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MATLAB R2019a for Mac Review
MathWorks MATLAB R2019a for MacOS X is one of the best and most amazing applications developed by MathWorks. Basically Matlab 2019a for Mac is a programming platform designed specifically for solving and dealing with the complex mathematical equations and problems. Engineers and Scientists use MATLAB R2019a for technical computing. It is also used for a range of applications, including deep learning and machine learning, signal processing and communications, image and video processing, control systems, test and measurement, computational finance, and computational biology.
Well, right now this program is in its version R2019a released this year, but we wouldn't be surprised if a 2017 version is launched shortly because ever since Cleve Moler published the first edition back in 1984 there have been periodical updates almost every year. One feels old thinking about the fact that the first version I used was 6.5. MATLAB R2019a for Mac Review. MathWorks MATLAB R2019a for MacOS X is one of the best and most amazing applications developed by MathWorks. Basically Matlab 2019a for Mac is a programming platform designed specifically for solving and dealing with the complex mathematical equations and problems. Engineers and Scientists use MATLAB R2019a for. MathWorks MATLAB R2019a v9.6.0.1072779 X64-ISO ISO 22 March 2019 18.8 GB Company MathWorks most complete software for computational computer produces; the main program the company that. Learn more about license MATLAB and Simulink Student Suite.
This easy to manage but powerful software enables users to analyze data, develop algorithms, and create models and applications. Its amazing built-in math functions help engineers and scientists to explore multiple approaches to arrive at a solution. MATLAB R2019a not only optimizes the engineering mathematics but also helps users to execute their ideas from research to production in a most professional way by deploying to enterprise applications and embedded devices, as well as integrating with Simulink and Model-Based Design. With the passage of time, MATLAB adding new features to entertains its users in a most intelligent way. The new Live Editor of MATLAB allows users to create scripts that not only capture the code but enables them to share it with others. Similarly, its automated, contextual hints keep the users on track to perform their tasks quickly and accurately.
A new App Designer is now part of MATLAB application as it allows users to create professional apps without having any previous software development experience. Through its simple but powerful tools, users can create the app design and functionalities by performing drag and drop. The developed apps could be share by creating standalone desktop or web apps with MATLAB Compiler. In Data Analysis, it provides the latest and unique tools for importing, cleaning, filtering, and grouping the data. For dealing with Big Data, it has excellent compatibility to works with the popular storage systems including traditional file systems, SQL and NoSQL databases, and Hadoop/HDFS. To make it simple and straightforward, MATLAB R2019a for MacOS X has all the necessary and advanced tools, features and functions to make your technical computing easier, organize, optimize and sharable.
Features of MATLAB R2019a for Mac
- Excellent application for dealing with technical computing
- No #1 choice of engineers and scientists for solving the complex equations
- Offers latest tools and functions for optimizing the engineering problems
- Makes deep learning accessible for everyone even without pro-level skills
- Allows users to design their own models or access the latest models
- Helps users to import pre-trained models from Caffe without any extra software
- Save live scripts and functions as Microsoft Word documents
- Offers automatically generate CUDA code for embedded deployment
- Provides capabilities to support collaborative software development practices
- Got New MATLAB Online to share folders with a view-only link or edit permissions
System Requirements for MATLAB R2019a for Mac
- Operating System: Mac OS X 10.10 or later
- Memory (RAM): 2 GB
- Hard Disk Space: 18 GB
- Processor: Intel Multi-Core Processor higher
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OUTLINE
Running MATLAB via Your Web Browser
If you are new to high-performance computing then you will find that the simplest way to use MATLAB on the HPC clusters is through the Open OnDemand web interface. You will need to use a VPN to connect from off-campus (GlobalProtect VPN is recommended). If you have an account on Adroit or Della then browse to https://myadroit.princeton.edu or https://mydella.princeton.edu. We recommend the GlobalProtect VPN. If you need an account on Adroit then complete this form.
To begin a session, click on 'Interactive Apps' and then 'MATLAB'. You will need to choose the 'MATLAB version', 'Number of hours' and 'Number of cores'. Set 'Number of cores' to 1 unless you are sure that your script has been explicitly parallelized using, for example, the Parallel Computing Toolbox (see below). Click 'Launch' and then when your session is ready click 'Launch MATLAB'. Note that the more resources you request, the more you will have to wait for your session to become available.
Submitting Batch Jobs to the Slurm Scheduler
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The web interface described above is good for interactive work. One can also submit MATLAB batch jobs to the Slurm scheduler. This applies to Adroit, Della and TigerGPU. A job consists of two pieces: (1) a MATLAB script and (2) a Slurm script that specifies the needed resources, sets the environment and lists the commands to be run. Learn more about Slurm or see the example below.
