# High Performance Computing

> **NIH NIH P01** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2020 · $255,307

## Abstract

Abstract 
The unprecedented progress in the area of technologies for generating genomic data has led to 
an imbalance where efforts to analyze these data is now becoming the bottleneck. Common 
methods in the statistician’s toolbox often falter in the face of these datasets which are massive 
not only in the number of data points but the dimension of parameters to be estimated. Each of 
the four projects will be faced with these challenges. It will be the responsibility of Core C to 
collaborate with project researchers in developing novel computational methods and tools that 
scale well. As an example, Project 1 will rely heavily on MCMC and high-dimensional 
regression. Fitting parameters with these statistical models entail massive number of iterations, 
so development of innovative approaches such as data-parallel algorithms for Graphics 
Processing Units will be a critical activity of the core. Other projects involve deploying extensive 
simulations that explore a constellation of model parameterizations, assumptions about disease 
effects, false discovery rates, etc. To this end, we will streamline such processes with re-usable 
code that can be easily tailored for specific simulation experiments.

## Key facts

- **NIH application ID:** 9991769
- **Project number:** 5P01CA196569-05
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** William JAMES GAUDERMAN
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $255,307
- **Award type:** 5
- **Project period:** — → —

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9991769

## Citation

> US National Institutes of Health, RePORTER application 9991769, High Performance Computing (5P01CA196569-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9991769. Licensed CC0.

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