icewm , is recommended for remote performance. On Unix or Linux systems execute the following command once the port has been opened on an LS5 login node:. On Windows systems, find the menu in the SSH client where tunnels can be specified, and enter the local and remote ports as required, then ssh to LS5. Connect to " localhost:xxxx ", where " xxxx " is the local port you used for your tunnel. In the examples above, we would connect the VNC client to " localhost::xxxx " note the "::", some VNC clients accept " localhost:xxxx ".

Before the desktop is presented, the user will be prompted for their VNC server password the password created before your first session using vncpasswd as explained above. Depending on your local system this prompt's location may or may not be obvious. If you don't see it immediately take a good look around your desktop. The virtual desktop should appear at this point and includes one or two initial xterm windows which may be overlapping. One, which is white-on-black, manages the lifetime of the VNC server process.

Killing this window by typing "exit" or "ctrl-D" at the prompt, or selecting the "X" in the upper corner will cause the vncserver process to terminate and the original batch job to end. Because of this, we recommend that this window not be used for other purposes; it is just too easy to accidentally kill it and terminate the session. Move it off to one side out of the way. The other xterm window is black-on-white, and can be used to start both serial programs running on the node hosting the vncserver process, or parallel jobs running across the set of cores associated with the original batch job.

Additional xterm windows can be created using the window-manager left-button menu. From an interactive desktop, applications can be run from icons or from xterm command prompts. Two special cases arise: running parallel applications, and running applications that use OpenGL. Parallel applications are run on the desktop using the same ibrun wrapper described above see Running. The command:. At present, the compute nodes on Lonestar 5 do not support native X instances. VisIt was compiled under the Intel compiler and the cray-mpich stacks. After connecting to a VNC server on Lonestar 5, as described above, load the VisIt module at the beginning of your interactive session before launching the Visit application:.

VisIt first loads a dataset and presents a dialog allowing for selecting either a serial or parallel engine.

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Select the parallel engine. Note that this dialog will also present options for the number of processes to start and the number of nodes to use; these options are actually ignored in favor of the options specified when the VNC server job was started. In order to take advantage of parallel processing, VisIt input data must be partitioned and distributed across the cooperating processes. This requires that the input data be explicitly partitioned into independent subsets at the time it is input to VisIt. Otherwise, VisIt supports a metadata file with a. In addition, VisIt supports a "brick of values" format, also using the.

Set up your environment with the necessary modules. Load the swr , qt5 , ospray , and paraview modules in this order :. Select the "auto" configuration, then press "Connect". Then you'll see the parallel servers being spawned and the connection established. The large memory nodes are for jobs that require more than the 64GB of RAM available on the standard memory nodes. Demand for these nodes is high and wait times can be long. Please assess your memory needs carefully before submitting jobs to the large memory queues: in many cases there are ways to resolve memory issues that do not require moving to large memory nodes.

For example, instead of " -N 4 -n 96 " 96 tasks spread across 4 nodes, or 24 tasks per node , one can increase the memory available to each task by running " -N 8 -n 96 " 96 tasks spread across 8 nodes, or 12 tasks per node or even " -N 96 -n 96 " 1 task per node. TACC's Remora tool allows you to examine your memory needs easily. For more information about remora:. The TACC-largemem module provides access to the large memory queues. It is the large memory equivalent of the default " TACC " module that manages access to the standard memory queues. To configure your environment for the large memory queues, execute:.

Loading the TACC-largemem module has two effects:. You can of course achieve the same outcome in other ways e. Do not, however, simply unload the TACC-largemem module; doing so would have the effect of leaving you without access to any Slurm commands. To run an MPI application in either of the large memory queues you will need to rebuild the application using the impi-largemem module. This module provides an MPI stack compatible with the Infiniband network on the large memory queues.

