Nonlinear optimization is becoming more challenging in information sciences and various industrial applications, but nonlinear problems solved by the classical particle swarm‐based methods are usually characterized by low efficiency, accuracy, and convergence speed in specific issues. To solve these problems and enhance the nonlinear optimization performance, an improved metaheuristic particle swarm optimization (PSO) model is proposed here. First, the optimization principles and model of the new method are introduced, and algorithms of the improved PSO are presented by updating the displacement and velocity of the moving particle according to Euler–Maruyama (EM) principle rather than traditional standard normal distribution. Then, the influence of the model parameters, input dimensions, and different nonlinear problems on the PSO optimization characterizations are studied by Pareto set solving and optimization performance comparison. The analysis regarding diverse nonlinear problems and optimization methods manifests that the improved method is capable of solving various nonlinear problems especially for multiobjective models, while the robustness and reliability can always keep consistent regardless of the change of model parameters. Finally, the performance evaluation is exhibited by the case study of nonlinear parameter optimization, 3 groups of CEC benchmark problems, and rank‐sum test for 6 comparable optimization algorithms, which all verify its effectiveness and reliability, as well as the significance and great application promise. The results show that the new proposed PSO method has the fastest convergence speed and least iteration numbers in searching for the global best solution of 9 nonlinear problems among 8 different optimization models indicated by the p values smaller than 0.05. Additionally, the main conclusions showing the calculation efficiency, stability, robustness, and great application promise of the proposed method are summarized, and future work is discussed.

Support vector machine (SVM) has been applied in data classification and defect recognition in various scenes, the common kernels are not always suitable to satisfy the requirement of high accuracy and efficiency inspection. A new hybrid conditional kernel (HCK) and SVM model based on exponential functions is proposed in this article, the validity and feasibility of the basic characterizations are introduced theoretically first, and the numerical classification results for different datasets are calculated and compared with related algorithms. Then, the performance evaluation of the new model from the perspective of kernel parameter investigation, statistical data analysis, and experimental verification are conducted, and the wire rope defect inspection sample sets are built according to features extraction and eigenvalues computation. Finally, the comparison results where the new HCK based SVM can reach almost the highest classification accuracy of 91.70% and least running time of 0.1968 s for five different wire rope defects among 12 machine learning models not only prove the superior performance of the proposed method in wire rope defect recognition, but also show great promise in high precision classification in other application fields. The limitations of this model and future work are also discussed.

Recently, mobile applications such as augmented reality, virtual reality and face recognition have become more and more ubiquitous. These delay- sensitive applications demand intensive computation and high energy consumption. However, mobile devices have limited battery power and computation resources that influence the quality of experience (QoE) of mobile users. Mobile edge computing (MEC) has become a key technology to meet these demands. The crucial challenges regarding MEC paradigm are computation offloading decision, spectrum and computation resource allocation for offloading users. In order to deal with these challenges, this paper formulates a joint delay-energy optimization problem by jointly considering spectrum resource Allocation, computation resource Allocation and computation Offloading decision (AAO). Further, we transform the original problem into convex optimization and solve the optimization problem through an alternating direction method of multipliers (ADMM) algorithm in a distributed way. Finally, simulation results validate the efficiency of the proposed AAO in saving delay and energy consumption.

In the past few decades, a number of user-level threading and tasking models have been proposed in the literature to address the shortcomings of OS-level threads, primarily with respect to cost and flexibility. Current state-of-the-art user-level threading and tasking models, however, either are too specific to applications or architectures or are not as powerful or flexible. In this paper, we present Argobots, a lightweight, low-level threading and tasking framework that is designed as a portable and performant substrate for high-level programming models or runtime systems. Argobots offers a carefully designed execution model that balances generality of functionality with providing a rich set of controls to allow specialization by end users or high-level programming models. We describe the design, implementation, and performance characterization of Argobots and present integrations with three high-level models: OpenMP, MPI, and colocated I/O services. Evaluations show that (1) Argobots, while providing richer capabilities, is competitive with existing simpler generic threading runtimes; (2) our OpenMP runtime offers more efficient interoperability capabilities than production OpenMP runtimes do; (3) when MPI interoperates with Argobots instead of Pthreads, it enjoys reduced synchronization costs and better latency-hiding capabilities; and (4) I/O services with Argobots reduce interference with colocated applications while achieving performance competitive with that of a Pthreads approach.

