SC16 Salt Lake City, UT

37. Enabling K-Nearest Neighbor Algorithm Using a Heterogeneous Streaming Library: hStreams


Authors: Jesmin Jahan Tithi (Intel Corporation)

Abstract: hStreams is a recently proposed (IPDPSW 2016) task-based target-agnostic heterogeneous streaming library that supports task concurrency on heterogeneous platforms. In this poster, we share our experience of enabling a non-trivial machine learning (ML) algorithm: K-nearest neighbor using hStreams. The K-nearest neighbor (KNN) is a popular algorithm with numerous applications in machine learning, data-mining, computer vision, text processing, scientific computing such as in computational biology, astronomy, physics, and in other areas. This is the first example of showcasing hStreams’ ability to enable an ML algorithm. hStreams enabled KNN achieves the best performance achievable by either Xeon R or Xeon PhiTM 1 by utilizing both platforms simultaneously and selectively.

Poster: pdf
Two-page extended abstract: pdf


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