A Distributed System for Parallel Simulations

We presented the technologies and algorithms to build a web-based visualization and steering system to monitor the dynamics of remote parallel simulations executed on a Linux Cluster. The polynomial time based algorithm to optimally utilize distributed computing resources over a network to achieve maximum frame-rate was also proposed. Keeping up with the advancements in modern web technologies, we have developed an Ajax-based web frontend which allows users to remotely access and control ongoing computations via a web browser facilitated by visual feedbacks in real-time. Experimental results are also given from sample runs mapped to distributed computing nodes and initiated by users at different geographical locations. Our preliminary results on frame-rates illustrated that system performance was affected by network conditions of the chosen mapping loop including available network bandwidth and computing capacities. The underlying programming framework of our system supports mixed-programming mode and is flexible to integrate most serial or parallel simulation code written in different programming languages such as Fortran, C and Java

The Brain on Low Power Architectures - Efficient Simulation of Cortical Slow Waves and Asynchronous States

Efficient brain simulation is a scientific grand challenge, a parallel/distributed coding challenge and a source of requirements and suggestions for future computing architectures. Indeed, the human brain includes about 10^15 synapses and 10^11 neurons activated at a mean rate of several Hz. Full brain simulation poses Exascale challenges even if simulated at the highest abstraction level. The WaveScalES experiment in the Human Brain Project (HBP) has the goal of matching experimental measures and simulations of slow waves during deep-sleep and anesthesia and the transition to other brain states. The focus is the development of dedicated large-scale parallel/distributed simulation technologies. The ExaNeSt project designs an ARM-based, low-power HPC architecture scalable to million of cores, developing a dedicated scalable interconnect system, and SWA/AW simulations are included among the driving benchmarks. At the joint between both projects is the INFN proprietary Distributed and Plastic Spiking Neural Networks (DPSNN) simulation engine. DPSNN can be configured to stress either the networking or the computation features available on the execution platforms. The simulation stresses the networking component when the neural net - composed by a relatively low number of neurons, each one projecting thousands of synapses - is distributed over a large number of hardware cores. When growing the number of neurons per core, the computation starts to be the dominating component for short range connections. This paper reports about preliminary performance results obtained on an ARM-based HPC prototype developed in the framework of the ExaNeSt project. Furthermore, a comparison is given of instantaneous power, total energy consumption, execution time and energetic cost per synaptic event of SWA/AW DPSNN simulations when executed on either ARM- or Intel-based server platforms

Allocation of Distributed Generation for Maximum Reduction of Energy Losses in Distribution Systems

The analysis of actual distribution systems with penetration of distributed generation requires powerful tools with capabilities that until very recently were not available in distribution software tools; for instance, probabilistic and time mode simulations. This chapter presents the application of parallel computing to the allocation of distributed generation for maximum reduction of energy losses in distribution system when the system is evaluated during a given period (e.g., the target is to minimize energy losses for periods equal or longer than 1 year). The simulations have been carried out using OpenDSS, a freely available software tool for distribution system studies, when it is driven as a COM DLL from MATLAB using a multicore installation. The chapter details a MATLAB–OpenDSS procedure for allocation of photovoltaic (PV) generation in distribution systems using a parallel Monte Carlo approach and assuming that loads are voltage‐dependent. The main goals are to check the viability of a Monte Carlo method in some studies for which parallel computing can be advantageously applied and propose a simple procedure for minimization of energy losses in distribution systems

Rigorous results on spontaneous symmetry breaking in a one-dimensional driven particle system

We study spontaneous symmetry breaking in a one-dimensional driven two-species stochastic cellular automaton with parallel sublattice update and open boundaries. The dynamics are symmetric with respect to interchange of particles. Starting from an empty initial lattice, the system enters a symmetry broken state after some time T_1 through an amplification loop of initial fluctuations. It remains in the symmetry broken state for a time T_2 through a traffic jam effect. Applying a simple martingale argument, we obtain rigorous asymptotic estimates for the expected times ~ L ln(L) and ln() ~ L, where L is the system size. The actual value of T_1 depends strongly on the initial fluctuation in the amplification loop. Numerical simulations suggest that T_2 is exponentially distributed with a mean that grows exponentially in system size. For the phase transition line we argue and confirm by simulations that the flipping time between sign changes of the difference of particle numbers approaches an algebraic distribution as the system size tends to infinity.Comment: 23 pages, 7 figure

Output feedback robust distributed model predictive control for parallel systems in process networks with competitive characteristics

The parallel structure is one of the basic system architectures found in process networks. This paper formulates control strategies for such parallel systems when the states are unmeasured. The competitive coupling and competitive constraints are addressed in the control design. A distributed buffer and pre-estimator are proposed to solve problems relating to coupling and timely communication whilst a distributed moving horizon estimator is employed to further improve the estimation accuracy in the presence of the constraints. An output feedback robust distributed model predictive control algorithm is then developed for such parallel systems. The Lyapunov method is used for the theoretical analysis which produces tractable linear matrix inequalities (LMI). Simulations and experimental results are provided to validate the effectiveness of the proposed approach

A Distributed Algebra System for Time Integration on Parallel Computers

We present a distributed algebra system for efficient and compact implementation of numerical time integration schemes on parallel computers and graphics processing units (GPU). The software implementation combines the time integration library Odeint from Boost with the OpenFPM framework for scalable scientific computing. Implementing multi-stage, multi-step, or adaptive time integration methods in distributed-memory parallel codes or on GPUs is challenging. The present algebra system addresses this by making the time integration methods from Odeint available in a concise template-expression language for numerical simulations distributed and parallelized using OpenFPM. This allows using state-of-the-art time integration schemes, or switching between schemes, by changing one line of code, while maintaining parallel scalability. This enables scalable time integration with compact code and facilitates rapid rewriting and deployment of simulation algorithms. We benchmark the present software for exponential and sigmoidal dynamics and present an application example to the 3D Gray-Scott reaction-diffusion problem on both CPUs and GPUs in only 60 lines of code