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.
|Original language||English (US)|
|Journal||Journal of Physics: Conference Series|
|State||Published - Nov 23 2017|
|Event||22nd International Conference on Computing in High Energy and Nuclear Physics, CHEP 2016 - San Francisco, United States|
Duration: Oct 10 2016 → Oct 14 2016
All Science Journal Classification (ASJC) codes
- Physics and Astronomy(all)