Computational Resources
Loyola University Chicago Health Sciences Campus (LUC-HSC) include a state-of-the-art 2,200 sq. ft. research computing center supported by a 500KW uninterruptable power supply (UPS), a 1MW external diesel generator and redundant 100T cold water chilling units. The facility has 33 42U racks (expandable to 40 racks) each fed by redundant and diverse conditioned electrical subsystems. Currently, the facility operates more than 180 physical and 60 virtual servers that provide more than 2.5 petabytes of storage.
A backup disaster recovery and business continuity (DR/BC) facility is provided by a second diverse computing center that is located in the LUC Center for Translational Research and Education (CTRE). This facility has 12 42U equipment racks that house backup servers and storage units. This facility is supported by diverse environmentals including UPS, generator and dedicated chillers.
Requests for access to computing resources can be submitted by faculty using the Research Computing Resource Access request form.
Computing Resources Available to Researchers:
(Note: A limited number of accounts are available by request, subject to project review and approval. Some accounts may be conditioned upon meeting an IRB requirement)
- High-Performance Computing Cluster (HPCC)
- High-Memory Computing
- High-Performance Molecular Visualization Cluster
- High-Performance GPU Server
- Online Surveys and Databases (REDCap)
- Research File Server (IRB required)
Other Computing Resources Used in Research:
- Production (CRDB) Hadoop Cluster
- Production (ARIA) Hadoop Cluster
- General Scientific Hadoop Cluster
- High-Performance GPU Servers
You can request access to an RCS high performance computing resource by filling out a form on the ITS Service Portal: RCR Request Form
Computing Resources Available to Researchers
High-Performance Computing Cluster (HPCC)
This Linux-based ROCKS cluster features 65 server compute nodes with 528 processing cores and 1PB of storage configured with redundant data volumes shared as GPFS clustered file systems. This general-purpose computing cluster provides a wide range of open source software (e.g, Galaxy, BLAST, GROMACS, etc.) utilized in genomics and basic science research.
High-Memory Computing
These symmetric multiprocessing (SMP) servers each with 24 processing cores and large random-access memory are provided for analyses that require large physical memory spaces. These SMP servers have access to the GPFS file systems that are attached to HPCC resources.
High-Performance Molecular Visualization Cluster
This Linux-based ROCKS cluster featuring nine server compute nodes with 560 processing cores and 40TBs of file system storage dedicated to molecular structure and movement visualizations.
High-Performance GPU Server
This Linux-based GPU server features 40 processing cores and large random-access memory. This server will offer performance and quality optimized for Deep Learning using open-source software, such as TensorFlow and Theano.
Online Surveys and Databases (REDCap)
Loyola has two general purpose REDCap environments for use by clinical researchers. The primary environment has been operational for four years and currently has 1,329 user accounts with 390 product projects. More information about REDCap can be found at the REDCap page.
Research File Server
This Windows-based file/print server features 15TB of storage for use of storing research data associated with Institutional Review Board (IRB)-approved projects.
Other Computing Resources Used in Research
Production (CRDB) Hadoop Cluster
A Linux-based Hadoop (Cloudera) cluster with 15 data nodes providing 265TBs of Hadoop distributed file system (HDFS) storage. The cluster provides Hive, python and java frameworks for “big-data” processing related to advanced clinical analytics.
Production (ARIA) Hadoop Cluster
A Linux-based Hadoop (Cloudera) cluster with 16 data nodes providing 800TBs of Hadoop distributed file system (HDFS) storage. The cluster provides Hive, python and java frameworks for clinical “data laking” and distributed processing related to advanced clinical analytics. The focus of this cluster is to support natural language processing (NLP) and radiomics. The ARIA cluster includes three symmetric multiprocessing (SMP) servers and enough random-access memory to support analysis that require large physical memory spaces. These SMP servers have access to the HDFS file systems maintained by the ARIA cluster.
General Scientific Hadoop Cluster
A Linux-based Hadoop (Cloudera) cluster with 13 data nodes providing 21TBs of Hadoop distributed file system (HDFS) storage. The cluster provides Hive, python and java frameworks for “big-data” processing relating to general scientific processes.
High-Performance GPU Servers
Loyola has three Linux-based GPU servers and one Windows-based GPU server with 6-20 processing cores dedicated for clinical and biomedical research. These servers offer performance and quality optimized for Deep Learning using open-source software such as TensorFlow and Theano. Common uses of these servers include modeling involving natural language processing and image data. The Windows-based server is dedicated to run Deep Learning research for detection of suspicious prostate cancer areas on an MRI.
