A23: Evaluation of Data-Intensive Applications on Intel
Knights Landing Cluster
SessionPoster Reception
Author
Event Type
ACM Student Research Competition
Poster
Reception
TimeTuesday, November 14th5:15pm -
7pm
LocationFour Seasons Ballroom
DescriptionAnalyzing and understanding large datasets on high
performance computing platforms is becoming more and
more important in various scientific domains. MapReduce
is the dominant programming model for processing these
datasets. Platforms for data processing are empowered by
many-core nodes with cutting-edge processing units.
Intel Knights Landing (KNL) is the new arrival in the
field. However, this new architecture has not been fully
evaluated for data-intensive applications. In this
poster, we present the assess of KNL on the performance
of three key data-intensive applications based on a
high-performance MapReduce programming framework on the
latest KNL-cluster, Stampede2. We focus on the impact of
different KNL memory models, we compare Stampede2 with
other clusters such as Tianhe-2 and Mira, and we measure
the scalability of large datasets. We observe how
KNL-based clusters are a promising architecture for
data-intensive applications. We also identify key
aspects to enable more efficient usage of KNL-based
clusters.
Author