The login nodes and GB compute nodes share the same Haswell architecture. Be sure to load the impi-largemem when building MPI applications for the large memory nodes. Beyond this requirement, there are no other special considerations when using the login nodes to build software for the largememGB nodes. The Ivy Bridge 1TB compute nodes have a different processor architecture than the Haswell login nodes.

This can affect the way you use the Intel compiler and Haswell login nodes to build software targeting the 1TB Ivy Bridge nodes. Among the plausible approaches:.

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The resulting binary may be up to 2x larger than a single-target executable, and compilation will take more time. But this approach is otherwise an excellent choice for codes you want to run in both large memory queues. Generic Binary supported : If you compile with no architecture flags e. The executable, however, will not exploit new hardware features or the best processor-specific optimizations.

When building MPI applications, be sure to load the " impi-largemem " module. Submitting a batch job to a large memory queue is similar to doing so on the standard queues. Interactive sessions using idev are also possible, but queue wait times may make interactive sessions impractical. To submit a batch job or request an interactive session on a large memory queue:. You must load this module before executing your sbatch or idev command. For MPI jobs, check to make sure you have impi-largemem module loaded.

Loading the TACC-largemem module automatically loads impi-largemem module. You can also load the module explicitly before calling sbatch or idev , in your job script itself, or from within your interactive idev session. The Cray Environment is designed to make available to you a user experience similar to other Cray systems and the standard software stack provided by the vendor. The ticket will be directed to the appropriate staff member for support. Skip to Content. Interconnect Lonestar 5 uses an Aries Dragonfly interconnect.

Accessing Lonestar 5 The standard way to access Lonestar 5 ls5. To access the most recent versions of GCC, load one of the gcc modules. Table 1. Good Citizenship The Lonestar 5 system is a shared resource. Good practices can be boiled down to two items: The two login nodes provide an interface to the "back-end" compute nodes. Don't Run Jobs on Login Nodes The login nodes are meant to be used exclusively for file management, editing and compiling. Avoid too many simultaneous file transfers. Three concurrent scp sessions is probably fine. One hundred concurrent file sessions is not.

Computing Environment Lonestar 5's default login shell is Bash. Modules TACC continually updates application packages, compilers, communications libraries, tools, and math libraries. Managing your Files Lonestar 5 supports multiple file transfer programs such as scp , sftp , and rsync. Sharing Files Users often wish to collaborate with fellow project members by sharing files and data with each other. Transferring Files. Running Jobs on Lonestar 5 This section provides an overview of how compute jobs are charged to allocations and describes the Simple Linux Utility for Resource Management Slurm batch environment, Lonestar 5 queue structure, lists basic Slurm job control and monitoring commands along with options.

Job Accounting Lonestar 4 users have been accustomed to requesting allocations and seeing their usage reported in service units SUs , where an SU is defined as a wallclock core hour. Production Queues The Lonestar 5 production queues, Standard Memory and Large Memory, and their characteristics wall-clock and processor limits; charge factor; and purpose are listed in Table 3a and Table 3b below.

Table 3b. Submit a batch job with sbatch Use Slurm's sbatch command to submit a job. Sample Slurm Batch Scripts Five sample batch scripts are below. Interactive sessions via idev Request an interactive session to a compute node using TACC's idev utility. Interactive sessions via ssh Users may also ssh to a compute node from a login node, but only when that user's batch job is running, or the user has an active interactive session, on that node.

Submitted batch job User polls the queue, waiting till the job runs state "R" and nodes are assigned. The connection is denied. Connection closed by Connection to nid closed by remote host. Connection to nid closed.

Parameter Sweeps and High Throughput Jobs Parameter sweeps, where the same executable is run with multiple input data sets, as well as other high throughput scenarios, can be combined into a single job using TACC's launcher and pylauncher utilities. Twenty four of the compute nodes are reserved for development and are accessible interactively for up to two hours. The 16 GPU nodes are distributed across the cabinets. The Aries network provides dynamic routing, enabling for optimal use of the overall system bandwidth under load. See Section 2. Lonestar 5 uses an Aries Dragonfly interconnect.

