A dissertation submitted in fulfillment of the requirements
for the degree of Master of Physics
in the
School of Physics
November 1, 2017.The ATLAS detector at the Large Hadron Collider (LHC) at CERN is
undergoing upgrades to its instrumentation, as well as the hardware and
software that comprise its Trigger and Data Acquisition (TDAQ) system.
The increased energy will yield larger cross sections for interesting physics
processes, but will also lead to increased artifacts in on-line reconstruction
in the trigger, as well as increased trigger rates, beyond the current system’s
capabilities. To meet these demands it is likely that the massive parallelism
of General-Purpose Programming with Graphic Processing Units (GPGPU)
will be utilised. This dissertation addresses the problem of integrating GPGPU
into the existing Trigger and TDAQ platforms; detailing and analysing
GPGPU performance in the context of performing in a high-throughput,
on-line environment like ATLAS. Preliminary tests show low to moderate
speed-up with GPU relative to CPU, indicating that to achieve a more significant
performance increase it may be necessary to alter the current platform
beyond pairing suitable GPUs to CPUs in an optimum ratio. Possible
solutions are proposed and recommendations for future work are given.LG201
