Parallel Simulation Techniques Research Articles

Structures of MERS-CoV macro domain in aqueous solution with dynamics: Impacts of parallel tempering simulation techniques and CHARMM36m and AMBER99SB force field parameters.

A novel virus, severe acute respiratory syndrome Coronavirus 2 (SARS‐CoV‐2), causing coronavirus disease 2019 (COVID‐19) worldwide appeared in 2019. Detailed scientific knowledge of the members of the Coronaviridae family, including the Middle East Respiratory Syndrome Coronavirus (MERS‐CoV) is currently lacking. Structural studies of the MERS‐CoV proteins in the current literature are extremely limited. We present here detailed characterization of the structural properties of MERS‐CoV macro domain in aqueous solution. Additionally, we studied the impacts of chosen force field parameters and parallel tempering simulation techniques on the predicted structural properties of MERS‐CoV macro domain in aqueous solution. For this purpose, we conducted extensive Hamiltonian‐replica exchange molecular dynamics simulations and Temperature‐replica exchange molecular dynamics simulations using the CHARMM36m and AMBER99SB parameters for the macro domain. This study shows that the predicted secondary structure properties including their propensities depend on the chosen simulation technique and force field parameter. We perform structural clustering based on the radius of gyration and end‐to‐end distance of MERS‐CoV macro domain in aqueous solution. We also report and analyze the residue‐level intrinsic disorder features, flexibility and secondary structure. Furthermore, we study the propensities of this macro domain for protein‐protein interactions and for the RNA and DNA binding. Overall, results are in agreement with available nuclear magnetic resonance spectroscopy findings and present more detailed insights into the structural properties of MERS CoV macro domain in aqueous solution. All in all, we present the structural properties of the aqueous MERS‐CoV macro domain using different parallel tempering simulation techniques, force field parameters and bioinformatics tools.

A network‐decomposition‐based multi‐rate parallel transient simulation technique for active distribution networks

A network‐decomposition‐based multi‐rate parallel transient simulation technique for active distribution networks

A Study on Parallel and Pipelining Simulation Techniques for Edge Detection and Their Performance Analysis

Real processing components along with component simulators are combined together to construct a new virtual prototyping system. The increase in component simulators result in degraded performance of the simulation in distributed systems. The speed of simulation can be increased by doing parallel simulation techniques. Prime number test and Image edge detection are chosen to implement the parallel simulation techniques and achieved the expected results while implementing in real time applications. The prime number test calculates the number of processors in a system and the image edge detection can be done in two stages by Canny Edge detection and Sobel Edge detection. The Canny Edge detection is used to detect the edges in the images by using a multi-stage algorithm. The smaller, separable and integer valued filter in images are combined in horizontal and vertical directions by using the Sobel edge detection resulting in reduction of implementation cost. The tool named OpenMP is used for implementing the parallel simulation techniques by combining both the canny edge and Sobel edge detection. An add-on named MPI is used along with the OpenMP to reduce the implementation time in parallel processing.

Parallel Tempering Monte Carlo Studies of Phase Transition of Free Boundary Planar Surfaces.

We numerically study surface models defined on hexagonal disks with a free boundary. 2D surface models for planar surfaces have recently attracted interest due to the engineering applications of functional materials such as graphene and its composite with polymers. These 2D composite meta-materials are strongly influenced by external stimuli such as thermal fluctuations if they are sufficiently thin. For this reason, it is very interesting to study the shape stability/instability of thin 2D materials against thermal fluctuations. In this paper, we study three types of surface models including Landau-Ginzburg (LG) and Helfirch-Polyakov models defined on triangulated hexagonal disks using the parallel tempering Monte Carlo simulation technique. We find that the planar surfaces undergo a first-order transition between the smooth and crumpled phases in the LG model and continuous transitions in the other two models. The first-order transition is relatively weak compared to the transition on spherical surfaces already reported. The continuous nature of the transition is consistent with the reported results, although the transitions are stronger than that of the reported ones.