This high performance network has three levels. Rank 1 level has all to all connectivity in the backplane of each node chassis 64 nodes and provides per packet adaptive routing. Rank 2 level is made of sets of 6 chassis backplanes connected by passive copper cables, forming two cabinet groups nodes. Rank 3 level is made of seven cabinets connected in an all to all fashion with active optical links. Figure 2.

Lonestar 5 Network. Four nodes within a blade dark green boxes are connected to an Aires router larger dark blue box. Nodes within a chassis blades shown arranged in a row are connected rank-1 routes within a chassis by a backplane light green line. Each blade of a six chassis group, 3 chassis from 2 racks indicated by perforated lines ,are connected rank-2 routes, blue lines through 5 ports of the router.

Intra group connections rank-3 routes are formed through fiber orange lines to each router. There are 7 racks of normal-queue computes nodes ranks 3,4 and 7 not shown. In many ways, however, the large memory nodes form a separate, largely independent cluster. This large memory cluster has its own Infiniband connectivity, Slurm job scheduler, queues, and software stack. The " TACC-largemem " module controls access to the large memory nodes. See Using the Large Memory Nodes section for more information.

The standard way to access Lonestar 5 ls5. Please visit the Wikipedia page for more SSH info. Mac users may use the built-in Terminal application.


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Windows users may choose from many clients available for download. TACC staff recommends either of the following two light-weight and free clients. Users must connect to Lonestar 5 using the Secure Shell " ssh " command to ensure a secure login session. Initiate an ssh connection to a Lonestar 5 login node from your local system:. If you've forgotten your password, go to the TUP home page and select the "? Forgot Password" button in the Sign In area. To report a problem please run the ssh command with the " -vvv " option and include the verbose information when submitting a help ticket.

Do not run the optional ssh-keygen command to set up Public-key authentication. This command sets up a passphrase that will interfere with the specially configured. If you have already done this, remove the ". Log out and log back in to regenerate the keys. For compiling MPI codes, the familiar commands " mpicc ", " mpicxx ", " mpif90 " and " mpif77 " are available.

Also, the compilers " icc ", " icpc ", and " ifort " are directly accessible. For pure OpenMP jobs, or other jobs that use threads but not MPI, specify a single node using the " -N " option and a single task using the " -n " option. For a more comprehensive list, consult the ld man page. Use the " -l " loader option to link in a library at load time. Many of the modules for applications and libraries, such as the hdf5 library module provide environment variables for compiling and linking commands. The following example illustrates their use for the hdf5 library:.

You can view the full path of the dynamic libraries inserted into your binary with the ldd command. The example below shows a partial listing for the h5stat binary:. The Intel Math Kernel Library MKL is a collection of highly optimized functions implementing some of the most important mathematical kernels used in computational science, including standardized interfaces to:. Unless you have specialized needs, there is no need to specify include paths and libraries explicitly. Instead, using MKL with the Intel modules requires nothing more than compiling and linking with the " -mkl " option.

When using a GNU compiler, load the MKL module before compiling or running your code, then specify explicitly the MKL libraries, library paths, and include paths your application needs. Execute " module help mkl " for more information. Replace generic instructions that include link options like " -lblas " or " -llapack " with the simpler MKL approach described above.

This requires no action on your part other than specifying an appropriate max thread count for MKL; see the section below for more information. Any code that calls MKL functions can potentially benefit from MKL's thread-based parallelism; this is true even if your code is not otherwise a parallel application. In either case, MKL will attempt to choose an optimal thread count less than or equal to the specified value. If you are running a single serial, unthreaded application or an unthreaded MPI code involving a single MPI task per node it is usually best to give MKL as much flexibility as possible by setting the max thread count to the total number of hardware threads on the node 48 on the typical Haswell LS5 compute node.

