# Introduction of HTCondor ## What is HTCondor [HTCondor](https://research.cs.wisc.edu/htcondor/index.html) is a specialized workload management system for compute-intensive jobs. Like other full-featured batch systems, HTCondor provides a job queueing mechanism, scheduling policy, priority scheme, resource monitoring, and resource management. Users submit their serial or parallel jobs to HTCondor, HTCondor places them into a queue, chooses when and where to run the jobs based upon a policy, carefully monitors their progress, and ultimately informs the user upon completion. HTCondor is the product of years of research by the [Center for High Throughput Computing](https://chtc.cs.wisc.edu/) in the Department of Computer Sciences at the University of Wisconsin-Madison (UW-Madison), and it was first installed as a production system in the UW-Madison Department of Computer Sciences over 15 years ago. This HTCondor installation has since served as a major source of computing cycles to UW-Madison faculty and students. Additional HTCondor installations have been established over the years across campus and the world ([CERN](https://information-technology.web.cern.ch/services/fe/lxbatch/howto/quickstart-guide-htcondor), [UIUC](https://engrit.illinois.edu/services/research-services/campus-research-computing-options), [UT Austin](https://www.cs.utexas.edu/facilities/documentation/condor), [NJIT](https://ist.njit.edu/htcondor), etc). Hundreds of organizations in industry, government, and academia have used HTCondor to establish compute installations ranging in size from a handful to many thousands of workstations. The [HTCondor software, source code](http://research.cs.wisc.edu/htcondor/downloads/), and [complete documentation](http://research.cs.wisc.edu/htcondor/manual/) are freely available under an open source license. Linux, MacOS, and Windows platforms are supported. ## Features of HTCondor While providing functionality similar to that of a more traditional batch queueing system, HTCondor’s novel architecture allows it to succeed in areas where traditional scheduling systems fail. HTCondor can be used to manage a cluster of dedicated compute nodes (such as a “Beowulf” cluster). In addition, unique mechanisms enable HTCondor to effectively harness wasted CPU power from otherwise idle desktop workstations. For instance, HTCondor can be configured to only use desktop machines where the keyboard and mouse are idle. Should HTCondor detect that a machine is no longer available (such as a key press detected), in many circumstances HTCondor is able to transparently produce a checkpoint and migrate a job to a different machine which would otherwise be idle. HTCondor does not require a shared file system across machines - if no shared file system is available, HTCondor can transfer the job’s data files on behalf of the user, or HTCondor may be able to transparently redirect all the job’s I/O requests back to the submit machine. As a result, HTCondor can be used to seamlessly combine all of an organization’s computational power into one resource. The [ClassAd mechanism](http://research.cs.wisc.edu/htcondor/classad/classad.html) in HTCondor provides an extremely flexible and expressive framework for matching resource requests (jobs) with resource offers (machines). Jobs can easily state both job requirements and job preferences. Likewise, machines can specify requirements and preferences about the jobs they are willing to run. These requirements and preferences can be described in powerful expressions, resulting in HTCondor’s adaptation to nearly any desired policy. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://compsci-hunter.gitbook.io/xie-research-group/useful-tutorials/htcondor-tutorial/introduction-of-htcondor-tm.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.