Facilities, Equipment and Other Resources

CCS systems are collocated at the Verizon Terremark NAP of the Americas (NOTA or NAP). The NAP Datacenter in Miami currently features a 750,000 square foot, purpose-built datacenter, Tier IV facility with N+2 14 Megawatt power and cooling infrastructure. The equipment floors start at 32 feet above sea level, and the roof slope designed to aid in drainage of floodwater in excess of 100-year storm intensity assisted by: 18 rooftop drains, architecture designed to withstand a Category 5 hurricane with approximately 19 million pounds of concrete roof ballast, and 7-inch-thick steel reinforced concrete exterior panels. Plus, the building is outside FEMA 500-year designated flood zone. The NAP uses a dry pipe fire-suppression system to minimize the risk of damage from leaks.

 

Equipment

Advanced Computing

At present, CCS maintains several clusters and application servers:

 
Pegasus – CentOS 6.5 based batch/interactive compute cluster consisting of:

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  10,000 cores. IBM IDataPlex/Blade system
  
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  Diverse operating environments (Intel Xeon, Intel Phi, AMD processors)
  
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  19TB of RAM
  
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  Dedicated graphical nodes (Pegasus-gui)
  
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  Dedicated data transfer nodes with direct connection to I2 (Aspera, GridFTP, SFTP)
  
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  250+ programs, compilers, and libraries
  

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  184 cores
  
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  1 TB RAM
  
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  Graphical access from all nodes
  
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  Firewalled access to all resources
  
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  1 PB+ of storage
  

Elysium – CentOS 6.5 based secure data processing cluster (HIPAA/IRB compliant)

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  32 cores
  
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  128 GB RAM
  
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  Separate VLAN
  
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  Restricted access (MAC authentication/user ACL’s enforced)
  
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  Full auditing and attestation
  
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  500 TB
  

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  32 cores
  
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  128 GB RAM
  
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  CIFS/NFS/FTP/HTTP access
  
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  500 TB
  

CCS offers an integrated storage environment for both structured (relational) and unstructured (flat file) data. These systems are specifically tuned for CCS’ data type and application requirements, whether they are serial access or highly parallelized. Each investigator or group has access to its own area and can present his or her data through a service-oriented architecture (SOA) model. Researchers can share their data via access control lists (ACLs), which ensure data integrity and security while allowing flexibility for collaboration.

CCS offers structured data services through the most common relational database formats, including: Oracle, MySQL, and PostgreSQL. Investigators and project teams can access their space through SOA and utilize their resources with the support of an integrated backend infrastructure.

The CCS flat file storage environment is built as a multi-tier solution combining high-speed storage with dense high capacity storage in a tiered architecture, all supported by IBM’s GPFS.  Our HPC/Global tier (700TB) is available on all compute nodes.  This storage is designed for massively parallel work and has been clocked at 157,000 IOP/sec and over 20 GB/sec bandwidth.

Our standard tier of storage (2.8 PB) is designed for general-purpose data storage, analysis, and presentation of data to collaborators both within and without the University of Miami.  All tier 2 storage is available from all systems including our visualization cluster.  Several data management tools are available for tier 2 storage including public presentation, long-term archive, deduplication, encryption, and HSM.

Our archival tier of storage (2.5 PB) leverages several platforms for keeping critical data safe.  By using a combination of tape and disk technologies, we are able to reduce restore times significantly while still ensuring data integrity.

 

HPC Core Expertise

The HPC team has in-depth experience in various scientific research areas with extensive experience in parallelizing or distributing codes written in Fortran, C, Java, Perl, Python and R. The  team is active in contributing to Open Source software efforts including: R, Python, the Linux Kernel, Torque, Maui, XFS and GFS. The team also specializes in scheduling software (LSF) to optimize the efficiency of the HPC systems and adapt codes to the CCS environment. The HPC core has  expertise in parallelizing code using both MPI and OpenMP depending on the programming paradigm. CCS has contributed several parallelization efforts back to the community in projects such as R, WRF, and HYCOM.

The core specializes in implementing and porting open source codes to CCS’ environment and often contributes changes back to the community. CCS currently supports more than 300 applications and optimized libraries on its computing environment. The core personnel are experts in implementing and designing solutions in the three different variants of Unix. CCS also maintains industry research partnerships with IBM, Schrodinger, Open Eye, and DDN.

 

Software

HPC users have a complete software suite at their fingertips, including standard scientific libraries and numerous optimized libraries and algorithms tuned for the computing environment. All programs and algorithms are implemented in 64-bit mode in order to address large memory problems, and also offer compatible 32-bit libraries and algorithms. In addition, the LSF grid scheduling process maximizes the efficiency of the computational resources. Increased efficiency translates into the faster execution of programs, which provides researchers faster access to more resources. By utilizing the full suite of LSF tools we are able to provide both batch and interactive workloads while still retaining workload management features.