Of course things are more complicated if you are running more than one process on a node: e. The Lonestar 5 system is a shared resource. Hundreds of users may be logged on to the two login nodes at one time accessing the filesystem, hundreds of jobs may be running on all compute nodes, with hundreds more jobs queued up. All users must follow a set of good practices which entail limiting activities that may impact the system for other users. Good practices can be boiled down to two items:. The two login nodes provide an interface to the "back-end" compute nodes.

Think of the login nodes as a prep area, where users may edit, compile, perform file management, issue transfers, submit batch jobs etc. The compute nodes are where actual computations occur and where research is done. All batch jobs and executables, as well as development and debugging sessions, are run on the compute nodes. To access compute nodes on TACC resources, one must either submit a job to a batch queue or initiate an interactive session using the idev utility.

You can also access a compute node via ssh if you are already running a job on that node. The login nodes are meant to be used exclusively for file management, editing and compiling. DO NOT run programs on the login nodes. The login nodes are shared among all users currently logged into LS5.

Running jobs on the login nodes is one of the fastest routes to account suspension. Instead, run on the compute nodes via an interactive session idev or by submitting a batch job. This ruling also applies to, for example, Matlab and computationally expensive Python scripts. Please see the idev utility. Lonestar 5's default login shell is Bash. The csh and zsh shells are also available. Submit a support ticket to change your default login shell; the chsh command is not supported. Lonestar 5 does not support ".

Put your aliases and other customizations directly in the standard startup files. See the default templates in your account for further instructions and examples. Unless you have specialized needs, it is generally best to leave the bash ". TACC continually updates application packages, compilers, communications libraries, tools, and math libraries. To facilitate this task and to provide a uniform mechanism for accessing different versions of software.

TACC employs Lmod for environment management. At login, modules commands set up a basic environment for the default compilers, tools, and libraries. Therefore, there is no need for you to set them or update them when updates are made to system and application software. Users that require third-party applications, special libraries, and tools for their projects can quickly tailor their environment with only the applications and tools they need. Using modules to define a specific application environment allows you to keep your environment free from the clutter of all the application environments you don't need.

The environment for executing each major TACC application can be set with a module command. The specifics are defined in a modulefile file, which sets, unsets, appends to, or prepends to environment variables e. Each modulefile also sets functions or aliases for use with the application. The general format of this command is:. Once you know the module that you want to load, you can simply use the load option. Or you can get more information about that module with the command in this case Python :.

The spider command is also used to find modules. For example, to find all the hdf5 modules, type:. To look at a synopsis about using an application in the module's environment in this case, fftw2 , or to see a list of currently loaded modules, execute the following commands:. Lonestar 5 supports multiple file transfer programs such as scp , sftp , and rsync. Lonestar 5 mounts the three file systems that are shared across all nodes: home, work, and scratch. Consult Table 2 for quota and purge policies on these file systems. Your quota and reported usage on this file system is the sum of all files stored on Stockyard regardless of their actual location on the work file system.

Figure 3. Stockyard File System. Example for fictitious user bjones. All directories usable from all systems. Sub-directories e. Users often wish to collaborate with fellow project members by sharing files and data with each other. Project managers or delegates can create shared workspaces, areas that are private and accessible only to other project members, using UNIX group permissions and commands. Shared workspaces may be created as read-only or read-write, functioning as data repositories and providing a common work area to all project members.

Use the Secure Shell scp utility to transfer data from any Linux system to and from the login node. A file can be copied from your local system to the remote server by using the command:. The rsync command is another way to keep your data up to date. In contrast to scp , rsync transfers only the actual changed parts of a file instead of transferring an entire file. Hence, this selective method of data transfer can be much more efficient than scp. The following example demonstrates usage of the rsync command for transferring a file named " myfile.

