How stable are transport model results to changes of resonance parameters? A UrQMD model study

The Ultrarelativistic Quantum Molecular Dynamics [UrQMD] model is widely used to simulate heavy ion collisions in broad energy ranges. It consists of various components to implement the different physical processes underlying the transport approach. A major building block are the shared tables of constants, implementing the baryon masses and widths. Unfortunately, many of these input parameters are not well known experimentally. In view of the upcoming physics program at FAIR, it is therefore of fundamental interest to explore the stability of the model results when these parameters are varied. We perform a systematic variation of particle masses and widths within the limits proposed by the particle data group (or up to 10%). We find that the model results do only weakly depend on the variation of these input parameters. Thus, we conclude that the present implementation is stable with respect to the modification of not yet well specified particle parameters

Lattice QCD based on OpenCL

We present an OpenCL-based Lattice QCD application using a heatbath algorithm for the pure gauge case and Wilson fermions in the twisted mass formulation. The implementation is platform independent and can be used on AMD or NVIDIA GPUs, as well as on classical CPUs. On the AMD Radeon HD 5870 our double precision dslash implementation performs at 60 GFLOPS over a wide range of lattice sizes. The hybrid Monte-Carlo presented reaches a speedup of four over the reference code running on a server CPU.Comment: 19 pages, 11 figure

Fast TPC Online Tracking on GPUs and Asynchronous Data Processing in the ALICE HLT to facilitate Online Calibration

ALICE (A Large Heavy Ion Experiment) is one of the four major experiments at the Large Hadron Collider (LHC) at CERN, which is today the most powerful particle accelerator worldwide. The High Level Trigger (HLT) is an online compute farm of about 200 nodes, which reconstructs events measured by the ALICE detector in real-time. The HLT uses a custom online data-transport framework to distribute data and workload among the compute nodes. ALICE employs several calibration-sensitive subdetectors, e.g. the TPC (Time Projection Chamber). For a precise reconstruction, the HLT has to perform the calibration online. Online-calibration can make certain Offline calibration steps obsolete and can thus speed up Offline analysis. Looking forward to ALICE Run III starting in 2020, online calibration becomes a necessity. The main detector used for track reconstruction is the TPC. Reconstructing the trajectories in the TPC is the most compute-intense step during event reconstruction. Therefore, a fast tracking implementation is of great importance. Reconstructed TPC tracks build the basis for the calibration making a fast online-tracking mandatory. We present several components developed for the ALICE High Level Trigger to perform fast event reconstruction and to provide features required for online calibration. As first topic, we present our TPC tracker, which employs GPUs to speed up the processing, and which bases on a Cellular Automaton and on the Kalman filter. Our TPC tracking algorithm has been successfully used in 2011 and 2012 in the lead-lead and the proton-lead runs. We have improved it to leverage features of newer GPUs and we have ported it to support OpenCL, CUDA, and CPUs with a single common source code. This makes us vendor independent. As second topic, we present framework extensions required for online calibration. ...Comment: 8 pages, 6 figures, contribution to CHEP 2015 conferenc

BioEM: GPU-accelerated computing of Bayesian inference of electron microscopy images

In cryo-electron microscopy (EM), molecular structures are determined from large numbers of projection images of individual particles. To harness the full power of this single-molecule information, we use the Bayesian inference of EM (BioEM) formalism. By ranking structural models using posterior probabilities calculated for individual images, BioEM in principle addresses the challenge of working with highly dynamic or heterogeneous systems not easily handled in traditional EM reconstruction. However, the calculation of these posteriors for large numbers of particles and models is computationally demanding. Here we present highly parallelized, GPU-accelerated computer software that performs this task efficiently. Our flexible formulation employs CUDA, OpenMP, and MPI parallelization combined with both CPU and GPU computing. The resulting BioEM software scales nearly ideally both on pure CPU and on CPU+GPU architectures, thus enabling Bayesian analysis of tens of thousands of images in a reasonable time. The general mathematical framework and robust algorithms are not limited to cryo-electron microscopy but can be generalized for electron tomography and other imaging experiments

Online Calibration of the TPC Drift Time in the ALICE High Level Trigger

ALICE (A Large Ion Collider Experiment) is one of four major experiments at the Large Hadron Collider (LHC) at CERN. The High Level Trigger (HLT) is a compute cluster, which reconstructs collisions as recorded by the ALICE detector in real-time. It employs a custom online data-transport framework to distribute data and workload among the compute nodes. ALICE employs subdetectors sensitive to environmental conditions such as pressure and temperature, e.g. the Time Projection Chamber (TPC). A precise reconstruction of particle trajectories requires the calibration of these detectors. Performing the calibration in real time in the HLT improves the online reconstructions and renders certain offline calibration steps obsolete speeding up offline physics analysis. For LHC Run 3, starting in 2020 when data reduction will rely on reconstructed data, online calibration becomes a necessity. Reconstructed particle trajectories build the basis for the calibration making a fast online-tracking mandatory. The main detectors used for this purpose are the TPC and ITS (Inner Tracking System). Reconstructing the trajectories in the TPC is the most compute-intense step. We present several improvements to the ALICE High Level Trigger developed to facilitate online calibration. The main new development for online calibration is a wrapper that can run ALICE offline analysis and calibration tasks inside the HLT. On top of that, we have added asynchronous processing capabilities to support long-running calibration tasks in the HLT framework, which runs event-synchronously otherwise. In order to improve the resiliency, an isolated process performs the asynchronous operations such that even a fatal error does not disturb data taking. We have complemented the original loop-free HLT chain with ZeroMQ data-transfer components. [...]Comment: 8 pages, 10 figures, proceedings to 2016 IEEE-NPSS Real Time Conferenc

Autonomic Management of Large Clusters and Their Integration into the Grid

We present a framework for the co-ordinated, autonomic management of multiple clusters in a compute center and their integration into a Grid environment. Site autonomy and the automation of administrative tasks are prime aspects in this framework. The system behavior is continuously monitored in a steering cycle and appropriate actions are taken to resolve any problems. All presented components have been implemented in the course of the EU project DataGrid: The Lemon monitoring components, the FT fault-tolerance mechanism, the quattor system for software installation and configuration, the RMS job and resource management system, and the Gridification scheme that integrates clusters into the Grid

Benchmarks and implementation of the ALICE high level trigger

The ALICE high level trigger combines and processes the full information from all major detectors in a large computer cluster. Data rate reduction is achieved by reducing the event rate by selecting interesting events (software trigger) and by reducing the event size by selecting sub-events and by advanced data compression. Reconstruction chains for the barrel detectors and the forward muon spectrometer have been benchmarked. The HLT receives a replica of the raw data via the standard ALICE DDL link into a custom PCI receiver card (HLT-RORC). These boards also provide a FPGA co-processor for data-intensive tasks of pattern recognition. Some of the pattern recognition algorithms (cluster finder, Hough transformation) have been re-designed in VHDL to be executed in the Virtex-4 FPGA on the HLT-RORC. HLT prototypes were operated during the beam tests of the TPC and TRD detectors. The input and output interfaces to DAQ and the data flow inside of HLT were successfully tested. A full-scale prototype of the dimuon-HLT achieved the expected data flow performance. This system was finally embedded in a GRID-like system of several distributed clusters demonstrating the scalability and fault-tolerance of the HL