Leveraging Time Prediction and Error Compensation to Enhance the Scalability of Parallel Multi-Core Simulations

Due to synchronization overhead, it is challenging to apply the parallel simulation technique of multi-core processors at larger scales. Although the use of lax synchronization schemes could reduce overhead and balance the load between synchronous points, it introduces timing error and deteriorates simulation accuracy. Through observing the propagation paths of errors, we find that these paths always concentrate on some pivotal events. Based on the observation, we design a delay-calibration mechanism to alleviate errors. We decouple the timing and functional processes of the pivotal events, leveraging prediction technique of delays to connect two categories of the processes. Errors are traced throughout the timing processes of the pivotal events, and are deducted from the predicted delays before the delays are consumed by the functional processes. Therefore, through cleaning the errors at the successive pivot events, the mechanism decreases the simulated time deviations efficiently. Since the prediction and error deduction processes do not have any constraint on synchronizations, our approach largely maintains the scalability of lax synchronization schemes. Furthermore, our proposal is orthogonal to other parallel simulation techniques and can be used in conjunction with them. Experimental results show that error compensation improves the accuracy of lax synchronized simulations by 68 percent and achieves 97.8 percent accuracy when combined with an enhanced lax synchronization.

Parallel SystemC Simulation for ESL Design

Virtual platforms have become essential tools for the design of embedded systems. Developers rely on them for design space exploration and software debugging. However, with rising HW/SW complexity and the need to simulate more and more processors simultaneously, the performance of virtual platforms degrades rapidly. Parallel simulation techniques can help to counter this by leveraging multicore PCs, which are widely available today. This work presents a novel parallel simulation approach that is targeted toward acceleration of virtual platforms from the ESL domain. By trading some timing accuracy, multiprocessor virtual platforms can be accelerated by up to 3.4× on regular quad-core workstations.

Parallel GPU Architecture Simulation Framework Exploiting Architectural-Level Parallelism with Timing Error Prediction

The performance analysis and study of large-scale many-core processor architectures require fast and highly accurate simulation techniques in order to reduce time consumption. State-of-the-art graphics processing units (GPUs), which are used extensively as coprocessors in the high-performance-computing area, also require fast simulation techniques because they have massively complex microarchitectures with thousands of processing elements. At present, however, GPU simulators do not have sufficient simulation speed for advanced software and architecture studies. In this study, we propose a new parallel simulation framework and a new parallel simulation technique for improving the simulation speed of GPUs. The proposed framework facilitates multithreaded simulation by exploiting the architectural-level parallelism and execution model parallelism of GPUs. In addition, an error predictive synchronization scheme based on a timing error prediction mechanism is used to minimize the cycle errors and simulator slowdown during parallel simulations. The experimental results obtained using a simulator with the proposed framework showed that the proposed technique provided a speedup of up to 8.9 times compared with an existing single-thread-based GPU simulator on a 16-core machine.

LegaSCi

Architects and developers use virtual prototypes of computer systems to receive early feedback on hardware design decisions as well as to develop and debug system software. This is facilitated by the comprehensive inspection capabilities virtual prototypes offer. For virtual prototypes, execution speed is crucial to support the users' productivity. Parallel simulation techniques are employed to offset the speed impact of the increasing number of cores that need to be simulated in virtual prototypes of parallel and embedded systems. SystemC is the de facto industry standard library for virtual platform modeling. Since currently no parallel SystemC library is commonly available, typical SystemC models are coded for execution in sequential simulation environments. Simply putting such models into parallel simulators may lead to thread-safety issues and may additionally cause nondeterministic simulator behavior. This article proposes a methodology to support simulation creators to face the challenge of integrating such legacy models into parallel SystemC environments. The feasibility of the proposed method is evaluated by parallelizing the latest instance of the EU FP7 project EURETILE embedded platform simulator. Using legaSCi , on four host processor cores a speedup of 2.13× is demonstrated, without having to change the individual models of the simulator.