An entire directory can be transferred from source to destination by using rsync as well. For directory transfers the options " -avtr " will transfer the files recursively " -r " option along with the modification times " -t " option and in the archive mode " -a " option to preserve symbolic links, devices, attributes, permissions, ownerships, etc. The " -v " option verbose increases the amount of information displayed during any transfer. When executing multiple instantiations of scp or rsync , please limit your transfers to no more than processes at a time.

This section provides an overview of how compute jobs are charged to allocations and describes the Simple Linux Utility for Resource Management Slurm batch environment, Lonestar 5 queue structure, lists basic Slurm job control and monitoring commands along with options. Lonestar 4 users have been accustomed to requesting allocations and seeing their usage reported in service units SUs , where an SU is defined as a wallclock core hour.

Lonestar 5 will measure usage and calculate SUs differently. On Lonestar 5 an SU will be defined as a wallclock node hour - the use of one node and all its cores for one hour of wallclock time plus any additional charges for the use of specialized queues, e. Table 3a and Table 3b below list production queues and their multipliers.

Measuring usage in Node Hours vs Core Hours results in significantly lower SU values as the "cores per node" scaling factor is not included. Users will therefore see a lower "Available SUs" balance when logging into Lonestar 5 than they have in the past.

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In the future, users must submit allocation requests, renewals and increases for Lonestar 5 using the Node Hours metric. Be aware that the figures shown on the portal may lag behind the most recent usage. Projects and allocation balances are also displayed upon command-line login. Interactive vs Batch Jobs Once logged into Lonestar 5 users are automatically placed on one of two "front-end" login nodes. To determine what type of node you're on, simply issue the "hostname" command.

Lonestar 5's login nodes will be labeled login[]. The compute nodes will be labeled something like nid To run jobs and access compute nodes on TACC resources, one must either submit a job to a batch queue or initiate an interactive session using the idev utility. Schedulers such as LoadLeveler, SGE and Slurm differ in their user interface as well as the implementation of the batch environment. Common to all, however, is the availability of tools and commands to perform the most important operations in batch processing: job submission, job monitoring, and job control cancel, resource request modification, etc.

The scheduler on Lonestar 5 is Slurm. Batch jobs are programs scheduled for execution on the compute nodes, to be run without human interaction. A job script also called "batch script" contains all the commands necessary to run the program: the path to the executable, program parameters, number of nodes and tasks needed, maximum execution time, and any environment variables needed.

Batch jobs are submitted to a queue and then managed by a scheduler. The scheduler manages all pending jobs in the queue and allocates exclusive access to the compute nodes for a particular job. The scheduler also provides an interface allowing the user to submit, cancel, and modify jobs. All users must wait their turn, not first come, first served. The Slurm scheduler will fit jobs in when the requested resources nodes, runtime become available.

Do not request more resources than needed, else the job will wait longer in the queue. The Lonestar 5 production queues, Standard Memory and Large Memory, and their characteristics wall-clock and processor limits; charge factor; and purpose are listed in Table 3a and Table 3b below. Queues that don't appear in the table such as systest are non-production queues for system and HPC group testing and special support.

Include in your request reasonable evidence of your readiness to run at scale on Lonestar 5. In most cases this should include strong or weak scaling results summarizing experiments you have run on Lonestar 5 up to the limits of the normal queue. An important note on scheduling: hyper-threading is currently enabled on Lonestar 5. While there are 24 cores on each non-GPU standard memory node, the operating system and scheduler will report a total of 48 CPUs hardware threads. These queues are available through TACC-largemem module. See " Using the Large Memory Nodes " section below for more information.

While there are 32 cores on each GB Haswell node, the operating system and scheduler will report a total of 64 CPUs hardware threads. Similarly, there are 48 cores on each 1TB node, but the operating system and scheduler will report a total of 96 CPUs. Use Slurm's sbatch command to submit a job. Specify the resources needed for your job e.

