Anisotropic distributions in a multi-phase transport model

With A Multi-Phase Transport (AMPT) model we investigate the relation between the magnitude, fluctuations and correlations of the initial state spatial anisotropy $\varepsilon_{n}$ and the final state anisotropic flow coefficients $v_{n}$ in Au+Au collisions at $\sqrt{s_{_{\rm NN}}}=$ 200 GeV. It is found that the relative eccentricity fluctuations in AMPT account for the observed elliptic flow fluctuations, in agreement with measurements of the STAR collaboration. In addition, the studies based on 2- and multi-particle correlations and event-by-event distributions of the anisotropies suggest that the Elliptic-Power function is a promising candidate of the underlying probability density function of the event-by-event distributions of $\varepsilon_{n}$ as well as $v_{n}$. Furthermore, the correlations between different order symmetry planes and harmonics in the initial coordinate space and final state momentum space are presented. Non-zero values of these correlations have been observed. The comparison between our calculations and data will, in the future, shed new insight into the nature of the fluctuations of the Quark-Gluon Plasma produced in heavy ion collisions.Comment: 10 pages, 8 figures, accepted by PR

Stable knots in the trapped Bose-Einstein condensates

The knot of spin texture is studied within the two-component Bose-Einstein condensates which are described by the nonlinear Gross-Pitaevskii equations. We start from the non-interacting equations including an axisymmetric harmonic trap to obtain an exact solution, which exhibits a non-trivial topological structure. The spin-texture is a knot with an integral Hopf invariant. The stability of the knot is verified by numerically evolving the nonlinear Gross-Pitaevskii equations along imaginary time.Comment: 4 pages, 5 figure

Performance Evaluation and Modeling of HPC I/O on Non-Volatile Memory

HPC applications pose high demands on I/O performance and storage capability. The emerging non-volatile memory (NVM) techniques offer low-latency, high bandwidth, and persistence for HPC applications. However, the existing I/O stack are designed and optimized based on an assumption of disk-based storage. To effectively use NVM, we must re-examine the existing high performance computing (HPC) I/O sub-system to properly integrate NVM into it. Using NVM as a fast storage, the previous assumption on the inferior performance of storage (e.g., hard drive) is not valid any more. The performance problem caused by slow storage may be mitigated; the existing mechanisms to narrow the performance gap between storage and CPU may be unnecessary and result in large overhead. Thus fully understanding the impact of introducing NVM into the HPC software stack demands a thorough performance study. In this paper, we analyze and model the performance of I/O intensive HPC applications with NVM as a block device. We study the performance from three perspectives: (1) the impact of NVM on the performance of traditional page cache; (2) a performance comparison between MPI individual I/O and POSIX I/O; and (3) the impact of NVM on the performance of collective I/O. We reveal the diminishing effects of page cache, minor performance difference between MPI individual I/O and POSIX I/O, and performance disadvantage of collective I/O on NVM due to unnecessary data shuffling. We also model the performance of MPI collective I/O and study the complex interaction between data shuffling, storage performance, and I/O access patterns.Comment: 10 page

Vehicular Fog Computing Enabled Real-time Collision Warning via Trajectory Calibration

Vehicular fog computing (VFC) has been envisioned as a promising paradigm for enabling a variety of emerging intelligent transportation systems (ITS). However, due to inevitable as well as non-negligible issues in wireless communication, including transmission latency and packet loss, it is still challenging in implementing safety-critical applications, such as real-time collision warning in vehicular networks. In this paper, we present a vehicular fog computing architecture, aiming at supporting effective and real-time collision warning by offloading computation and communication overheads to distributed fog nodes. With the system architecture, we further propose a trajectory calibration based collision warning (TCCW) algorithm along with tailored communication protocols. Specifically, an application-layer vehicular-to-infrastructure (V2I) communication delay is fitted by the Stable distribution with real-world field testing data. Then, a packet loss detection mechanism is designed. Finally, TCCW calibrates real-time vehicle trajectories based on received vehicle status including GPS coordinates, velocity, acceleration, heading direction, as well as the estimation of communication delay and the detection of packet loss. For performance evaluation, we build the simulation model and implement conventional solutions including cloud-based warning and fog-based warning without calibration for comparison. Real-vehicle trajectories are extracted as the input, and the simulation results demonstrate that the effectiveness of TCCW in terms of the highest precision and recall in a wide range of scenarios