Airborne Downward Looking Sparse Linear Array 3-D SAR Heterogeneous Parallel Simulation

The airborne downward looking sparse linear array three dimensional synthetic aperture radar (DLSLA 3-D SAR) operates nadir observation with the along-track synthetic aperture formulated by platform movement and the cross-track synthetic aperture formulated by physical sparse linear array. Considering the lack of DLSLA 3-D SAR data in the current preliminary study stage, it is very important and essential to develop DLSLA 3-D SAR simulation (echo generation simulation and image reconstruction simulation, including point targets simulation and 3-D distributed scene simulation). In this paper, DLSLA 3-D SAR imaging geometry, the echo signal model and the heterogeneous parallel technique are discussed first. Then, heterogeneous parallel echo generation simulation with time domain correlation and the frequency domain correlation method is described. In the following, heterogeneous parallel image reconstruction simulation with two imaging algorithms, e.g., 3-D polar format algorithm, polar formatting and L1 regularization algorithm is discussed. Finally, the point targets and the 3-D distributed scene simulation are demonstrated to validate the effectiveness and performance of our proposed heterogeneous parallel simulation technique. The 3-D distributed scene employs airborne X-band DEM and P-band Circular SAR image of the same area as simulation scene input.

ZSim

Architectural simulation is time-consuming, and the trend towards hundreds of cores is making sequential simulation even slower. Existing parallel simulation techniques either scale poorly due to excessive synchronization, or sacrifice accuracy by allowing event reordering and using simplistic contention models. As a result, most researchers use sequential simulators and model small-scale systems with 16-32 cores. With 100-core chips already available, developing simulators that scale to thousands of cores is crucial. We present three novel techniques that, together, make thousand-core simulation practical. First, we speed up detailed core models (including OOO cores) with instruction-driven timing models that leverage dynamic binary translation. Second, we introduce bound-weave, a two-phase parallelization technique that scales parallel simulation on multicore hosts efficiently with minimal loss of accuracy. Third, we implement lightweight user-level virtualization to support complex workloads, including multiprogrammed, client-server, and managed-runtime applications, without the need for full-system simulation, sidestepping the lack of scalable OSs and ISAs that support thousands of cores. We use these techniques to build zsim, a fast, scalable, and accurate simulator. On a 16-core host, zsim models a 1024-core chip at speeds of up to 1,500 MIPS using simple cores and up to 300 MIPS using detailed OOO cores, 2-3 orders of magnitude faster than existing parallel simulators. Simulator performance scales well with both the number of modeled cores and the number of host cores. We validate zsim against a real Westmere system on a wide variety of workloads, and find performance and microarchitectural events to be within a narrow range of the real system.

A distributed timing synchronization technique for parallel multi-core instruction-set simulation

As multi-core architecture has become the mainstream, the corresponding multi-core instruction-set simulation (MCISS) is also needed to aid system development. Ideally, we may run a MCISS in parallel to enhance the simulation speed. However, the conventional centralized timing synchronization mechanism would greatly constrain the parallelism of a MCISS, so the simulation speed is bounded. To resolve this issue, we propose a new distributed timing synchronization technique which allows higher parallelism for a MCISS. Hence, it accelerates the simulation speed by 9 to 20 times as the number of cores increases in contrast to the centralized synchronization approach.

A Parallel Simulation Technique for Multicore Embedded Systems and Its Performance Analysis

A virtual prototyping system is constructed by replacing real processing components with component simulators running concurrently. The performance of such a distributed simulation decreases drastically as the number of component simulators increases. Thus, we propose a novel parallel simulation technique to boost up the simulation speed. In the proposed technique, a simulator wrapper performs time synchronization with the simulation backplane on behalf of the associated component simulator itself. Component simulators send null messages periodically to the backplane to enable parallel simulation without any causality problems. Since excessive communication may degrade the simulation performance, we also propose a novel performance analysis technique to determine an optimal period of null message transfer, considering both the characteristics of a target application and the configurations of the simulation host. Through intensive experiments, we show that the proposed parallel simulation achieves almost linear speedup to the number of processor cores if the frequency of null message transfer is optimally decided. The proposed analysis technique could predict the simulation performance with more than 90% accuracy in the worst case for various target applications and simulation environments we have used for experiments.

