Interest in many-core architectures applied to real time selections is growing in High Energy Physics (HEP) experiments. In this paper we describe performance measurements of many-core devices when applied to a typical HEP online task: the selection
of events based on the trajectories of charged particles. We use as benchmark a scaled-up version of the algorithm used at CDF experiment at Tevatron for online track reconstruction - the SVT algorithm - as a realistic test-case for low-latency trigger systems using new computing architectures for LHC experiment. We examine the complexity/performance trade-off in porting existing serial algorithms to many-core devices. We measure performance of different architectures (Intel Xeon Phi and AMD GPUs, in addition to NVidia GPUs) and different software environments (OpenCL, in addition to NVidia CUDA). Measurements of both data processing and data transfer latency are shown, considering different I/O strategies to/from the many-core devices.
Interest in parallel architectures applied to real time selections is growing in High Energy Physics (HEP) experiments. In this paper we describe performance measurements of Graphic Processing Units (GPUs) and Intel Many Integrated Core architecture
(MIC) when applied to a typical HEP online task: the selection of events based on the trajectories of charged particles. We use as benchmark a scaled-up version of the algorithm used at CDF experiment at Tevatron for online track reconstruction - the SVT algorithm - as a realistic test-case for low-latency trigger systems using new computing architectures for LHC experiment. We examine the complexity/performance trade-off in porting existing serial algorithms to many-core devices. Measurements of both data processing and data transfer latency are shown, considering different I/O strategies to/from the parallel devices.
We present the development and validation of a new multivariate $b$ jet identification algorithm ($b$ tagger) used at the CDF experiment at the Fermilab Tevatron. At collider experiments, $b$ taggers allow one to distinguish particle jets containing
$B$ hadrons from other jets. Employing feed-forward neural network architectures, this tagger is unique in its emphasis on using information from individual tracks. This tagger not only contains the usual advantages of a multivariate technique such as maximal use of information in a jet and tunable purity/efficiency operating points, but is also capable of evaluating jets with only a single track. To demonstrate the effectiveness of the tagger, we employ a novel method wherein we calculate the false tag rate and tag efficiency as a function of the placement of a lower threshold on a jets neural network output value in $Z+1$ jet and $tbar{t}$ candidate samples, rich in light flavor and $b$ jets, respectively.
Diboson production ($WW+WZ+ZZ$) has been observed at the Tevatron in hadronic decay modes dominated by the $WW$ process. This paper describes the measurement of the cross section of $WZ$ and $ZZ$ events in final states with large $mett$ and using $b$
-jet identification as a tool to suppress $WW$ contributions. Due to the limited energy resolution, we cannot distinguish between partially hadronic decays of $WZ$ and $ZZ$, and we measure the sum of these processes. The number of signal events is extracted using a simultaneous fit to the invariant mass distribution of the two jets for events with two $b$-jet candidates and events without two $b$-jet candidates. We measure a cross section $sigma(pbar{p}to WZ,ZZ) = 5.8^{+3.6}_{-3.0}$ pb, in agreement with the standard model.
