Simulations of Collisional Effects in an Inner-Shell Solid-Density Mg X-Ray Laser

Inner-shell K$\alpha$ x-ray lasers have been created by pumping gaseous, solid, and liquid targets with the intense x-ray output of free-electron-lasers (FELs). For gaseous targets lasing relies on the creation of K-shell core-holes on a time-scale short compared with filling via Auger decay. In the case of solid and liquid density systems, collisional effects will also be important, affecting not only populations, but also line-widths, both of which impact the degree of overall gain, and its duration. However, to date such collisional effects have not been extensively studied. We present here initial simulations using the CCFLY code of inner-shell lasing in solid density Mg, where we self-consistently treat the effects of the incoming FEL radiation and the atomic kinetics of the Mg system, including radiative, Auger, and collisional effects. We find that the combination of collisional population of the lower states of the lasing transitions and broadening of the lines precludes lasing on all but the K$\alpha$ of the initially cold system. Even assuming instantaneous turning on of the FEL pump, we find the duration of the gain in the solid system to be sub-femtosecond.Comment: This paper has been submitted to Philosophical Transactions

Performance-aware task scheduling in multi-core computers

Multi-core systems become more and more popular as they can satisfy the increasing computation capacity requirements of complex applications. Task scheduling strategy plays a key role in this vision and ensures that the task processing is both Quality-of-Service (QoS, in this thesis, refers to deadline) satisfied and energy-efficient. In this thesis, we develop task scheduling strategies for multi-core computing systems. We start by looking at two objectives of a multi-core computing system. The first objective aims at ensuring all tasks can satisfy their time constraints (i.e. deadline), while the second strives to minimize the overall energy consumption of the platform. We develop three power-aware scheduling strategies in virtualized systems managed by Xen. Comparing with the original scheduling strategy in Xen, these scheduling algorithms are able to reduce energy consumption without reducing the performance for the jobs. Then, we find that modelling the makespan of a task (before execution) accurately is very important for making scheduling decisions. Our studies show that the discrepancy between the assumption of (commonly used) sequential execution and the reality of time sharing execution may lead to inaccurate calculation of the task makespan. Thus, we investigate the impact of the time sharing execution on the task makespan, and propose the method to model and determine the makespan with the time-sharing execution. Thereafter, we extend our work to a more complex scenario: scheduling DAG applications for time sharing systems. Based on our time-sharing makespan model, we further develop the scheduling strategies for DAG jobs in time-sharing execution, which achieves more effective at task execution. Finally, as the resource interference also makes a big difference to the performance of co-running tasks in multi-core computers (which may further influence the scheduling decision making), we investigate the influential factors that impact on the performance when the tasks ii are co-running on a multicore computer and propose the machine learning-based prediction frameworks to predict the performance of the co-running tasks. The experimental results show that the techniques proposed in this thesis is effective

Efficient parallel processing of all-pairs shortest paths on multicore and GPU systems

Finding the shortest path between any two nodes in a graph, known as the All-Pairs Shortest Paths (APSP), is a fundamental problem in many data analysis problems, such as supply chains in logistics, routing protocols in IoT networks that involve consumer electronics as well as data analysis for social networking apps and Google Maps apps used by the general public on their smartphones. In this work, we present a novel approach to solve the APSP problem on multicore and GPU systems. In our approach, a graph is first pre-processed by partitioning the graph into sub-graphs. Then, each sub-graph is processed in parallel using any existing shortest path algorithm such as the Floyd-Warshall algorithm or Dijkstra’s algorithm. Finally, the distance results in individual sub-graphs are aggregated to obtain the distances of APSP for the entire graph. OpenMP and CUDA are used to implement the parallelization on multicore CPUs and GPUs, respectively. We conduct the extensive experiments with both synthetic and real-world graphs on the JADE (Joint Academic Data Science Endeavour) cluster at the University of Oxford, which is part of the Tier-2 high performance computing facilities in the UK. In the experiments, we compared our methods with three existing APSP algorithms in the literature, including n-Dijkstra, ParAPSP and SuperFW. The results show that our methods outperform the existing algorithms, achieving the speedup of up to 8.3x over Dijkstra

Investigating mechanisms of state localization in highly ionized dense plasmas

Producción CientíficaWe present experimental observations of Kβ emission from highly charged Mg ions at solid density, driven by intense x rays from a free electron laser. The presence of Kβ emission indicates the n=3 atomic shell is relocalized for high charge states, providing an upper constraint on the depression of the ionization potential. We explore the process of state relocalization in dense plasmas from first principles using finite-temperature density functional theory alongside a wave-function localization metric, and find excellent agreement with experimental results.This work has been supported by the Spanish Ministry of Science and Innovation under Research Grant No. PID2019-108764RB-I0