A new parallel simulation technique

We develop a “semi-parallel” simulation technique suggested by Pretorius and Lehner, in which the simulation spacetime volume is divided into a large number of small 4-volumes that have only initial and final surfaces. Thus there is no two-way communication between processors, and the 4-volumes can be simulated independently and potentially at different times. This technique allows us to simulate much larger volumes than we otherwise could, because we are not limited by total memory size. No processor time is lost waiting for other processors. We compare a cosmic string simulation we developed using the semi-parallel technique with our previous MPI-based code for several test cases and find a factor of 2.6 improvement in the total amount of processor time required to accomplish the same job for strings evolving in the matter-dominated era.

Fine-Grained Parallel and Distributed Spatial Stochastic Simulation of Biological Reactions

To date, discrete event stochastic simulations of large scale biological reaction systems are extremely compute-intensive and time-consuming. Besides, it has been widely accepted that spatial factor plays a critical role in the dynamics of most biological reaction systems. The NSM (the Next Sub-Volume Method), a spatial variation of the Gillespie’s stochastic simulation algorithm (SSA), has been proposed for spatially stochastic simulation of those systems. While being able to explore high degree of parallelism in systems, NSM is inherently sequential, which still suffers from the problem of low simulation speed. Fine-grained parallel execution is an elegant way to speed up sequential simulations. Thus, based on the discrete event simulation framework JAMES II, we design and implement a PDES (Parallel Discrete Event Simulation) TW (time warp) simulator to enable the fine-grained parallel execution of spatial stochastic simulations of biological reaction systems using the ANSM (the Abstract NSM), a parallel variation of the NSM. The simulation results of classical Lotka-Volterra biological reaction system show that our time warp simulator obtains remarkable parallel speed-up against sequential execution of the NSM.I.IntroductionThe goal of Systems biology is to obtain system-level investigations of the structure and behavior of biological reaction systems by integrating biology with system theory, mathematics and computer science [1][3], since the isolated knowledge of parts can not explain the dynamics of a whole system. As the complement of “wet-lab” experiments, stochastic simulation, being called the “dry-computational” experiment, plays a more and more important role in computing systems biology [2]. Among many methods explored in systems biology, discrete event stochastic simulation is of greatly importance [4][5][6], since a great number of researches have present that stochasticity or “noise” have a crucial effect on the dynamics of small population biological reaction systems [4][7]. Furthermore, recent research shows that the stochasticity is not only important in biological reaction systems with small population but also in some moderate/large population systems [7].To date, Gillespie’s SSA [8] is widely considered to be the most accurate way to capture the dynamics of biological reaction systems instead of traditional mathematical method [5][9]. However, SSA-based stochastic simulation is confronted with two main challenges: Firstly, this type of simulation is extremely time-consuming, since when the types of species and the number of reactions in the biological system are large, SSA requires a huge amount of steps to sample these reactions; Secondly, the assumption that the systems are spatially homogeneous or well-stirred is hardly met in most real biological systems and spatial factors play a key role in the behaviors of most real biological systems [19][20][21][22][23][24]. The next sub-volume method (NSM) [18], presents us an elegant way to access the special problem via domain partition. To our disappointment, sequential stochastic simulation with the NSM is still very time-consuming, and additionally introduced diffusion among neighbor sub-volumes makes things worse. Whereas, the NSM explores a very high degree of parallelism among sub-volumes, and parallelization has been widely accepted as the most meaningful way to tackle the performance bottleneck of sequential simulations [26][27]. Thus, adapting parallel discrete event simulation (PDES) techniques to discrete event stochastic simulation would be particularly promising. Although there are a few attempts have been conducted [29][30][31], research in this filed is still in its infancy and many issues are in need of further discussion. The next section of the paper presents the background and related work in this domain. In section III, we give the details of design and implementation of model interfaces of LP paradigm and the time warp simulator based on the discrete event simulation framework JAMES II; the benchmark model and experiment results are shown in Section IV; in the last