- Semesters - Project Work by Semester
- [System Description]
- Experiments - List of speech experiments
- Torque is an open source resource manager providing control over batch jobs and distributed compute nodes. One can setup a home or small office Linux cluster and queue jobs with this software. For the purpose of this course, Torque is used to queue experiments to the server in order to drastically reduce the amount of time the experiments take. A cluster consists of one head node and many compute nodes. The head node runs the torque-server daemon and the compute nodes run the torque-client daemon. The head node also runs a scheduler daemon.
- While Torque has a built-in scheduler, pbs_sched, it is typically used solely as a resource manager with a scheduler making requests to it. Resources managers provide the low-level functionality to start, hold, cancel, and monitor jobs. Without these capabilities, a scheduler alone cannot control jobs.
- While Torque is flexible enough to handle scheduling a conference room, it is primarily used in batch systems. Batch systems are a collection of computers and other resources (networks, storage systems, license servers, and so forth) that operate under the notion that the whole is greater than the sum of the parts. Some batch systems consist of just a handful of machines running single-processor jobs, minimally managed by the users themselves. Other systems have thousands and thousands of machines executing users' jobs simultaneously while tracking software licenses and access to hardware equipment and storage systems. Pooling resources in a batch system typically reduces technical administration of resources while offering a uniform view to users. Once configured properly, batch systems abstract away many of the details involved with running and managing jobs, allowing higher resource utilization. For example, users typically only need to specify the minimal constraints of a job and do not need to know the individual machine names of each host on which they are running. With this uniform abstracted view, batch systems can execute thousands and thousands of jobs simultaneously. Batch systems are comprised of four different components: (1) Master Node, (2) Submit/Interactive Nodes, (3) Compute Nodes, and (4) Resources.