» Grid Engine

Why Univa

  • Deliver higher quality of service
  • No dedicated Apache Hadoop cluster is needed
  • Access to on-demand cloud resources
  • Reduce execution times

Grid Engine™ Software

Grid Engine software is the most widely deployed distributed resource management software platform used by enterprises and research organizations across the globe.

Univa® has earned the reputation as a world leader in mid-market and large enterprise technical computing and Big Data through our leadership, global support and development of Grid Engine technology. The original development team of the product supports our leadership role.

We have already made substantial advances in large scale, high-throughput distributed computing that help our customers drive down their costs while operating at industry-leading utilization rates. Our innovative feature and product evolution ensures fast, reliable, secure and easy to manage performance of mixed workloads.

If you are a growing enterprise in need of optimizing mission-critical technical computing and big data applications, we welcome you to the industry’s most powerful and integrated solution suite.

Now, we are truly the “Home of Grid Engine” software and the only company with the expertise and means to further enhance this innovative solution.

Browse the topics below to see a variety of enhancements to Grid Engine software that are available exclusively by Univa:

Performance Begins with Univa Grid Engine

Univa® Grid Engine™ is a distributed resource management system that fosters increased utilization, better workload throughput, and higher end-user productivity by harnessing existing compute resources. By transparently selecting the resources that are best suited for each segment of work, Grid Engine software is able to distribute workloads efficiently across a resource pool while shielding end users from the inner workings of the compute cluster.

Unique Performance Features

  • Best Support

    As a customer, you don't just get support when you need it from Univa. You gain access to our exclusive expertise in the scheduler, policies and best practices. Our customers regularly learn from our knowledge and insight and apply it to their unique environment and configuration. Often this leads to customer requirements on our product roadmap.

    • Unsurpassed expertise and unbeatable services and support
  • Job Classes

    Increase your operational efficiency with Univa's advanced job management via Job Classes - the single largest functional improvement to Grid Engine in the past five years. Job Classes - essentially job or application templates - allows you to define defaults or group your workloads and streamline their management from attribute control and policy implementation to accounting.

    • Tuned workload means significant improvements in throughput
    • Avoid rogue jobs and poor cluster utilization by badly tuned workloads
  • Short Jobs

    Grid Engine users with an extreme number of short jobs can see dispatch times explode when they stuff hundreds of thousands of jobs into the scheduler. Univa's support for Postgres database job spooling balances speed of submission with reliability in high volume clusters with lots of small jobs.

    • Dramatic improvements in throughput at scale
  • Core & Non-Uniform Memory Access (NUMA) Binding

    We implemented changes in the scheduler to optimize the use of modern computer architectures and topology characteristics such as sockets, cores and memory banks. Our design ensures the best repeatable performance across different server vendor designs through automated, optimized core and NUMA selection. We can also guarantee that specific jobs have exclusive access to the required cores and memory segments for optimal performance.

    • Including NUMA binding improves application performance
    • Moving scheduling to the Master improves decision making and avoids collisions by guaranteeing a binding
  • Fair Urgency

    Our new scheduling algorithm that helps to ensure a balanced utilization of critical resource pools such as file servers. Fair Urgency is a standard means to avoid overloading and performance degradation.

    • Provides equitable access to critical resources
    • Elimination of overloading maintains performance at peak
  • New Documentation & Improved debugging and diagnostics

    We rewrote documentation to help you find administration and configuration information. These improvements and diagnostic additions help you to find the root cause of an issue without a need to reproduce the problems and avoid those issues before they hit again.

    • Reduce the time spent on diagnosing job related issues by up to 90%
    • Saves time again and again
  • Resource Maps

    When we were working on the NVIDIA GPU integration we realized that a mechanism to map resource units in use to jobs was not easy in Grid Engine. Specifically, when a job requested GPUs on a GPU-enabled host there was no easy way to tell Grid Engine that a particular GPU was attached to a job - so don't use it for another job. Also, there was no easy way to tell the Grid Engine Scheduler that a specific GPU should be used for a job. So we created Resource Maps - an extension to consumable resources and implemented using a new Univa Grid Engine complex attribute type called RSMAP.

