Condor is a system in distributed computing, designed to harness the collective power of numerous interconnected computers. It transforms idle computing resources into a powerful, shared infrastructure for large-scale computational problems. It allows organizations and researchers to manage and execute vast numbers of independent computational tasks efficiently. By pooling otherwise unused processing power, Condor offers a scalable solution for complex calculations that would overwhelm a single machine.
Understanding Condor
Condor is a high-throughput computing (HTC) system, distinct from high-performance computing (HPC). While HPC focuses on delivering immense computational power over short durations, HTC prioritizes sustained processing capacity over extended periods, often months or years. Condor utilizes idle computing resources across a network, scavenging unused CPU cycles from various machines to run a large volume of independent or loosely coupled tasks. This system originated at the University of Wisconsin–Madison, with its first production installation in the Computer Sciences department in the 1990s.
The association with “IBM Condor” often stems from collaborations or distribution efforts, not IBM’s fundamental creation. Condor, later known as HTCondor, was developed and continues to be maintained by the Center for High Throughput Computing at the University of Wisconsin–Madison. Its core development has always been rooted in academic research, supporting HTC on diverse, distributively owned computing resources.
How Condor Facilitates Computing
Condor pools and manages computing resources. It collects idle CPU cycles from machines across a network, creating a large, dynamic pool of processing power. This resource pooling allows an organization to leverage machines that might otherwise sit unused, such as desktop computers during off-hours.
The system employs job scheduling to match computational tasks with available resources. Users submit jobs to Condor, which places them into a queue and assigns them to suitable machines based on requirements and resource availability. This matchmaking process uses a flexible framework known as ClassAds, where both machines and jobs advertise their properties and preferences, ensuring efficient allocation.
Condor’s checkpointing mechanism provides robustness and fault tolerance. Checkpointing allows a job to periodically save its state, including data and open files. If a machine becomes unavailable or a job needs to be moved, Condor can pause the task, transfer its state, and resume it on a different machine without losing significant progress. This capability benefits long-running computations, preventing costly restarts and maximizing throughput.
Real-World Applications of Condor
Condor, and its evolution into HTCondor, has impacted various fields by enabling large-scale data processing and simulations. In scientific research, it has provided computing power for disciplines such as physics, biology, and chemistry. For instance, it has been used for complex simulations in high-energy physics, including those at CERN for particle physics research.
The system has also supported genomic research, allowing scientists to process genetic data for projects like the initial draft assembly of the Human Genome. In fields like environmental science, HTCondor facilitates computationally intensive tasks such as climate projections, hydrological models, and the analysis of large datasets from sources like high-resolution imagery and lidar. Engineering applications also benefit, with Condor enabling simulations for design optimization, such as stress analysis on structures or wind tunnel simulations for vehicles. HTCondor’s ability to manage thousands of independent jobs has allowed researchers and organizations to tackle problems impractical on single machines, accelerating discovery across a wide range of domains.
Condor’s Enduring Legacy
Condor’s evolution into HTCondor signifies its continued relevance in distributed computing. The system remains actively developed by the University of Wisconsin–Madison and is widely used in academic and research environments globally. Its influence extends to modern distributed computing paradigms, including grid computing, where it serves as a component for sharing resources across organizational boundaries.
HTCondor also integrates cloud resources into existing computing pools or builds entire HTCondor pools within cloud environments. While “IBM Condor” might be less common today, HTCondor persists as an open-source framework for managing compute-intensive tasks. Ongoing development and widespread adoption demonstrate HTCondor’s enduring impact on how large-scale computational problems are approached and solved.