Abstract
We report on the progress of our studies towards a Kalman filter track reconstruction algorithm with optimal performance on manycore architectures. The combinatorial structure of these algorithms is not immediately compatible with an efficient SIMD (or SIMT) implementation; the challenge for us is to recast the existing software so it can readily generate hundreds of shared-memory threads that exploit the underlying instruction set of modern processors. We show how the data and associated tasks can be organized in a way that is conducive to both multithreading and vectorization. We demonstrate very good performance on Intel Xeon and Xeon Phi architectures, as well as promising first results on Nvidia GPUs.
Highlights
The Large Hadron Collider (LHC) at CERN is the highest energy collider ever constructed
By measuring the energy and momentum of particles escaping the collision, we can infer the existence of massive particles that were created and measure those massive particles’ properties
The speed of online reconstruction has a direct impact on how much data can be stored from the 40 MHz collisions rate, while the speed on the offline reconstruction limits how much data can be processed for physics analyses
Summary
The Large Hadron Collider (LHC) at CERN is the highest energy collider ever constructed It consists of two counter-circulating proton beams made to collide in four locations around a 27 kilometer ring straddling the border between Switzerland and France. By several means it is the largest manmade scientific device on the planet. By measuring the energy and momentum of particles escaping the collision, we can infer the existence of massive particles that were created and measure those massive particles’ properties. This process is known as event reconstruction and consists of integrating information from different detector components.
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