    • Eliminates resource conflict

Obtain actionable intelligence with UniSight

UniSight™ is a reporting and analytics tool that allows organizations to measure, track and chargeback usage on Univa Grid Engine Clusters. UniSight provides organizations with the insight they need to make better decisions.

The solution comes pre-configured with sample reports for resources such as software, disk usage, people, etc. Any metric collected by Univa Grid Engine software can be reported and our special drill-down feature allows for ad-hoc reporting. UniSight has further simplified the administration overhead by removing ARCO to provide unrivalled usability and unmatched reliability.

Organizations can understand their usage and unique requirements for clusters such as:

  • Are the correct users getting access to the cluster?
  • Are Grid Engine software policies working?
  • Can we chargeback to departments for their usage?
  • Do we need to purchase more hardware?
  • Do we need to purchase more licenses?
  • How long do jobs wait in the queue?

» Learn More

» Read the data sheet

Monitor applications and license usage

Univa License Orchestrator™ enables maximum workload throughput for users, groups or projects with flexible sharing policies and simple configuration bringing administration efficiency.

Univa License Orchestrator integrates Univa Grid Engine software policies for managing the allocation of license features across users, groups or projects:

Functional Fixed minimal percentage by user, group (department) and project
Fairshare Establish fair share of license usage by user, group (department) and project. Supports the ability to lend and borrow license features while maintaining percentage-based access
Quotas Assign quotas and limits of usage per user, group or project
Deadline Obtain access no later than at a designated time
Urgency Automatic increase of access rights; over time or based on criticality of license need
Reservation Point in time usage


Univa License Orchestrator is designed by the same people who developed Grid Engine software - which has been battle-tested in thousands of mission critical environments across the globe - and this allows tight integration inside the scheduler which can only be found in Univa Grid Engine software.

Learn More »

Archimedes Slashed Hadoop Costs

  • Run One System – No Silos
  • Save up to 50% on Hardware
  • Accelerated Hadoop Deployment

» See case study

Key Resources

» Visit Resource Center

Reduce costs of deploying
Hadoop by 50%

The foundation of Big Data is infrastructure. Infrastructure is hardware and software that includes industry standard servers, storage, networking and clustering software. The most important element is the software that supports the applications. This is where Univa lives.

Univa software alleviates the challenges enterprises face when adopting Big Data solutions like Hadoop. Typically, organizations running Apache Hadoop for large-scale data processing had to run those applications on a dedicated cluster. Now, Univa® Grid Engine™ allows organizations to share infrastructure resources between Hadoop and other data center applications. This integration provides saving on hardware as well as the time and hassle of managing dual environments.

Univa Grid Engine software unifies Big Compute and Big Data workload management by making it possible to fully leverage and share existing clusters - ensuring the infrastructure is future-proofed.

Univa offers a range of benefits to Big Data applications:

  • Shared infrastructure reduces the costs of deploying Hadoop by up to 50%
  • Policy driven scheduling increases utilization and control
  • Support for dynamic and multiple instances of Hadoop and other applications on a shared cluster
  • Sharing supports high utilization and resource availability

Easily Connect Grid Engine Software to Clouds

  • Integrate existing workflow into Cloud

  • Configuration Flexibility
    • Private Cloud
    • CloudCluster
    • CloudBurst
    • Hybrid Cloud
  • One Click HPC
  • Synchronized Infrastructure Automation

Access on-demand cloud resources

The volume of high priority workloads grows daily within data centers, yet there is no room for errors or slow response times. Internal and external customers should not have to wait for resources and services because their IT infrastructure can‘t manage jobs effectively.

Using Univa‘s UniCloud™ you can not only meet current needs more productively, but also expand your business and save money on infrastructure at the same time. UniCloud plugs Univa Grid Engine software into any cloud management system or service enabling public and private cloud resources to be utilized with existing HPC workflow.

  • Provision applications to standalone, virtual or Cloud servers
  • Automatically scale application environments in response to increasing workload
  • Move resources from another running application to a higher priority workload
  • » Learn More