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Principal Investigator: Philip Bradley
Organization: FRED HUTCHINSON CANCER RESEARCH CENTER
Fiscal Year: 2020
Award: $2,000,000
Funding agency: NIH Office of the Director
Project Summary/Abstract
Fred Hutch respectfully requests funds to upgrade the current 523-node, 3328-core high performance computing
(HPC) cluster, which was created in 2004 and expanded in 2006, 2010 (S10 funds), 2013, 2015 (S10 funds),
and 2018. 456 end of life nodes (1824 cores) will be replaced and 144 nodes (3456 cores) will be added, creating
a new 211-node, 4996-core system with an overall 50% increase in core count, 60% increase in processing
power, and more than 100% increase in memory capacity over the old system. The expanded capacity will
enable deep and efficient analysis of our research studies and accommodate 20% annual growth in computing
intensive research, much of which is not possible on the current cluster. The core user group for the new HPC
cluster consists of at least 37 NIH-funded research groups participating in this proposal, however as much as 85
groups use the cluster regularly. Their biomedical research is aimed at eradicating cancer and other diseases
and dependent on computationally intensive technical approaches such as development of novel statistical
analysis or machine learning methods, for example for assessing immune correlates to facilitate vaccine
development, analyzing large scale clinical trials or to develop software tools for the analysis of large-scale
immunological datasets, DNA and RNA sequencing, modeling prostate cancer outcomes, studies of the human
microbiome, modeling of cancers, mRNA, miRNA, and structural variant detection, structural biology with Cryo-
EM, modeling of infectious agents and pandemics, computational modeling, prediction and design of
macromolecular structures and interactions, identifying drivers of neoplasia and an international consortium
improving colorectal cancer detection using GWAS, whole genome sequencing and genome-wide gene-
environment (GxE) studies as well as research in diabetes, mhealth and cardiovascular diseases. Several of the
Major Users at Fred Hutch are currently experiencing substantial delays in accomplishing their work using the
current cluster. Others have projects that cannot be done at all on the existing instrument. (see Research Projects
section for details). The Scientific Computing department (SciComp) has operated the current HPC cluster for
more than 10 years and has a staff with a combined experience of over 150 years. The proposed new HPC
cluster will be installed in available space in a Fred Hutch datacenter. The expanded cluster will address both
immediate and future needs of our user community, supporting NIH-funded research at Fred Hutch. Funded
research at our Center will greatly benefit from the increased data-processing capacity and improved
performance of the requested HPC cluster, including applications of machine learning to the study of clinical trial
efficacy, comparison of immune system receptors to identify responses to specific pathogens/diseases, modeling
of carcinogenesis. Besides multiple infectious diseases Fred Hutch researches all types of cancer and our
computationally intensive investigators tend to focus on colon, prostate, brain, Barrett’s esophagus, lung and
liquid tumors such as leukemia.
Terms: <Address><Barrett Esophagus><Barrett Syndrome><Barrett Ulcer><Biomedical Research><Brain><Brain Nervous System><CRC screening><Cancer Induction><Cancer Model><CancerModel><Cancers><Cardiovascular Diseases><Clinical Trials><Colon><Columnar Epithelial-Lined Lower Esophagus><Columnar-Lined Esophagus><Communicable Diseases><Communities><Computer Models><Computer Software Tools><Computerized Models><Cryo-electron Microscopy><Cryoelectron Microscopy><DNA seq><DNA sequencing><DNAseq><Data Set><Dataset><Development><Diabetes Mellitus><Disease><Disease model><Disorder><Electron Cryomicroscopy><Encephalon><Environment><Funding><Future><GWA study><GWAS><Generalized Growth><Genes><Growth><High Performance Computing><Human Microbiome><Immune><Immune system><Immunes><Immunochemical Immunologic><Immunologic><Immunological><Immunologically><Immunologics><Infectious Agent><Infectious Disease Pathway><Infectious Diseases><Infectious Diseases / Laboratory><Infectious Diseases Research><Infectious Disorder><International><Investigators><Liquid substance><Lung><Lung Respiratory System><Machine Learning><Macromolecular Structure><Malignant Neoplasms><Malignant Tumor><Memory><Messenger RNA><Micro RNA><MicroRNAs><Microbiomics><Modeling><Molecular Structure><NIH><National Institutes of Health><Neoplasms><Performance><Prostate><Prostate Cancer Outcomes Study><Prostate Gland><Prostatic Gland><R-Series Research Projects><R01 Mechanism><R01 Program><RNA Seq><RNA sequencing><RNAseq><Receptor Protein><Research><Research Grants><Research Personnel><Research Project Grants><Research Projects><Researchers><Software Tools><Statistical Data Analyses><Statistical Data Analysis><Statistical Data Interpretation><Structure><System><Tissue Growth><United States National Institutes of Health><Work><allergic/immunologic body system><allergic/immunologic organ system><cancer type><carcinogenesis><cardiovascular disorder><cluster computing><colo-rectal cancer detection><colo-rectal cancer early detection><colo-rectal cancer screening><colorectal cancer detection><colorectal cancer early detection><colorectal cancer screening><computational modeling><computational models><computer based models><computerized data processing><computerized modeling><cryo-EM><cryoEM><data grid><data processing><datagrid><design><designing><detect colo-rectal cancer><detect colorectal cancer><develop a vaccine><develop software><developing computer software><development of a vaccine><developmental><diabetes><disorder model><distributed computing><efficacy clinical trial><end of life><end-of-life><entire genome><esophageal intestinal metaplasia><experience><fluid><full genome><genome scale><genome sequencing><genome wide association><genome wide association scan><genome wide association studies><genome wide association study><genome-wide><genomewide><genomewide association scan><genomewide association studies><genomewide association study><high-end computing><human-associated microbiome><improved><infectious organism><instrument><leukemia><liquid><m-Health><mHealth><mRNA><machine learned><machine learning method><malignancy><miRNA><miRNAs><microbiome research><microbiome science><microbiome studies><mobile health><neoplasia><neoplasm/cancer><neoplastic growth><novel><ontogeny><pandemic><pandemic disease><pathogen><prostate cancer model><prostate tumor model><pulmonary><receptor><research study><response><scientific computing><software development><statistical analysis><structural biology><transcriptome sequencing><tumor><vaccine development><vaccine formulation><variant detection><whole genome><whole genome association analysis><whole genome association studies><whole genome association study>