section, we conclude the paper with some future work.II. Background and Related WorkA. Parallel Discrete Event Simulation (PDES)The notion Logical Process (LP) is introduced to PDES as the abstract of the physical process [26], where a system consisting of many physical processes is usually modeled by a set of LP. LP is regarded as the smallest unit that can be executed in PDES and each LP holds a sub-partition of the whole system’s state variables as its private ones. When a LP processes an event, it can only modify the state variables of its own. If one LP needs to modify one of its neighbors’ state variables, it has to schedule an event to the target neighbor. That is to say event message exchanging is the only way that LPs interact with each other. Because of the data dependences or interactions among LPs, synchronization protocols have to be introduced to PDES to guarantee the so-called local causality constraint (LCC) [26]. By now, there are a larger number of synchronization algorithms have been proposed, e.g. the null-message [26], the time warp (TW) [32], breath time warp (BTW) [33] and etc. According to whether can events of LPs be processed optimistically, they are generally divided into two types: conservative algorithms and optimistic algorithms. However, Dematté and Mazza have theoretically pointed out the disadvantages of pure conservative parallel simulation for biochemical reaction systems [31]. B. NSM and ANSM The NSM is a spatial variation of Gillespie’ SSA, which integrates the direct method (DM) [8] with the next reaction method (NRM) [25]. The NSM presents us a pretty good way to tackle the aspect of space in biological systems by partitioning a spatially inhomogeneous system into many much more smaller “homogeneous” ones, which can be simulated by SSA separately. However, the NSM is inherently combined with the sequential semantics, and all sub-volumes share one common data structure for events or messages. Thus, directly parallelization of the NSM may be confronted with the so-called boundary problem and high costs of synchronously accessing the common data structure [29]. In order to obtain higher efficiency of parallel simulation, parallelization of NSM has to firstly free the NSM from the sequential semantics and secondly partition the shared data structure into many “parallel” ones. One of these is the abstract next sub-volume method (ANSM) [30]. In the ANSM, each sub-volume is modeled by a logical process (LP) based on the LP paradigm of PDES, where each LP held its own event queue and state variables (see Fig. 1). In addition, the so-called retraction mechanism was introduced in the ANSM too (see algorithm 1). Besides, based on the ANSM, Wang etc. [30] have experimentally tested the performance of several PDES algorithms in the platform called YH-SUPE [27]. However, their platform is designed for general simulation applications, thus it would sacrifice some performance for being not able to take into account the characteristics of biological reaction systems. Using the similar ideas of the ANSM, Dematté and Mazza have designed and realized an optimistic simulator. However, they processed events in time-stepped manner, which would lose a specific degree of precisions compared with the discrete event manner, and it is very hard to transfer a time-stepped simulation to a discrete event one. In addition, Jeschke etc.[29] have designed and implemented a dynamic time-window simulator to execution the NSM in parallel on the grid computing environment, however, they paid main attention on the analysis of communication costs and determining a better size of the time-window.Fig. 1: the variations from SSA to NSM and from NSM to ANSMC. JAMES II JAMES II is an open source discrete event simulation experiment framework developed by the University of Rostock in Germany. It focuses on high flexibility and scalability [11][13]. Based on the plug-in scheme [12], each function of JAMES II is defined as a specific plug-in type, and all plug-in types and plug-ins are declared in XML-files [13]. Combined with the factory method pattern JAMES II innovatively split up the model and simulator, which makes JAMES II is very flexible to add and reuse both of models and simulators. In addition, JAMES II supports various types of modelling formalisms, e.g. cellular automata, discrete event system specification (DEVS), SpacePi, StochasticPi and etc.