Speaker
Dr
Sebastien Binet
(LBNL)
Description
Computers are no longer getting faster: instead, they are growing more and more
CPUs, each of which is no faster than the previous generation.
This increase in the number of cores evidently calls for more parallelism in
HENP software.
If end-users' stand-alone analysis applications are relatively easy to modify,
LHC experiments frameworks, being mostly written with a single 'thread' of
execution in mind and consequent code bases, are on the other hand more
challenging to parallelize. Widespread and inconsiderate changes so close to
data taking are out of the equation: we need clear strategies and guidelines to
reap the benefits out of the multicore/manycore era while minimizing the code
changes.
Exploiting parallelism is usually achieved via a) multithreading or
b) multiple processes (or a combination of both) but each option has its own
set of tradeoffs in terms of code changes (and code complication) and possible
speed-ups.
This paper describes the different strategies the Offline and Online
communities from the ATLAS collaboration investigated, which have been
implemented and integrated into the Athena framework, and finally how well
they perform in terms of speed-ups, memory usage, I/O and code development.
We present the work integrated in AthenaMT to harness the High Level Trigger
farms' computing power via multithreading, the needed improvements and
modifications applied to Athena/Gaudi in order to raise its thread awareness
and the impact on common design patterns to preserve thread safety across
release cycles.
Threads sharing the same address space, AthenaMT is the most promising option
in terms of speed-ups and memory usage efficiency at the cost of an increased
development load for the code writer needing to worry about locks, data races
and the like.
AthenaMP leverages the 'fork()' system call and the 'Copy On Write' mechanism
through the 'multiprocessing' python module, to free oneself from the usual
concurrency problems that stem from sharing state, while still retaining part
of the memory usage efficiency at the cost of a diminished flexibility
(compared to threading.)
We'll detail the AthenaMP implementation, highlighting the minimized code
changes, how they seamlessly blend into the Athena framework and their
interplay with I/O and OS resources.
Authors
Dr
Frank Winklmeier
(CERN)
Dr
Sebastien Binet
(LBNL)
Co-authors
Dr
Hans von der Schmitt
(Max Planck Institut für Physik)
Dr
Paolo Calafiura
(LBNL)
Dr
Scott Snyder
(BNL)
Dr
Werner Wiedenmann
(University of Wisconsin)
