The Advanced Computing Facility
The Advanced Computing Facility provides researchers at the University of Kansas access to high-performance computing power. The ACF houses servers from the Information and Telecommunications Technology Center (ITTC) and the KU Community Cluster which is managed by the Center for Research Computing (CRC). The ACF is located at Nichols Hall.
The ACF serves an exceptionally diverse range of researchers from all KU campuses, including chemists, biologists, pharmaceutical scientists and engineers. Research activities include computer modeling and simulation high dimensional data processing. Servers are available for researchers and general access, and knowledgeable staff with expertise in data-intensive research help support users.
Completed in 2013, the ACF project provided a major expansion of floor space dedicated to higher-performance computing, adding 32 new high-density hardware racks and the capability for supporting more than 24,000 processing cores. In addition, to ensure reliability, the project included a 1,500-kilowatt emergency backup generator, a 500-kilowatt modular uninterruptable power supply and a complete retrofit of the Nichols Hall electrical distribution panels.
The facility also provides a more energy-efficient and sustainable solution to KU’s expanding high-performance computing needs. For example, the ACF recovers heat generated by the computing hardware to supplement the Nichols Hall boilers, significantly reducing the consumption of natural gas in the building. In addition, when outside temperatures fall below 45 degrees, the chilled water plant compressors are powered down, and a passive “dry cooler” supports equipment cooling. This reduces electricity consumption dramatically.
A $4.7 million grant from the National Center for Research Resources at NIH as part of the 2009 American Recovery and Reinvestment Act helped fund the ACF. The project was led by Luke Huan, associate professor of electrical engineering and computer science and director of the Bioinformatics and Computational Life Sciences Laboratory at ITTC. The project received additional support from the KU Office of Research and Graduate Studies and KU Information Technology.