MATLAB Parallel Server


MATLAB Parallel Server

Perform MATLAB and Simulink computations on clusters and clouds

Run Algorithms on Multiple Machines

Develop a prototype on your desktop using Parallel Computing Toolbox, and scale to clusters on-premises or in the cloud without needing to change your model or algorithm.

Connect to Clusters from MATLAB

Access different cluster environments from your desktop just by changing your cluster profile. Take advantage of high-end hardware on-premises or in the cloud without changing your code or leaving the MATLAB desktop environment. 

Use Your Desktop Toolboxes on the Cluster

MATLAB Parallel Server is the only license required on the cluster. Your desktop license profile is dynamically enabled on the cluster.

Scale on Your Existing Hardware

Create a cluster from a few dedicated machines and manage jobs with MATLAB Job Scheduler or integrate with your existing cluster and manage jobs with your scheduler. Users can manage their jobs and job artifacts without leaving MATLAB.

Scale in Public and Private Clouds

Run MATLAB Parallel Server in public and private clouds where you can access specialized and more powerful hardware. Use preconfigured options from both MathWorks and MathWorks hosting providers or build the infrastructure yourself.

Scale in Cloud Native Environments

Run MATLAB Parallel Server in containerized environments. Integrate MATLAB Parallel Server with container-based solutions like Kubernetes, either on-premises or in the cloud.

Run on More Hardware Resources

Get access to more CPU cores and GPU resources on your cluster on-premises or in the cloud without leaving the MATLAB desktop environment.

Run Multiple Simulink Simulations in Parallel

Easily set up parameter sweeps, manage model dependencies and build folders, and transfer base workspace variables to cluster processes. Use the Simulation Manager user interface to visualize and manage multiple runs of Simulink models on a cluster.

Overcome Memory Barriers

Use distributed arrays to execute calculations that will not fit in the memory of a single machine without needing to recode your algorithm or use a shared-memory architecture.

“High-performance computing with MATLAB enables us to process previously unanalyzed big data. We translate what we learn into an understanding of how human activities affect the health of ecosystems to inform responsible decisions about what humans do in the ocean and on land.”

Dr. Christopher Clark, Cornell University