For more details about our HPC infrastructure, please visit our pages on this website at http://www.ccs.miami.edu/hpc

 

Other Resources

CCS’ Computational Biology and Bioinformatics Program (CBBP) was established to conduct research and offer services and training in the management and analysis of biological and medical/health record data. The program’s mission is to spearhead bioinformatics capacity at the University of Miami for all biological and medical applications. This includes data management, data mining, and data analysis capacities. The CBBP aims to achieve this mission through infrastructure, education, and expertise. In particular, CBBP provides an online portal for bioinformatics databases and web tools, and offers a number of data analysis services. CBBP are concomitantly leading educational and training initiatives in bioinformatics analysis, and nourishing these activities with high impact bioinformatics research.

 

Bioinformatics Data Analysis

The team provides data analysis training and expertise at a three levels, consulting, preliminary data generation, and fully collaborative, based on the time and complexity of the service requested. The analyses are undertaken by skilled analysts, and overseen by experienced faculty. The group has been working mostly with microarray data and next generation sequencing data, and the analytical services include, but are not limited to, the following:

• gene expression analysis for transcriptome profiling and/or gene regulatory network building,

• prognostics and/or diagnostic biomarker discovery,

• microRNA target analysis,

• copy number variant analysis, in this context we are testing the few existing algorithms and developing new ones for accurate and unambiguous discovery of copy number variation in the human genome,

• genome or transcriptome assembly from next generation sequencing data, and its visualization,

• SNP functionality analysis,

• other projects include merging or correlating data from various data types for a holistic view of a particular pathway or disease process.

 

Advanced Data Mining

The Center’s Data Mining Research Group provides advanced data mining expertise and capabilities to further explore high dimensional data. The following are examples of the expertise areas covered by our faculty.

• Classification, which appears essentially in every subject area that involves collection of data of different types, such as disease diagnosis based on clinical and laboratory data. Methods include regression (linear and logistic), artificial neural nets (ANN), k-nearest neighborhood (KNN), support vector machines (SVM), Bayesian networks, decision trees and others.

• Clustering, which is used to partition the input data points into mutually similar groupings, such that data points from different groups are not similar. Methods include KMeans, hierarchical clustering, and self-organizing map (SOM), and are often accompanied by space decomposition methods to offer low dimensional representations of high dimensional data space. Methods of space decomposition include principal component analysis (PCA), independent component analysis (IDA), multidimensional scaling (MDA), Isomap, and manifold learning. Advanced topics in clustering include multifold clustering, graphical models, and semi-supervised clustering.

• Association data mining, which finds frequent combinations of attributes in databases of categorical attributes. The frequent combinations can be then used to develop prediction of categorical values.

• Analysis of sequential data involves mostly biological sequence and includes such diverse topics as extraction of common patterns in genomic sequences for motif discovery, sequence comparison for haplotype analysis, alignment of sequences, and phylogeny reconstruction.

• Text mining, particularly in terms of extracting information from published papers, thus transforming documents to vectors of relatively low dimension to enable the use of data mining methods mentioned above.

 

Visualization

The Visualization program conducts both theoretical and applied research in the general areas of Machine Vision and Learning, and specifically in

(i) computer vision and image processing,

(ii) machine learning,

(iii) biomedical image analysis, and

(iv) computational biology and neuroscience.

The goal is to provide expertise n this area to develop novel fully automated methods that can provide robustness, accuracy and computational efficiency. The program works towards finding better solutions to existing open problems in the above areas, as well as exploring different scientific fields where our research can provide useful interpretation, quantification and modeling.

 

Cheminformatics

CCS has a sophisticated cheminformatics and compute infrastructure with a significant level of support from the institution.  CCS facilitates scientific interactions and enables efficient research using informatics and computational approaches.  A variety of departments and centers at the University use high content and high throughout screening approaches – The Miami Project to Cure Paralysis, the Diabetes Research Institute, the Cancer Center, Bascom Palmer Eye Institute, the Dept. of Surgery, the John P. Hussman Institute for Human Genomics.

Cheminformatics and computational chemistry tools—running on HPC Linux cluster and high performance application server.

• Leadscope Enterprise—integrated cheminformatics data mining and visualization environment; unique chemical perception (~27K custom keys; user extensions); various algorithms, HTS analysis, SAR / R-group analysis, data modeling.

• ChemAxon Tools and Applications—cheminformatics software applications and tools; wide variety of cheminformatics functionality.

• Spotfire—highly interactive visualization and data analysis environment, various statistical algorithms with chemical structure visualization, HTS and SAR analysis.

• Open Eye ROCS, FRED, OMEGA, EON, etc. implemented on Linux cluster  – suite of powerful applications and tool kits for high-throughput 3D manipulation of chemical structures, modeling of shape, electrostatics, protein-ligand interactions and various other aspects of structure- and ligand-based design; also includes powerful cheminformatics 2D structure tools.

• Schrodinger Glide, Prime, Macromodel, and various other tools implemented on Linux Cluster—powerful state of the art docking, protein modeling and structure prediction tools and visualization.

• Desmond implemented on Linux Cluster—powerful state of the art explicit solvent molecular dynamics.

• TIP workgroup—powerful environment for global analysis of protein structures, binding sites, binding interactions; implemented automated homology modeling, binding site prediction, structure and site comparison for amplification of known protein structure space.