Computing infrastructure at the CERN neutrino platform prototypes experiments

We describe the challenges arising from tracking dark matter particles in state of the art cosmological simulations. We are in the process of running the Indra suite of simulations, with an aggregate count of more than 35 trillion particles and 1.1PB of total raw data volume. However, it is not enough just to store the particle positions and velocities in an efficient manner -- analyses also need to be able to track individual particles efficiently through the temporal history of the simulation. The required inverted indices can easily have raw sizes comparable to the original simulation.We explore various strategies on how to create an efficient index for such data, using additional insight from the physical properties of the particle motions for a greatly compressed data representation. The basic particle data are stored in a relational database in course-grained containers corresponding to leaves of a fixed depth oct-tree labeled by their Peano-Hilbert index. Within each container the individual objects are sorted by their Lagrangian identifier. Thus each particle has a multi-level address: the PH key of the container and the index of the particle within the sorted array (the slot).Given the nature of the cosmological simulations and choice of the PH-box sizes, in consecutive snapshots particles can only cross into spatially adjacent boxes. Also, the slot number of a particle in adjacent snapshots is adjusted up or down by typically a small number. As a result, a special version of delta encoding over the multi-tier address already results in a dramatic reduction of data that needs to be stored. We follow next with an efficient bit-compression, adapting to the statistical properties of the two-part addresses, achieving a final compression ratio better than a factor of 9. The final size of the full inverted index is projected to be 22.5 TB for a petabyte ensemble of simulations.

In the last decade, the sophistication of three-dimensional (3D) seismic data has been applied increasingly to the more precise definition of geological structures containing oil and gas accumulations. In favourable circumstances, it is even possible to define the limits of the hydrocarbons themselves and, in rare cases, show the retreat of hydrocarbon-water contacts in response to sustained oil and gas production. In the early days, 3D seismic data were interpreted in a manner comparable to the interpretation of a grid of two-dimensional (2D) profiles. Equispaced in-lines and cross-lines were abstracted from the total data volume and, with the additional benefit of arbitrary lines and time-slices, interpreters were able to construct time contour maps of selected horizons with increased confidence. However, a significant percentage of the total volume of acquired data was by-passed in this process, and it was not until the advent of interactive trace interpretation systems (the so called work stations) that the interpretation of the full data volume became feasible. Feasibility turned into reality with the development of new and powerful automatic tracking (horizon picking) programs. These enable the interpreter to incorporate data from every common midpoint (CM) in the data volume. In some more advanced programs, selected ‘seed’ lines, which are initially interpreted by hand, are fed to the automatic tracking programs which, in turn, extrapolate the horizon pick until every CMP in the survey has been covered. For any given reflection horizon it is thus possible to generate files of two-way time, amplitude, instantaneous phase, etc. in a framework of x,y coordinates. These files permit the calculation of additional horizon attributes, especially dip and azimuth (Shell Internationale Petroleum Maatschappij BV 1988. Horizon processing techniques for recognition of structural geology on 3D seismic. Research Disclosure 29473). This paper is concerned with the determination and display of the latter two attributes. It will be shown that, with carefully chosen colour display parameters, subtle laterally continuous features that would have been overlooked during traditional interpretation may be interpreted with relative ease and confidence as having geological significance. The techniques employed have much in common with those used in photogeology.

The gluon contribution to the nucleon spin and the COMPASS experiment at CERN

COMPASS (COmmon Muon Proton Apparatus for Structure and Spectroscopy) is a high-energy physies experiment at the Super Proton Synchrotron (SPS) at CERN in Geneva, Switzerland. The purpose of this experiment is to study the hadron structure and hadron spectrum using high intensity muon and hadron beams. A set of programs, which transfers the data taken by the on-line experimental systems to the central data storage CASTOR, are required. We use the software package called Central Data Recording (CDR) system.

