Thomas Hauth

Speeding up HEP experiment software with a library of fast and auto-vectorisable mathematical functions

During the first years of data taking at the Large Hadron Collider (LHC), the simulation and reconstruction programs of the experiments proved to be extremely resource consuming. In particular, for complex event simulation and reconstruction applications, the impact of evaluating elementary functions on the runtime is sizeable (up to one fourth of the total), with an obvious effect on the power consumption of the hardware dedicated to their execution. This situation clearly needs improvement, especially considering the even more demanding data taking scenarios after the first LHC long shut down. A possible solution to this issue is the VDT (VectorisD maTh) mathematical library. VDT provides the most common mathematical functions used in HEP in an open source product. The function implementations are fast, can be inlined, provide an approximate accuracy and are usable in vectorised loops. Their implementation is portable across platforms: x86 and ARM processors, Xeon Phi coprocessors and GPGPUs. In this contribution, we describe the features of the VDT mathematical library, showing significant speedups with respect to the LibM library and comparable accuracies. Moreover, taking as examples simulation and reconstruction workflows in production by the LHC experiments, we show the benefits of the usage of VDT in terms of runtime reduction and stability of physics output.

Development and Evaluation of Vectorised and Multi-Core Event Reconstruction Algorithms within the CMS Software Framework

The processing of data acquired by the CMS detector at LHC is carried out with an object-oriented C++ software framework: CMSSW. With the increasing luminosity delivered by the LHC, the treatment of recorded data requires extraordinary large computing resources, also in terms of CPU usage. A possible solution to cope with this task is the exploitation of the features offered by the latest microprocessor architectures. Modern CPUs present several vector units, the capacity of which is growing steadily with the introduction of new processor generations. Moreover, an increasing number of cores per die is offered by the main vendors, even on consumer hardware. Most recent C++ compilers provide facilities to take advantage of such innovations, either by explicit statements in the programs sources or automatically adapting the generated machine instructions to the available hardware, without the need of modifying the existing code base. Programming techniques to implement reconstruction algorithms and optimised data structures are presented, that aim to scalable vectorization and parallelization of the calculations. One of their features is the usage of new language features of the C++11 standard. Portions of the CMSSW framework are illustrated which have been found to be especially profitable for the application of vectorization and multi-threading techniques. Specific utility components have been developed to help vectorization and parallelization. They can easily become part of a larger common library. To conclude, careful measurements are described, which show the execution speedups achieved via vectorised and multi-threaded code in the context of CMSSW.

Parallel track reconstruction in CMS using the cellular automaton approach

The Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC) is a general-purpose particle detector and comprises the largest silicon-based tracking system built to date with 75 million individual readout channels. The precise reconstruction of particle tracks from this tremendous amount of input channels is a compute-intensive task. The foreseen LHC beam parameters for the next data taking period, starting in 2015, will result in an increase in the number of simultaneous proton-proton interactions and hence the number of particle tracks per event. Due to the stagnating clock frequencies of individual CPU cores, new approaches to particle track reconstruction need to be evaluated in order to cope with this computational challenge. Track finding methods that are based on cellular automata (CA) offer a fast and parallelizable alternative to the well-established Kalman filter-based algorithms. We present a new cellular automaton based track reconstruction, which copes with the complex detector geometry of CMS. We detail the specific design choices made to allow for a high-performance computation on GPU and CPU devices using the OpenCL framework. We conclude by evaluating the physics performance, as well as the computational properties of our implementation on various hardware platforms and show that a significant speedup can be attained by using GPU architectures while achieving a reasonable physics performance at the same time.

Dynamic Extension of a Virtualized Cluster by using Cloud Resources

The specific requirements concerning the software environment within the HEP community constrain the choice of resource providers for the outsourcing of computing infrastructure. The use of virtualization in HPC clusters and in the context of cloud resources is therefore a subject of recent developments in scientific computing. The dynamic virtualization of worker nodes in common batch systems provided by ViBatch serves each user with a dynamically virtualized subset of worker nodes on a local cluster. Now it can be transparently extended by the use of common open source cloud interfaces like OpenNebula or Eucalyptus, launching a subset of the virtual worker nodes within the cloud. This paper demonstrates how a dynamically virtualized computing cluster is combined with cloud resources by attaching remotely started virtual worker nodes to the local batch system.