Title: Benchmarking Data Analysis and Machine Learning Applications on the Intel KNL Many-Core Processor,
Abstract: Knights Landing (KNL) is the code name for the second-generation Intel Xeon
Phi product family. KNL has generated significant interest in the data analysis
and machine learning communities because its new many-core architecture targets
both of these workloads. The KNL many-core vector processor design enables it
to exploit much higher levels of parallelism. At the Lincoln Laboratory
Supercomputing Center (LLSC), the majority of users are running data analysis
applications such as MATLAB and Octave. More recently, machine learning
applications, such as the UC Berkeley Caffe deep learning framework, have
become increasingly important to LLSC users. Thus, the performance of these
applications on KNL systems is of high interest to LLSC users and the broader
data analysis and machine learning communities. Our data analysis benchmarks of
these application on the Intel KNL processor indicate that single-core
double-precision generalized matrix multiply (DGEMM) performance on KNL systems
has improved by ~3.5x compared to prior Intel Xeon technologies. Our data
analysis applications also achieved ~60% of the theoretical peak performance.
Also a performance comparison of a machine learning application, Caffe, between
the two different Intel CPUs, Xeon E5 v3 and Xeon Phi 7210, demonstrated a 2.7x
improvement on a KNL node.