COMPASS at CERN uses hadron and muon beams with up to 200 GeV/c momentum, produced from the SPS proton beam, for investigations in hadron structure and spectroscopy. From a pilot run with a 190 GeV/c pion beam on a lead target, various results are presented. In the region of low momentum transfer, interference of photon‐exchange and strong production of the a1(1260) and a2(1320) resonances is observed, revealing the different nature of the two interactions.

We report the development of an optical encoder and its readout system for a cryogenically-cooled continuously rotating half-wave plate (HWP) polarization modulator unit (PMU) in the LiteBIRD low-frequency telescope. LiteBIRD is a cosmic microwave background polarization satellite mission to probe B-mode polarization, which originates from primordial gravitational waves, observing from the second Lagrange point (L2). LiteBIRD employs a continuously-rotating HWP to mitigate systematic effects. The knowledge of the position angle of the HWP is in a one-to-one relationship to the incident polarization angle. The required reconstruction accuracy is about 1 arcmin and the targeted rotational frequency stability is 1 mHz. A unique development constraint comes from a telemetry bandwidth limitation between the Earth and L2, and thus we implement a digital process to reduce the data volume assuming a future implementation of on-board processing of the encoder data before the downlink. The demonstrations were done experimentally using a breadboard model of the PMU: a readout system using FPGA (Spartan-6) and a rotational mechanism using a superconducting magnetic bearing and AC motor. We acquired the encoder data from the rotational mechanism operating under two conditions: liquid nitrogen at room pressure and below 10 K in a cryostat. We demonstrated the reconstruction of the position angle accuracy < 0.5 arcmin and the corresponding data volume of 0.12 GB/day, which is at least an order of magnitude smaller than the total data volume per day. We further discuss the sources of the position angle uncertainty and its implications to the observations.

Time series prediction problems have a wide range of real-world applications and lots of study have been devoted on them. Most of the previous results need to determine the order for the model according to some information criterion such as Bayesian information criterion (BIC), Akaike information criterion (AIC) and so on. However, for online data stream scenario, keeping the order of the model fixed may not exploit the historical data adequately. Thus it is reasonable to consider a model with adaptive order. In this paper, we study the ARX (∞) approximation algorithm for ARX (autoregressive with exogenous inputs) model and allow the order of ARX model to increase with total data volume. Under the assumption that the noise terms are martingale difference sequence, we analyze the one-step prediction error and show that the growth rate of regret loss can be bounded by $O(\lceil\log T\rceil^{4})$. In addition, we demonstrate the performance of our approximation algorithm by comparing with ARX (p).

Though a number of techniques for test data compression have been proposed until now, most of them have not taken into consideration the volume of dictionary data that specify decompression rules. This paper presents a test modification and compression technique for reducing total test volume including dictionary data. As for most test compression techniques such as Huffman coding based methods, if we determine the block size large, it results in small test volume but large dictionary volume. Therefore, it has been thought that large block sizes are ineffective for total test data volume reduction. However, if dictionary volume can be greatly reduced, large block sizes can be more effective. This paper describes a compression method for reducing total test volume, especially dictionary volume, by using a technique that produces a test set for which the number of code words is small in the case of a large block size. The effectiveness of the proposed method is shown through test data compression experiments for ISCAS '89 benchmark circuits.

A combined approach for implementing system level test compression and core test scheduling to reduce SoC test costs is proposed in this paper. A broadcast scan based test compression algorithm for parallel testing of cores with multiple scan chains is used to reduce the test data of the SoC. Unlike other test compression schemes, the proposed algorithm doesn't require specialized test generation or fault simulation and is applicable with intellectual property (IP) cores. The core testing schedule with compression enabled is decided using a generalized strip packing algorithm. The hardware architecture to implement the proposed scheme is very simple. By using the combined approach, the total test data volume and test application time of the SoC is reduced to a level comparable with the test data volume and test application time of the largest core in the SoC.

