National Energy Research Scientific Computing Center

28 NERSC ANNUAL REPORT 2015
ELK Stack
Elastic cluster
Archive syslog data to disk.
Possible custom methods
custom methods
(text or json)
Multiple Logstash
Collectd, power and
environmental data
sent to rabbitmq for
other consumers
Event and time series data
stored here. Master for full
Collectd, power and correlation
or data.
Kibana graphical
interfacefor data
exploration and display
Closed shards that are
archived and can be loaded
later by other elastic cluster
instances. This included Mac
and PC stacks.
The new centralized data
collection infrastructure
at NERSC.
logstash follower
for text files
generated by
other programs
rsyslog
collectd
Per system forwarding logstash
logstash follower for
text files generated by
other programs
json inputs sent
to logstash to
enter into Elastic
Power and environmental
data. Direct from sensors
or a collector.
text and json format
LDMS
Procmon
amqp inputs
Rabbitmq cluster
Other output methods can be added
such as mysql, mongoDB, etc.
This can be a subset of the data
collectedor the full data.
This data can also be shipped to other
centers either over ssl or not.
Redis DB for Lustre and other data
for immediate access
Redis DB for power and cooling
limited to under 5 minutes
Archive collectd and other time
series data to hdf5 format
entire facility. This process involves collecting information on hosts or sensors across the center.
This data is then passed into the collection environment via RabbitMQ—an open-source message
broker software—which sends and stores the data to the Elasticsearch framework. RabbitMQ is
capable of passing data to many targets, and we leveraged this feature to send the same data to Redis
(an open-source, in-memory data structure store) for real-time access to some data, and to Freeboard
(an open-source dashboard) to display real-time data without any storage ability. We anticipate using
this method to send data to multiple targets to address future collection issues as they arise.
All of the acquired sensor data is stored in a commonly accessible format—few changes need to be
made when collecting new data or analyzing the data—that is centrally accessible to most staff
without transferring the data from one storage method to another or re-collecting the data in a
different configuration. This new method is already providing potential data analysis opportunities
not previously possible.
Compact Cori: An Educational Tool for HPC
In 2015 NERSC developed an HPC educational tool designed to inspire students of all ages,
including middle and high-school levels, and demonstrate the value of HPC to the general public.
A team of NERSC staff and student interns (two undergraduates and one high school student)
created “Compact Cori,” a micro-HPC system based on Intel NUC (next unit of computing)
hardware and a modern software stack based on python, MPI4PY, REST APIs, WebGL, Meteor
and other modern web technologies.
The hardware design was inspired by the “Tiny Titan” micro-HPC developed at Oak Ridge National
Laboratory. The hardware and software stack were chosen to be accessible and to represent modern
programming techniques and paradigms with richer, more accessible educational value than
traditional HPC languages like C and FORTRAN. The software stack allows students to explore
several novel design points for HPC.