SAS® GRID MANAGER

Outcompete by outcomputing.

A managed, shared grid computing environment with workload balancing, high availability and faster processing.

Managed, shared environment

Delivers enterprise-class dynamic workload balancing for multiple users and applications.

High availability, grid-enabled SAS

Includes several high-availability capabilities for all critical services, and provides maximum processing efficiency with minimum user intervention.

Real-time monitoring and administration

Real-time monitoring and administration. Includes an interface for configuring and managing high-availability services.

Flexible infrastructure

Allows you to add computing resources as needed to meet changing demands.

Meet peak computing demands reliably and cost-effectively.

SAS Grid Manager on desktop monitor showing LSF configurations.

Balance your workload.

As your organization's use of business analytics grows, so does the need for a flexible IT infrastructure that can scale to increasingly diverse workloads. Easily reassign computing resources to meet peak demand. A central point of control lets you queue and prioritize jobs across multiple users and applications. High-priority jobs run immediately and computing resources are fully utilized.

Be ready for anything.

Having multiple servers in a grid environment means that if a server fails, its jobs transition seamlessly to another server. With a highly available, shared grid environment, you're always on and always connected. You can perform maintenance on specific servers without interrupting jobs. And add computing resources without disrupting the business.

SAS Grid Manager on desktop monitor showing job activity under stored processes
SAS Grid Manager on desktop monitor showing job activity metrics such as load average.

Divide and conquer.

Now you can get faster results and scale cost-effectively. Divide individual jobs into subtasks that are run in parallel on the best available hardware. Programs with large data sets and long run times, and those with replicate runs of independent tasks running against large data sets, are best-suited for multiprocessing. Make the most of all your available computing resources now, then add single-processing units as needed to keep IT spending in check.

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