Abstract Cyber‐physical system has been widely used in various areas as the integration of computing and physical system. As a typical application of cyber‐physical system, cyber‐physical surveillance system for transportation (CPSST) allows real‐time video monitoring to facilitate the deploying of smart transportation systems. For video streaming in CPSST, dynamic radio spectrum management is a key technology dealing with the current situation that the spectrum resource is almost used up. In this paper, taking into account the application layer quality of service and wireless link security, a novel dynamic spectrum management scheme has been proposed to minimize the system cost of CPSST. Video distortion is considered as the application layer quality of service metric, and the system cost is defined as a combination of distortion and security cost. We use intra‐refreshing rate in video coding to minimize the distortion. Furthermore, the problem is formulated as a restless bandit system, which uses current and historical information to optimize the action, with the objective of maximizing the total discounted system reward. We also describe the two spectrum management processes. Extensive simulation results are presented to demonstrate the significant performance improvement of the proposed scheme compared with the existing one that ignores video distortion and subband security optimization. Copyright © 2014 John Wiley & Sons, Ltd.

Fine-grained communication in supercomputing applications often limits performance through high communication overhead and poor utilization of network bandwidth. This paper presents Topological Routing and Aggregation Module (TRAM), a library that optimizes fine-grained communication performance by routing and dynamically combining short messages. TRAM collects units of fine-grained communication from the application and combines them into aggregated messages with a common intermediate destination. It routes these messages along a virtual mesh topology mapped onto the physical topology of the network. TRAM improves network bandwidth utilization and reduces communication overhead. It is particularly effective in optimizing patterns with global communication and large message counts, such as all-to-all and many-to-many, as well as sparse, irregular, dynamic or data dependent patterns. We demonstrate how TRAM improves performance through theoretical analysis and experimental verification using benchmarks and scientific applications. We present speedups on petascale systems of 6x for communication benchmarks and up to 4x for applications.

The advent of petascale computing has introduced new challenges (e.g. Heterogeneity, system failure) for programming scalable parallel applications. Increased complexity and dynamism in science and engineering applications of today have further exacerbated the situation. Addressing these challenges requires more emphasis on concepts that were previously of secondary importance, including migratability, adaptivity, and runtime system introspection. In this paper, we leverage our experience with these concepts to demonstrate their applicability and efficacy for real world applications. Using the CHARM++ parallel programming framework, we present details on how these concepts can lead to development of applications that scale irrespective of the rough landscape of supercomputing technology. Empirical evaluation presented in this paper spans many miniapplications and real applications executed on modern supercomputers including Blue Gene/Q, Cray XE6, and Stampede.

Currently deployed petascale supercomputers typically use toroidal network topologies in three or more dimensions. While these networks perform well for topology-agnostic codes on a few thousand nodes, leadership machines with 20,000 nodes require topology awareness to avoid network contention for communication-intensive codes. Topology adaptation is complicated by irregular node allocation shapes and holes due to dedicated input/output nodes or hardware failure. In the context of the popular molecular dynamics program NAMD, we present methods for mapping a periodic 3-D grid of fixed-size spatial decomposition domains to 3-D Cray Gemini and 5-D IBM Blue Gene/Q toroidal networks to enable hundred-million atom full machine simulations, and to similarly partition node allocations into compact domains for smaller simulations using multiple-copy algorithms. Additional enabling techniques are discussed and performance is reported for NCSA Blue Waters, ORNL Titan, ANL Mira, TACC Stampede, and NERSC Edison.