The Cherenkov Telescope Array (CTA) - an array of several tens of Cherenkov telescopes - is the next generation of ground-based instrument in the field of very high energy gamma-ray astronomy. The CTA observatory is expected to produce a main data stream for permanent storage of the order of 1-to-5 GB/s for about 1000 hours of observation per year, thus producing a total data volume of the order of several PB per year. The CPU time needed to calibrate and process one hour of data taking will be of the order of some thousands CPU hours with current technology. The high data rate of CTA, together with the large computing power requirements for Monte Carlo (MC) simulations, need dedicated computing resources. Massive MC simulations are needed to study the physics of cosmic-ray atmospheric showers as well as telescope response and performance for different detectors and layout configurations. Given these large storage and computing requirements, the Grid approach is well suited, and a vast number of MC simulations are already running on the European Grid Infrastructure (EGI). In order to optimize resource usage and to handle all production and future analysis activities in a coherent way, a high-level framework with advanced functionalities is desirable. For this purpose we have preliminarly evaluated the DIRAC framework for distributed computing and tested it for the CTA workload and data management systems. In this paper we present a possible implementation of a Distributed Computing Infrastructure (DCI) Computing Model for CTA as well as the benchmark test results of DIRAC.

Study on ammonia nitrogen and phosphorus removal using sequencing batch reactor

Traditionally, the ATLAS experiment at Large Hadron Collider (LHC) has utilized distributed resources as provided by the Worldwide LHC Computing Grid (WLCG) to support data distribution, data analysis and simulations. For example, the ATLAS experiment uses a geographically distributed grid of approximately 200,000 cores continuously (250 000 cores at peak), (over 1,000 million core-hours per year) to process, simulate, and analyze its data (todays total data volume of ATLAS is more than 300 PB). After the early success in discovering a new particle consistent with the long-awaited Higgs boson, ATLAS is continuing the precision measurements necessary for further discoveries. Planned high-luminosity LHC upgrade and related ATLAS detector upgrades, that are necessary for physics searches beyond Standard Model, pose serious challenge for ATLAS computing. Data volumes are expected to increase at higher energy and luminosity, causing the storage and computing needs to grow at a much higher pace than the flat budget technology evolution (see Fig. 1). The need for simulation and analysis will overwhelm the expected capacity of WLCG computing facilities unless the range and precision of physics studies will be curtailed.

This data set includes 11 periods of spatial distribution data of Qinghai lake water body every three years from 1987 to 2017, with a total data volume of 2.25mb. The data is stored in SHP vector data format, and the projection system is WGS-84. It can be used as the basic data for the study of the temporal and spatial changes of water body and the assessment of lake water change, and also can be used as an important basis for the study of the relationship between water resources change, climate change and lake response.

An edge-based netlist extractor using a relocatable netlist representation scheme is described. Retaining an existing layout hierarchy, it extracts the devices formed as a result of cell overlaps. Using the implemented overlap-handling algorithm, it is possible to access cell pins not available at the boundary of the cell. The extractor handles all angled geometrics. Total volume of data handled by the extractor is reduced by extracting a cell netlist only only and modifying this netlist according to the nature of the cell instance overlap. The extractor is implemented in C under a Unix environment. >

Now-a-days TCP is a famous protocol used in Internet but the main problem is packet losses due to congestion. In this thesis we proposed a new Loss Tolerant TCP (LT-TCP), an enhancement of TCP which makes it robust and applicable for extreme wireless environment. In the proposed LT-TCP two additional term, data and data header compression are added in existing LT-TCP. We reduce the total volume of data and packet size in our adaptive method which is able to minimize the congestion and increase the reliability of wireless communication. The ECN respond about random data packet loss and disruption process. The overhead of Forward Error Control FEC is imposed just-in-time process and target to maximize the performance even if the path characteristics are uncertain. This proposal show that it will perform better over regular TCP and it is possible to reduce packet losses up to 40-50%.