Investigating Mechanisms of State Localization in Highly-Ionized Dense Plasmas

We present the first experimental observation of K$_{\beta}$ emission from highly charged Mg ions at solid density, driven by intense x-rays from a free electron laser. The presence of K$_{\beta}$ emission indicates the $n=3$ atomic shell is relocalized for high charge states, providing an upper constraint on the depression of the ionization potential. We explore the process of state relocalization in dense plasmas from first principles using finite-temperature density functional theory alongside a wavefunction localization metric, and find excellent agreement with experimental results.Comment: 22 pages, 13 figure

Simulations of collisional effects in an inner-shell solid-density mg x-ray laser

Inner-shell Kα x-ray lasers have been created by pumping gaseous, solid, and liquid targets with the intense x-ray output of free-electron-lasers (FELs). For gaseous targets lasing relies on the creation of K-shell core-holes on a time-scale short compared with filling via Auger decay. In the case of solid and liquid density systems, collisional effects will also be important, affecting not only populations, but also line-widths, both of which impact the degree of overall gain, and its duration. However, to date such collisional effects have not been extensively studied. We present here initial simulations using the CCFLY code of inner-shell lasing in solid density Mg, where we self-consistently treat the effects of the incoming FEL radiation and the atomic kinetics of the Mg system, including radiative, Auger, and collisional effects. We find that the combination of collisional population of the lower states of the lasing transitions and broadening of the lines precludes lasing on all but the Kα of the initially cold system. Even assuming instantaneous turning on of the FEL pump, we find the duration of the gain in the solid system to be sub-femtosecond

vGrouper: Optimizing the Performance of Parallel Jobs in Xen by Increasing Synchronous Execution of Virtual Machines

International audienceXen is one of the most popular virtualization platforms nowadays, which has been broadly used by the industry. Credit scheduler, the default scheduler of Xen, was initially designed for serial jobs, which achieves good performance overall for serial jobs. Unfortunately, the parallel jobs are likely to co-exist with serial jobs in the same host in practice, the resource contention between virtual machines results in severe performance degradation of the parallel jobs. In this paper, we propose vGrouper, a progressive solution to enhance the performance of the parallel jobs. The vGrouper focuses on synchronizing the execution time of the parallel nodes in order to achieve the best performance of the parallel job. Moreover, the vGrouper guarantees that the parallel job nodes are able to run concurrently on pCPUs for the entire time slice, which maximizes the efficiency of communication between parallel nodes. A prototype of vGrouper is implemented, the experimental results demonstrate that the performance of the parallel job and resource utilization in Xen have been significantly improved

vPlacer : a co-scheduler for optimizing the performance of parallel jobs in Xen

Xen, a popular virtualization platform which enables multiple operating systems sharing one physical host, has been widely used in various fields nowadays. Currently, the existing schedulers of Xen are initially targeting at serial jobs, which achieves a remarkable utilization of computer hardware and impressive overall performance. However, the virtualized systems are expected to accommodate both parallel jobs and serial jobs in practice, and resource contention between virtual machines results in severe performance degradation of the parallel jobs. Moreover, the physical resource is vastly wasted during the communication process due to the ineffective scheduling of parallel jobs. This paper aims to optimize the performance of the parallel jobs in Xen using the co-scheduling mechanism. In this paper, we statistically analyze the process of scheduling parallel jobs in Xen, which points out that the credit scheduler is not capable of properly scheduling a parallel job. Moreover, we propose vPlacer, a conservative co-scheduler to improve the performance of the parallel job in Xen. Our co-scheduler is able to identify the parallel jobs and optimize the scheduling process to satisfy the particularity of the parallel job. The prototype of our vPlacer is implemented, and the experimental results show that the performance of the parallel job is significantly improved and the utilization of the hardware resource is optimized

Contention-aware prediction for performance impact of task co-running in multicore computers

In this paper, we investigate the influential factors that impact on the performance when the tasks are co-running on a multicore computers. Further, we propose the machine learning-based prediction framework to predict the performance of the co-running tasks. In particular, two prediction frameworks are developed for two types of task in our model: repetitive tasks (i.e., the tasks that arrive at the system repetitively) and new tasks (i.e., the task that are submitted to the system the first time). The difference between which is that we have the historical running information of the repetitive tasks while we do not have the prior knowledge about new tasks. Given the limited information of the new tasks, an online prediction framework is developed to predict the performance of co-running new tasks by sampling the performance events on the fly for a short period and then feeding the sampled results to the prediction framework. We conducted extensive experiments with the SPEC2006 benchmark suite to compare the effectiveness of different machine learning methods considered in this paper. The results show that our prediction model can achieve the accuracy of 99.38% and 87.18% for repetitive tasks and new tasks, respectively