[14]. Besides, a well-defined simulator selection mechanism is designed and developed in JAMES II, which can not only automatically choose the proper simulators according to the modeling formalism but also pick out a specific simulator from a serious of simulators supporting the same modeling formalism according to the user settings [15].III. The Model Interface and SimulatorAs we have mentioned in section II (part C), model and simulator are split up into two separate parts. Thus, in this section, we introduce the designation and implementation of model interface of LP paradigm and more importantly the time warp simulator.A. The Mod Interface of LP ParadigmJAMES II provides abstract model interfaces for different modeling formalism, based on which Wang etc. have designed and implemented model interface of LP paradigm[16]. However, this interface is not scalable well for parallel and distributed simulation of larger scale systems. In our implementation, we accommodate the interface to the situation of parallel and distributed situations. Firstly, the neighbor LP’s reference is replaced by its name in LP’s neighbor queue, because it is improper even dangerous that a local LP hold the references of other LPs in remote memory space. In addition, (pseudo-)random number plays a crucial role to obtain valid and meaningful results in stochastic simulations. However, it is still a very challenge work to find a good random number generator (RNG) [34]. Thus, in order to focus on our problems, we introduce one of the uniform RNGs of JAMES II to this model interface, where each LP holds a private RNG so that random number streams of different LPs can be independent stochastically. B. The Time Warp SimulatorBased on the simulator interface provided by JAMES II, we design and implement the time warp simulator, which contains the (master-)simulator, (LP-)simulator. The simulator works strictly as master/worker(s) paradigm for fine-grained parallel and distributed stochastic simulations. Communication costs are crucial to the performance of a fine-grained parallel and distributed simulation. Based on the Java remote method invocation (RMI) mechanism, P2P (peer-to-peer) communication is implemented among all (master-and LP-)simulators, where a simulator holds all the proxies of targeted ones that work on remote workers. One of the advantages of this communication approach is that PDES codes can be transferred to various hardwire environment, such as Clusters, Grids and distributed computing environment, with only a little modification; The other is that RMI mechanism is easy to realized and independent to any other non-Java libraries. Since the straggler event problem, states have to be saved to rollback events that are pre-processed optimistically. Each time being modified, the state is cloned to a queue by Java clone mechanism. Problem of this copy state saving approach is that it would cause loads of memory space. However, the problem can be made up by a condign GVT calculating mechanism. GVT reduction scheme also has a significant impact on the performance of parallel simulators, since it marks the highest time boundary of events that can be committed so that memories of fossils (processed events and states) less than GVT can be reallocated. GVT calculating is a very knotty for the notorious simultaneous reporting problem and transient messages problem. According to our problem, another GVT algorithm, called Twice Notification (TN-GVT) (see algorithm 2), is contributed to this already rich repository instead of implementing one of GVT algorithms in reference [26] and [28].This algorithm looks like the synchronous algorithm described in reference [26] (pp. 114), however, they are essentially different from each other. This algorithm has never stopped the simulators from processing events when GVT reduction, while algorithm in reference [26] blocks all simulators for GVT calculating. As for the transient message problem, it can be neglect in our implementation, because RMI based remote communication approach is synchronized, that means a simulator will not go on its processing until the remote the massage get to its destination. And because of this, the high-costs message acknowledgement, prevalent over many classical asynchronous GVT algorithms, is not needed anymore too, which should be constructive to the whole performance of the time warp simulator.IV. Benchmark Model and Experiment ResultsA. The Lotka-Volterra Predator-prey SystemIn our experiment, the spatial version of Lotka-Volterra predator-prey system is introduced as the benchmark model (see Fig. 2). We choose the system for two considerations: 1) this system is a classical experimental model that has been used in many related researches [8][30][31], so it is credible and the simulation results are comparable; 2) it is simple but helpful enough to test the issues we are interested in. The space of predator-prey System is partitioned into a2D NXNgrid, whereNdenotes the edge size of the grid. Initially the population of the Grass, Preys and Predators are set to 1000 in each single sub-volume (LP). In Fig. 2,r1,r2,r3stand for the reaction constants of the reaction 1, 2 and 3 respectively. We usedGrass,dPreyanddPredatorto stand for the diffusion rate of Grass, Prey and Predator separately. Being similar to reference [8], we also take the assumption that the population of the grass remains stable, and thusdGrassis set to zero.R1:Grass + Prey ->2Prey(1)R2:Predator +Prey -> 2Predator(2)R3:Predator -> NULL(3)r1=0.01; r2=0.01; r3=10(4)dGrass=0.0;dPrey=2.5;dPredato=5.0(5)Fig. 2: predator-prey systemB. Experiment ResultsThe simulation runs have been executed on a Linux Cluster with 40 computing nodes. Each computing node is equipped with two 64bit 2.53 GHz Intel