Loyola University Chicago Health Sciences Campus (LUC-HSC) include a state-of-the-art 2,200 sq. ft. research computing center supported by a 500KW uninterruptable power supply (UPS), a 1MW external diesel generator and redundant 100T cold water chilling units. The facility has 33 42U racks (expandable to 40 racks) each fed by redundant and diverse conditioned electrical subsystems. Currently, the facility operates more than 180 physical and 60 virtual servers that provide more than 2.5 petabytes of storage.
A backup disaster recovery and business continuity (DR/BC) facility is provided by a second diverse computing center that is located in the LUC Center for Translational Research and Education (CTRE). This facility has 12 42U equipment racks that house backup servers and storage units. This facility is supported by diverse environmentals including UPS, generator and dedicated chillers.
Requests for access to computing resources can be submitted by faculty using the Research Computing Resource Access request form.
Computing Resources Available to Researchers:
(Note: A limited number of accounts are available by request, subject to project review and approval. Some accounts may be conditioned upon meeting an IRB requirement)
- High-Performance Computing Cluster (HPCC)
- High-Memory Computing
- High-Performance Molecular Visualization Cluster
- High-Performance GPU Server
- Online Surveys and Databases (REDCap)
- Research File Server (IRB required)
Other Computing Resources Used in Research:
- Production (CRDB) Hadoop Cluster
- Production (ARIA) Hadoop Cluster
- General Scientific Hadoop Cluster
- High-Performance GPU Servers
You can request access to an RCS high performance computing resource by filling out a form on the ITS Service Portal: RCR Request Form
Computing Resources Available to Researchers
High-Performance Computing Cluster (HPCC)
This Linux-based ROCKS cluster features 65 server compute nodes with 528 processing cores and 1PB of storage configured with redundant data volumes shared as GPFS clustered file systems. This general-purpose computing cluster provides a wide range of open source software (e.g, Galaxy, BLAST, GROMACS, etc.) utilized in genomics and basic science research.
High-Memory Computing
These symmetric multiprocessing (SMP) servers each with 24 processing cores and large random-access memory are provided for analyses that require large physical memory spaces. These SMP servers have access to the GPFS file systems that are attached to HPCC resources.
High-Performance Molecular Visualization Cluster
This Linux-based ROCKS cluster featuring nine server compute nodes with 560 processing cores and 40TBs of file system storage dedicated to molecular structure and movement visualizations.
High-Performance GPU Server
This Linux-based GPU server features 40 processing cores and large random-access memory. This server will offer performance and quality optimized for Deep Learning using open-source software, such as TensorFlow and Theano.
Online Surveys and Databases (REDCap)
Loyola has two general purpose REDCap environments for use by clinical researchers. The primary environment has been operational for four years and currently has 1,329 user accounts with 390 product projects. More information about REDCap can be found at the REDCap page.
Research File Server
This Windows-based file/print server features 15TB of storage for use of storing research data associated with Institutional Review Board (IRB)-approved projects.
Other Computing Resources Used in Research
Production (CRDB) Hadoop Cluster
A Linux-based Hadoop (Cloudera) cluster with 15 data nodes providing 265TBs of Hadoop distributed file system (HDFS) storage. The cluster provides Hive, python and java frameworks for “big-data” processing related to advanced clinical analytics.
Production (ARIA) Hadoop Cluster
A Linux-based Hadoop (Cloudera) cluster with 16 data nodes providing 800TBs of Hadoop distributed file system (HDFS) storage. The cluster provides Hive, python and java frameworks for clinical “data laking” and distributed processing related to advanced clinical analytics. The focus of this cluster is to support natural language processing (NLP) and radiomics. The ARIA cluster includes three symmetric multiprocessing (SMP) servers and enough random-access memory to support analysis that require large physical memory spaces. These SMP servers have access to the HDFS file systems maintained by the ARIA cluster.
General Scientific Hadoop Cluster
A Linux-based Hadoop (Cloudera) cluster with 13 data nodes providing 21TBs of Hadoop distributed file system (HDFS) storage. The cluster provides Hive, python and java frameworks for “big-data” processing relating to general scientific processes.
High-Performance GPU Servers
Loyola has three Linux-based GPU servers and one Windows-based GPU server with 6-20 processing cores dedicated for clinical and biomedical research. These servers offer performance and quality optimized for Deep Learning using open-source software such as TensorFlow and Theano. Common uses of these servers include modeling involving natural language processing and image data. The Windows-based server is dedicated to run Deep Learning research for detection of suspicious prostate cancer areas on an MRI.