Running a Serial MATLAB Job
A serial MATLAB job is one that requires only a single CPU-core. Here is an example of a trivial, one-line serial MATLAB script (hello_world.m):
The Slurm script (job.slurm) below can be used for serial jobs:
By invoking MATLAB with -singleCompThread -nodisplay -nosplash, the GUI is suppressed as is the creation of multiple threads. To run the MATLAB script, simply submit the job to the scheduler with the following command:
After the job completes, view the output with cat slurm-*:
Use squeue -u $USER to monitor the progress of queued jobs. To run the example above on Della, for example, carry out these commands:
Choosing a MATLAB Version
Run the command below to see the available MATLAB versions. For example, on Della:
In your Slurm script you must choose a specific version, for example: module load matlab/R2019b.
Running a Multi-threaded MATLAB Job with the Parallel Computing Toolbox
Most of the time, running MATLAB in single-threaded mode (as described above) will meet your needs. However, if your code makes use of the Parallel Computing Toolbox (e.g., parfor) or you have intense computations that can benefit from the built-in multi-threading provided by MATLAB's BLAS implementation, then you can run in multi-threaded mode. One can use up to all the CPU-cores on a single node in this mode. Multi-node jobs are not possible with the version of MATLAB that we have so your Slurm script should always use #SBATCH --nodes=1. Here is an example from MathWorks of using multiple cores (for_loop.m):
The Slurm script (`job.slurm`) below can be used for this case:
Note that -singleCompThread does not appear in the Slurm script in contrast to the serial case. One must tune the value of --cpus-per-task for optimum performance. Use the smallest value that gives you a significant performance boost because the more resources you request the longer your queue time will be. The example above runs in 30 s with 1 core and 9 s with 4 cores on Adroit.
Overriding the 12 core limit
By default MATLAB will restrict you to 12 worker threads. You can override this when making the parallel pool with the following line, for example, with 24 threads:
If you use more than one thread then make sure that your code can take advantage of all the CPU-cores. The amount of time that a job waits in the queue is proportional to the requested resources. Furthermore, your fairshare value is decreased in proportion to the requested resources. To find the optimal number of CPU-cores for a MATLAB job see the 'Multithreading' section on Chossing the Number of Nodes, CPU-cores and GPUs.
How Do I Know If My MATLAB Code is Parallelized?
A parfor statement is a clear indication of a parallelized MATLAB code. However, there are cases when the parallelization is not obvious. One example would be a code that uses linear algebra operations such as matrix multiplication. In this case MATLAB will use the BLAS library which offers multithreaded routines.
There are two common ways to deteremine whether or not a MATLAB code can take advantage of parallelism without knowing anything about the code. The first to is run the code using 1 CPU-core and then do a second run using, say, 4 CPU-cores. Look to see if there is a significant difference in the execution time of the two codes. The second method is to launch the job using, say, 4 CPU-cores then ssh to the compute node where the job is running and use htop -u $USER to inspect the CPU usage. To get the name of the compute node where your job is running use the following command:
The rightmost column labeled 'NODELIST(REASON)' gives the name of the node where your job is running. SSH to this node, for example:
Once on the compute node, run htop -u $USER. If your job is running in parallel you should see a process using much more than 100% in the %CPU column. For 4 CPU-cores this number would ideally be 400%.
Running MATLAB on Nobel
The Nobel cluster is a shared system without a job scheduler. Because of this, users are not allowed to run MATLAB in multi-threaded mode. The first step in using MATLAB on Nobel is choosing the version. Run module avail matlab to see the choices. Load a module with, e.g., module load matlab/R2019b. After loading a MATLAB module, to run MATLAB interactively on the script myscript.m:
If you would like to use the GUI then you must first connect using ssh -X. In that case the command is:
If you are on a Windows machine then consider reading Run MATLAB from Nobel Cluster for Windows Computers.
Using the MATLAB GUI on Tigressdata
In addition to the web interfaces on MyAdroit and MyDella, one can also launch MATLAB with its GUI on Tigressdata. Tigressdata is ideal for data post-processing and visualization. You can access your files on the different filesystems using these paths: /tiger/scratch/gpfs/<YourNetID>, /della/scratch/gpfs/<YourNetID>, /perseus/scratch/gpfs/<YourNetID>, /tigress and /projects. Mac users will need to have XQuartz installed while Windows users should install MobaXterm (Home Edition). Visit the OIT Tech Clinic for assistance with installing, configuring and using these tools. To run MATLAB interactively with its graphical user interface:
It can take a minute or more for the GUI to appear and for initialization to complete. To work interactively without the GUI:
Note that one can use the procedures above on the HPC clusters (e.g., Della) but only for non-intensive work since the head node is shared by all users of the cluster.
MATLAB is Not Allowed on TigerCPU or Perseus
TigerCPU is designed for parallel, multi-node jobs. MATLAB cannot be used across multiple nodes so it is not allowed. If you try to run a MATLAB job on TigerCPU you will encounter the following error message:
You will need to carry out the work on another cluster such as Della or if your script can be written to use a GPU then you can use TigerGPU. To get started with MATLAB and GPUs see below.
Like TigerCPU, Perseus was also designed for parallel, multi-node jobs. While MATLAB is available on the head node of Perseus for very light work, it is not available on the Perseus compute nodes.
