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Register a new userReal-time free-running time scale with remote clocks on fiber-based frequency network
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In this paper, we propose a real-time free-running time scale based on four remote hydrogen masers. The clocks in the ensemble were scattered around Beijing, connected by urban fiber links using a novel frequency synchronization system. The remote clock ensemble prevents the time scale from potential problems caused by correlation among co-located clocks. Insofar as it is real-time, it fulfills the requirements for applications such as navigation, telecommunications and so on. The free-running time scale is updated every 1200 s, and a disturbance-resistant algorithm makes it robust to fiber link disturbances and clock malfunctions. The results of a continuous experiment over 224 days are reported. The stability of the time scale outperformed any clock in the ensemble for averaging times of more than approximately 10000 s.
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Dual-comb spectroscopy has emerged as an indispensable analytical technique in applications that require high resolution and broadband coverage within short acquisition times. Its experimental realization, however, remains hampered by intricate experimental setups with large power consumption. Here, we demonstrate an ultra-simple free-running dual-comb spectrometer realized in a single all-fiber cavity suitable for the most demanding Doppler-limited measurements. Our dual-comb laser utilizes just a few basic fiber components, allows to tailor the repetition rate difference, and requires only 350 mW of electrical power for sustained operation over a dozen of hours. As a demonstration, we measure low-pressure hydrogen cyanide within 1.7 THz bandwidth, and obtain better than 1% precision over a terahertz in 200 ms enabled by a drastically simplified all-computational phase correction algorithm. The combination of the unprecedented setup simplicity, comb tooth resolution and high spectroscopic precision paves the way for proliferation of frequency comb spectroscopy even outside the laboratory.
Time-Correlated Single Photon Counting (TCSPC) and time tagging of individual photon detections are powerful tools in many quantum optical experiments and other areas of applied physics. Using TCSPC, e.g., for the purpose of fluorescence lifetime measurements, is often limited in speed due to dead-time losses and pile-up. We show that this limitation can be lifted by reducing the dead-time of the timing electronics to the absolute minimum imposed by the speed of the detector signals while maintaining high temporal resolution. A complementing approach to speedy data acquisition is parallelization by means of simultaneous readout of many detector channels. This puts high demands on the data throughput of the TCSPC system, especially in time tagging of individual photon arrivals. Here, we present a new design approach, supporting up to 16 input channels, an extremely short dead-time of 650 ps, very high time tagging throughput, and a timing resolution of 80 ps. In order to facilitate remote synchronization of multiple such instruments with highest precision, the new TCSPC electronics provide an interface for White Rabbit fiber optic networks. Beside fundamental research in the field of astronomy, such remote synchronization tasks arise routinely in quantum communication networks with node to node distances on the order of tens of kilometers. In addition to showing design features and benchmark results of new TCSPC electronics, we present application results from spectrally resolved and high-speed fluorescence lifetime imaging in medical research. We furthermore show how pulse-pile-up occurring in the detector signals at high photon flux can be corrected for and how this data acquisition scheme performs in terms of accuracy and efficiency.
Over the last few years the GPGPU (General-Purpose computing on Graphics Processing Units) paradigm represented a remarkable development in the world of computing. Computing for High-Energy Physics is no exception: several works have demonstrated the effectiveness of the integration of GPU-based systems in high level trigger of different experiments. On the other hand the use of GPUs in the low level trigger systems, characterized by stringent real-time constraints, such as tight time budget and high throughput, poses several challenges. In this paper we focus on the low level trigger in the CERN NA62 experiment, investigating the use of real-time computing on GPUs in this synchronous system. Our approach aimed at harvesting the GPU computing power to build in real-time refined physics-related trigger primitives for the RICH detector, as the the knowledge of Cerenkov rings parameters allows to build stringent conditions for data selection at trigger level. Latencies of all components of the trigger chain have been analyzed, pointing out that networking is the most critical one. To keep the latency of data transfer task under control, we devised NaNet, an FPGA-based PCIe Network Interface Card (NIC) with GPUDirect capabilities. For the processing task, we developed specific multiple ring trigger algorithms to leverage the parallel architecture of GPUs and increase the processing throughput to keep up with the high event rate. Results obtained during the first months of 2016 NA62 run are presented and discussed.
The experiment data generated by the EAST device is getting larger and larger, and it is necessary to monitor the MDSplus data storage server on EAST. In order to facilitate the management of users on the MDSplus server, a real-time monitoring log analysis system is needed. The data processing framework adopted by this log analysis system is the Spark Streaming framework in Spark ecosphere, whose real-time streaming data is derived from MDSplus logs. The framework also makes use of key technologies such as log monitoring, aggregation and distribution with framework likes Flume and Kafka which makes it possible for MDSplus mass log data processing power. The system can process tens of millions of unprocessed MDSplus log information at a second level, then model the log information and display it on the web. This report introduces the design and implementation of the overall architecture of real time data access log analysis system based on spark. Experimental results show that the system is proved to be with steady and reliable performance and has an important application value to the management of fusion experiment data. The system has been designed and will be adopted in the next campaign and the system details will be given in the paper.
While the GPGPU paradigm is widely recognized as an effective approach to high performance computing, its adoption in low-latency, real-time systems is still in its early stages. Although GPUs typically show deterministic behaviour in terms of latency in executing computational kernels as soon as data is available in their internal memories, assessment of real-time features of a standard GPGPU system needs careful characterization of all subsystems along data stream path. The networking subsystem results in being the most critical one in terms of absolute value and fluctuations of its response latency. Our envisioned solution to this issue is NaNet, a FPGA-based PCIe Network Interface Card (NIC) design featuring a configurable and extensible set of network channels with direct access through GPUDirect to NVIDIA Fermi/Kepler GPU memories. NaNet design currently supports both standard - GbE (1000BASE-T) and 10GbE (10Base-R) - and custom - 34~Gbps APElink and 2.5~Gbps deterministic latency KM3link - channels, but its modularity allows for a straightforward inclusion of other link technologies. To avoid host OS intervention on data stream and remove a possible source of jitter, the design includes a network/transport layer offload module with cycle-accurate, upper-bound latency, supporting UDP, KM3link Time Division Multiplexing and APElink protocols. After NaNet architecture description and its latency/bandwidth characterization for all supported links, two real world use cases will be presented: the GPU-based low level trigger for the RICH detector in the NA62 experiment at CERN and the on-/off-shore data link for KM3 underwater neutrino telescope.
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