Xeon QuadCore Processors with 24GB RAM, and nodes are interconnected with Gigabit Ethernet connection. The operating system is Kylin Server 3.5, with kernel 2.6.18. Experiments have been conducted on the benchmark model of different size of mode to investigate the execution time and speedup of the time warp simulator. As shown in Fig. 3, the execution time of simulation on single processor with 8 cores is compared. The result shows that it will take more wall clock time to simulate much larger scale systems for the same simulation time. This testifies the fact that larger scale systems will leads to more events in the same time interval. More importantly, the blue line shows that the sequential simulation performance declines very fast when the mode scale becomes large. The bottleneck of sequential simulator is due to the costs of accessing a long event queue to choose the next events. Besides, from the comparison between group 1 and group 2 in this experiment, we could also conclude that high diffusion rate increased the simulation time greatly both in sequential and parallel simulations. This is because LP paradigm has to split diffusion into two processes (diffusion (in) and diffusion (out) event) for two interactive LPs involved in diffusion and high diffusion rate will lead to high proportional of diffusion to reaction. In the second step shown in Fig. 4, the relationship between the speedups from time warp of two different model sizes and the number of work cores involved are demonstrated. The speedup is calculated against the sequential execution of the spatial reaction-diffusion systems model with the same model size and parameters using NSM.Fig. 4 shows the comparison of speedup of time warp on a64X64grid and a100X100grid. In the case of a64X64grid, under the condition that only one node is used, the lowest speedup (a little bigger than 1) is achieved when two cores involved, and the highest speedup (about 6) is achieved when 8 cores involved. The influence of the number of cores used in parallel simulation is investigated. In most cases, large number of cores could bring in considerable improvements in the performance of parallel simulation. Also, compared with the two results in Fig. 4, the simulation of larger model achieves better speedup. Combined with time tests (Fig. 3), we find that sequential simulator’s performance declines sharply when the model scale becomes very large, which makes the time warp simulator get better speed-up correspondingly.Fig. 3: Execution time (wall clock time) of Seq. and time warp with respect to different model sizes (N=32, 64, 100, and 128) and model parameters based on single computing node with 8 cores. Results of the test are grouped by the diffusion rates (Group 1: Sequential 1 and Time Warp 1. dPrey=2.5, dPredator=5.0; Group 2: dPrey=0.25, dPredator=0.5, Sequential 2 and Time Warp 2).Fig. 4: Speedup of time warp with respect to the number of work cores and the model size (N=64 and 100). Work cores are chose from one computing node. Diffusion rates are dPrey=2.5, dPredator=5.0 and dGrass=0.0.V. Conclusion and Future WorkIn this paper, a time warp simulator based on the discrete event simulation framework JAMES II is designed and implemented for fine-grained parallel and distributed discrete event spatial stochastic simulation of biological reaction systems. Several challenges have been overcome, such as state saving, roll back and especially GVT reduction in parallel execution of simulations. The Lotka-Volterra Predator-Prey system is chosen as the benchmark model to test the performance of our time warp simulator and the best experiment results show that it can obtain about 6 times of speed-up against the sequential simulation. The domain this paper concerns with is in the infancy, many interesting issues are worthy of further investigated, e.g. there are many excellent PDES optimistic synchronization algorithms (e.g. the BTW) as well. Next step, we would like to fill some of them into JAMES II. In addition, Gillespie approximation methods (tau-leap[10] etc.) sacrifice some degree of precision for higher simulation speed, but still could not address the aspect of space of biological reaction systems. The combination of spatial element and approximation methods would be very interesting and promising; however, the parallel execution of tau-leap methods should have to overcome many obstacles on the road ahead.AcknowledgmentThis work is supported by the National Natural Science Foundation of China (NSF) Grant (No.60773019) and the Ph.D. Programs Foundation of Ministry of Education of China (No. 200899980004). The authors would like to show their great gratitude to Dr. Jan Himmelspach and Dr. Roland Ewald at the University of Rostock, Germany for their invaluable advice and kindly help with JAMES II.ReferencesH. Kitano, "Computational systems biology." Nature, vol. 420, no. 6912, pp. 206-210, November 2002.H. Kitano, "Systems biology: a brief overview." 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Parallel computing of discrete element method on multi-core processors