MATLAB is Not Available on Traverse
MathWorks does not produce a version of MATLAB that is compatible with the POWER architecture of Traverse.
Running MATLAB on GPUs
Many routines in MATLAB have been written to run on a GPU. Below is a MATLAB script (svd_matlab.m) that performs a matrix decomposition using a GPU:
The Slurm script (job.slurm) below can be used for this case:
In the above Slurm script, notice the new line: #SBATCH --gres=gpu:1.
The job can be submitted to the scheduler with:
Be sure that your MATLAB code is able to use a GPU before submitting your job. See this getting started guide on MATLAB and GPUs.
MyAdroit
To request a V100 GPU via the MyAdroit web portal, add the following to the 'Extra slurm options' field:
Note that if all the GPUs are in use then you will have to wait. During busy times this may take hours or days. To check what is available, from the OnDemand main menu, click on 'Clusters' and then '_Adroit Cluster Shell Access'. From the black terminal screen run this command: ssh adroit-h11g1. Then run gpustat. There are four V100 GPUs on that node. If you see usernames associated with each of them then the node is occupied. Type exit to leave the compute node. Note that Adroit is a training cluster so it is not intended for lengthy production jobs.
Running MATLAB in Python
You can embed MATLAB inside a Python script using the MATLAB Engine for Python. There are two sets of the directions below. The first shows how to do the installation in a standalone manner while the second uses a Conda environment.
You will need to set the PYTHONPATH as above in your Slurm script in addition to including the two modules.
You can also install the MATLAB Engine for Python into a Conda environment:
Your Slurm script in this case will need to include:
MATLAB and Java
MATLAB uses some functionality from Java. To see which implementation of Java it is using:
Additional information can be obtained by running: javaclasspath()
To directly import code: import java.util.ArrayList
If you are using Java functionality in MATLAB be sure to eliminate -nojvm in your Slurm script.
Where to Store Your Files
You should run your jobs out of /scratch/gpfs/ on the HPC clusters. These filesystems are very fast and provide vast amounts of storage. Do not run jobs out of /tigress or /projects. That is, you should never be writing the output of actively running jobs to these filesystems. /tigress and /projects are slow and should only be used for backing up the files that you produce on /scratch/gpfs. Your /home directory on all clusters is small and it should only be used for storing source code and executables. The commands below give you an idea of how to properly run a MATLAB job:
If the run produces data that you want to backup then copy or move it to /tigress:
For large transfers consider using rsync instead of cp. Most users only do back-ups to /tigress every week or so. While /scratch/gpfs is not backed-up, files are never removed. However, important results should be transferred to /tigress or /projects. The diagram below gives an overview of the filesystems:
FAQ
1. How to pass arguments to a MATLAB function within a SLURM script?
Let us use the following MATLAB script as a example:
It simply prints the value of the 3 arguments to the screen. Let us save this MATLAB function in a file called print_values.m. Now here is a SLURM script that shows you how to run using a SLURM script that uses a SLURM job array:
Matlab R2019a Free Download
Note that this simple scripts demonstrates how to pass three types of arguments: 1. A string, STRING, note that the double quotes (') are part of the bash syntax and the single quotes ( ' ) are part of the MATLAB syntax. 2. A real number, GOLDEN, 3. A SLURM environment variable, SLURM_ARRAY_TASK_ID, which happens to be an integer. Assuming that this script is saved in a file called batch.sh, we submit to the scheduler with the command: The job can be submitted to the scheduler with:
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Once the script is executed, 8 files should be created in your directory: `matlabN.out` and `matlabN.err` where N=0,...,3 because we asked for a job array of 4 elements.
2. Why do my MATLAB jobs using the Parallel Computing Toolbox fail on launch?
I have submitted multiple job where the MATLAB script contains this snippet:
Some of the jobs finish successfully but others fail with:
It appears that two jobs are launching simultaneously and attempting to create this .mat file at the same time. How do I avoid this? The solution is to set the output to /tmp. Every job has a private /tmp directory when running so you can redirect this file to there. The code snippet becomes:
3. How can I run MATLAB when I don't have internet access? The solution is to install a local license. Here is how to do it for macOS: http://www.princeton.edu/software/licenses/software/matlab/R2017a_LocalLicM.xml For other operating systems, see: http://www.princeton.edu/software/licenses/software/matlab/
4. Why do I get an error about licensing when I try to run MATLAB on TigerCPU?
TigerCPU is reserved for large parallel jobs. MATLAB jobs can only use a single node so they are not allowed to run on TigerCPU. You will either need to run the job on another cluster or if your code can make use of a GPU then TigerGPU can be used.
5. I installed MATLAB through the university on my laptop. How do I update the license?
Please see this OIT page.
6. What does this error mean: '/usr/licensed/matlab-R2019a/bin/matlab: fork: retry: Resource temporarily unavailable'?
It may be that you have too many processes running. Try killing some of your processes and retrying. To see you running processes use: ps -ef | grep <YourNetID>
Getting Help
If you encounter any difficulties while running MATLAB on the HPC clusters then please send an email to cses@princeton.edu or attend a help session.