High Performance Computing + Data Science = Competitive Advantage

High Performance Computing (HPC) plus data science allows public and private organizations get actionable, valuable intelligence from massive volumes of data and use predictive and prescriptive analytics to make better decisions and create game-changing strategies. The integration of computing resources, software, networking, data storage, information management, and data scientists using machine learning and algorithms is the secret sauce to achieving the fundamental goal of creating durable competitive advantage.

HPC has evolved in the past decade to provide "supercomputing" capabilities at significantly lower costs. Modern HPC uses parallel processing techniques for solving complex computational problems. HPC technology focuses on developing parallel processing algorithms and systems by incorporating both administration and parallel computational techniques.

HPC enables data scientists to address challenges that have been unmanageable in the past. HPC expands modeling and simulation capabilities, including using advanced data science techniques like random forests, monte carlo simulations, bayesian probability, regression, naive bayes, K-nearest neighbors, neural networks, decision trees and others.

Additionally, HPC allows an organization to conduct controlled experiments in a timely manner as well as conduct research for things that are too costly and time consuming to do experimentally. With HPC you can mathematically model and run numerical simulations to attempt to gain understanding via direct observation.

HPC technology today is implemented in multidisciplinary areas including:

• Finance and trading

• Oil and gas industry

• Electronic design automation

• Media and entertainment

• Biosciences

• Astrophysics

• Geographical data

• Climate research

In the near future both public and private organizations in many domains will use HPC plus data science to boost strategic thinking, improve operations and innovate to create better services and products.

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Tags: Computing, Data, High, K-nearest, Performance, Science, algorithms, bayes, bayesian, carlo, More…decision, forests, learning, machine, monte, naive, neighbors, networks, neural, probability, random, regression, simulations, trees


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Comment by Majid ALDOSARI on September 24, 2013 at 7:46am

Traditional HPC (those that number crunch to run simulations) isn't really part of the culture of statisticians and even "data scientists". Data scientists are all enchanted by the hadoop stack and don't realize that the scientific community has been working with parallel algorithms for decades.

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