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This file can contain both shell commands and special statements that include SBATCH options and resource specifications; shell commands other than the initial parser line e. Some of the most common options are described in Table 4 below and in the example job scripts. Details are available online in man pages e. Options can be passed to sbatch on the command-line or specified in the job script file; we recommend, however, that you avoid using the "—export" flag because there are subtle ways in which it can interfere with the automatic propagation of your environment.

As a general rule it is safer and easier to store commonly used SBATCH directives in a submission script that will be reused several times rather than retyping the options at every batch request. In addition, it is easier to maintain a consistent batch environment across runs if the same options are stored in a reusable job script. All batch submissions MUST specify a time limit, number of nodes, and total tasks. Jobs that do not use the -t time , -N nodes and -n total tasks options will be rejected. Batch scripts contain two types of statements: scheduler directives and shell commands in that order.

Slurm stops interpreting SBATCH directives after the first appearance of a shell command blank lines and comment lines are okay.

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CMATH_UG - MATLAB C Math Library Computation Visualization...

The UNIX shell commands are interpreted by the shell specified on the first line after the! By default, a job begins execution in the directory of submission with the local submission environment. If you don't want stderr and stdout directed to the same file, use both " -e " and " -o " options to designate separate output files.

The job script below requests an MPI job with 48 cores spread over 2 nodes and 1. Five sample batch scripts are below. Request an interactive session to a compute node using TACC's idev utility. Especially useful for development and debugging.

TACC's idev provides interactive access to a node and captures the resulting batch environment which is automatically inherited by any addition terminal sessions that ssh to the node. In the sample session below, a user requests interactive access to a single node default for 15 minutes default is 30 in the development queue idev 's default in order to debug the myprog application.

Now the user may open another window to run the newly-compiled application, while continuing to debug in the original terminal session:. Users may also ssh to a compute node from a login node, but only when that user's batch job is running, or the user has an active interactive session, on that node.

Once the batch job or interactive session ends, the user will no longer have access to that node. In the following example session user slindsey submits a batch job sbatch , queries which compute nodes the job is running on squeue , then ssh 's to one of the job's compute nodes. The displayed node list, nid0[] , nid is truncated for brevity. Notice that the user attempts to ssh to compute node nid which is NOT in the node list. Since that node is not assigned to this user for this job, the connection is refused. When the job terminates the connection will also be closed even if the user is still logged into the node.

User logs in to an assigned compute node and does work. Once the job has finished running, the connection is automatically closed. Parameter sweeps, where the same executable is run with multiple input data sets, as well as other high throughput scenarios, can be combined into a single job using TACC's launcher and pylauncher utilities. The Launcher is a simple shell-based utility for bundling large numbers of independent, single process runs into one multi-node batch submission.

This allows users to run more simultaneous serial jobs than is permitted directly through the serial queue, and improves turnaround time. See the sample Slurm launcher script above. Lonestar 5 has Hyperthreading HT enabled. When HT is enabled the OS will address two virtual threads per code.

These logical threads share core resources and thus may not improve performance for all workloads. In HT systems it is critical to pay attention to thread binding in multithreaded processes. HPC workloads often benefit from pinning processes to hardware instead of allowing the operating system to migrate them at will.

This is particularly important in multicore and heterogeneous systems, where process and thread migration can lead to less than optimal memory access and resource sharing patterns, and thus a significant performance degradation. This section describes techniques to control process affinity and memory locality that can be used to improve execution performance in Lonestar 5 and other HPC resources. In this section an MPI task is synonymous with a process. For a pure MPI-based job i. Do not use multiple methods to set affinity simultaneously as this can lead to unpredictable results.

Since each MPI task is launched as a separate process, numactl can be used to specify the affinity and memory policy for each task. There are two ways this can be used to exercise numa control when launching a batch executable:. The first command sets the same options for each task. Because the ranks for the execution of each a. The script will have access to the number of tasks per node and the rank of each task, and so it is possible to set individual affinity options for each task using this method.