John Cavazos' Research Lab targets research in high-performance computing, cybersecurity, machine learning, predictive analytics, and the application of these technologies to hard problems that if solved will make a societal and/or industrial impact.
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This project contains codes for Black-Scholes, Monte-Carlo, Bonds, and Repo financial applications which can be run on the CPU and GPU. All original algorithms were ported from QuantLib to CUDA, OpenCL, HMPP, and OpenACC. We showed that certain algorithms were able to achieve several hundred times speedup over sequential CPU.
PolyBench is a collection of benchmarks containing static control parts. The purpose is to uniformize the execution and monitoring of kernels, typically used in past and current publications. PolyBench/ACC originated from Pouchet's original PolyBench/C suite. We added CUDA, OpenCL, OpenACC, HMPP, and OpenMP versions of the original code.
PolyBench is a collection of benchmarks containing static control parts. The purpose is to uniformize the execution and monitoring of kernels, typically used in past and current publications. PolyBench/ACC originated from Pouchet's original PolyBench/C suite. We added CUDA, OpenCL, OpenACC, HMPP, and OpenMP versions of the original code.
A Framework For Analyzing Database Access Patterns Within A Company. We have developed a framework for analyzing database access patterns within a company based on the SQL queries the company's employees submit. Specifically, our goal is to uncover similarities between analysts that were previously unknown. This can lead to new collaborations within the company, as well as provide management with a tool to help maximize workforce efficiency.