Abstract

GPU Accelerated Data Science

Data Science/Data Mining is the exploration of data to extract novel knowledge and insight. That discovery process often involves a considerable amount of trial and error, after all, if you know what you are looking for you are not doing discovery. The Python programming language has grown in popularity amount data scientists for its flexibility, ease of programming, and readability. 


Session No: SIL8136
Speaker: Keith Kraus
Type:

Accelerated Data Analytics

Date: Thursday - October 18, 2018 03:00 PM - 03:45 PM
Location: Hall I
Topic: Accelerated Data Science
Industry: Software

Data Science/Data Mining is the exploration of data to extract novel knowledge and insight. That discovery process often involves a considerable amount of trial and error, after all, if you know what you are looking for you are not doing discovery. The Python programming language has grown in popularity amount data scientists for its flexibility, ease of programming, and readability. However, Python is not known for performance, which has not been an issue in the past. Unfortunately, today, a large amount of science is driven through the exploration of large volumes of data. Combined with the ever-increasing need for more complex algorithms and analytics, data scientists have had to turn more and more of their attention away from the problems they're trying to solve and instead towards implementing their hypotheses in less friendly, "more performant" systems. Luckily, work being done in the GPU Open Analytics Initiative (GOAI) is pushing to make GPU-accelerated Data Science in Python a first class citizen and driving performance to be on par with the other languages, including GPU-accelerated C/C++.