IBM Blue Gene/Q is the next generation Blue Gene machine that can scale to tens of Peta Flops with 16 cores and 64 hardware threads per node. However, significant efforts are required to fully exploit its capacity on various applications, spanning multiple programming models. In this paper, we focus on the asynchronous message driven parallel programming model -Charm++. Since its behavior (asynchronous) is substantially different from MPI, that presents a challenge in porting it efficiently to BG/Q. On the other hand, the significant synergy between BG/Q software and Charm++ creates opportunities for effective utilization of BG/Q resources. We describe various novel fine-grained threading techniques in Charm++ to exploit the hardware features of the BG/Qcompute chip. These include the use of L2 atomics to implement lockless producer-consumer queues to accelerate communication between threads, fast memory allocates, hardware communication threads that are awakened via low overhead interrupts from the BG/Q wakeup unit. Burst of short messages is processed by using the Many to Many interface to reduce runtime overhead. We also present techniques to optimize NAMD computation via Quad Processing Unit (QPX) vector instructions and the acceleration of message rate via communication threads to optimize the Particle Mesh Ewald (PME) computation. We demonstrate the benefits of our techniques via two benchmarks, 3D Fast Fourier Transform, and the molecular dynamics application NAMD. For the 92,000-atom ApoA1 molecule, we achieved 683us/stepwith PME every 4 steps and 782us/step with PME every step.

Gemini, the network for the new Cray XE/XK systems, features low latency, high bandwidth and strong scalability. Its hardware support for remote direct memory access enables efficient implementation of the global address space programming languages. Although the user Generic Network Interface (uGNI) provides a low-level interface for Gemini with support to the message-passing programming model (MPI), it remains challenging to port alternative programming models with scalable performance. CHARM++ is an object-oriented message-driven programming model. Its applications have been shown to scale up to the full Jaguar Cray XT machine. In this paper, we present an implementation of this programming model on uGNI for the Cray XE/XK systems. Several techniques are presented to exploit the uGNI capabilites by reducing memory copy and registration overhead, taking advantage of the persistent communication, and improving intra-node communication. Our microbenchmark results demonstrate that the uGNI-based runtime system outperforms the MPI-based implementation by up to 50% in terms of message latency. For communication intensive applications such as N-Queens, this implementation scales up to 15, 360 cores of a Cray XE6 machine and is 70% faster than the MPI-based implementation. In molecular dynamics application NAMD, the performance is also considerably improved by as much as 18%.

Achieving good scaling for fine-grained communication intensive applications on modern supercomputers remains challenging. In our previous work, we have shown that such an application -- NAMD -- scales well on the full Jaguar XT5 without long-range interactions; Yet, with them, the speedup falters beyond 64K cores. Although the new Gemini interconnect on Cray XK6 has improved network performance, the challenges remain, and are likely to remain for other such networks as well. We analyze communication bottlenecks in NAMD and its CHARM++ runtime, using the Projections performance analysis tool. Based on the analysis, we optimize the runtime, built on the uGNI library for Gemini. We present several techniques to improve the fine-grained communication. Consequently, the performance of running 92224-atom Apoa1 with GPUs on TitanDev is improved by 36%. For 100-million-atom STMV, we improve upon the prior Jaguar XT5 result of 26 ms/step to 13 ms/step using 298,992 cores on Jaguar XK6.

A 100-million-atom biomolecular simulation with NAMD is one of the three benchmarks for the NSF-funded sustainable petascale machine. Simulating this large molecular system on a petascale machine presents great challenges, including handling I/O, large memory footprint and getting good strong-scaling results. In this paper, we present parallel I/O techniques to enable the simulation. A new SMP model is designed to efficiently utilize ubiquitous wide multicore clusters by extending the Charm++ asynchronous message-driven runtime. We exploit node-aware techniques to optimize both the application and the underlying SMP runtime. Hierarchical load balancing is further exploited to scale NAMD to the full Jaguar PF Cray XT5 (224,076 cores) at Oak Ridge National Laboratory, both with and without PME full electrostatics, achieving 93% parallel efficiency (vs 6720 cores) at 9 ms per step for a simple cutoff calculation. Excellent scaling is also obtained on 65,536 cores of the Intrepid Blue Gene/P at Argonne National Laboratory.

The fast development of the Internet means that the security problems of information systems become more important. The traditional security mechanisms are not enough. Keystroke dynamics identification is a good means to strengthen the security of the computer and the network. The paper provides a novel method, the AR model to identify keystrokers.