This paper describes parallel simulation techniques for the discrete element method (DEM) on multi-core processors. Recently, multi-core CPU and GPU processors have attracted much attention in accelerating computer simulations in various fields. We propose a new algorithm for multi-thread parallel computation of DEM, which makes effective use of the available memory and accelerates the computation. This study shows that memory usage is drastically reduced by using this algorithm. To show the practical use of DEM in industry, a large-scale powder system is simulated with a complicated drive unit. We compared the performance of the simulation between the latest GPU and CPU processors with optimized programs for each processor. The results show that the difference in performance is not substantial when using either GPUs or CPUs with a multi-thread parallel algorithm. In addition, DEM algorithm is shown to have high scalability in a multi-thread parallel computation on a CPU.

Estimating the divisibility of complex biological networks by sparseness indices

In order to understand the complex relationships among the components of biological systems, network models have been used for a long time. Although they have been extensively used for visualization, data storage, structural analysis and simulation, some computational processes are still very inefficient when applied on complex networks. In particular, any parallel simulation technique requires a network previously divided into a number of clusters in numbers equal to that of the available processors. At the same time, let maximally disconnected clusters be chosen in order to minimize extra-communication overhead and to optimize the overall computational efficiency. Obtaining such a disconnection becomes a computationally hard problem when disconnection conditions are complex in themselves, like in the case of parallel simulation. Before applying any clustering method, topological indices might contribute to give an a priori insight about the divisibility of a network. Here we present a class of them, the sparseness indices. As particular topological indices provide either local or global quantification of network structure, they can help in identifying locally dense, but globally sparsely connected subgraphs.

差分法による 3次元不均質場での地震波伝播の大規模計算

The development of high-performance parallel computers such as the cluster of PCs and the Earth Simulator supercomputers made it possible for realistic 3-D simulation of seismic wave propagation and strong ground motions in heterogeneous structure. The seismic wavefield in heterogeneous structure is often evaluated numerically by solving equation of motions based on the finite-difference method (FDM). In the present paper, the FDM simulation of seismic wave propagation in heterogeneous structure is reviewed briefly with recently developed techniques such as for accurate representation of seismic phenomena in heterogeneous structure. Efficient techniques to reduce computational cost of the FDM simulation are also presented. The effectiveness of the vector-computing hardware for the FDM simulation and a parallel simulation technique are demonstrated with linearly increasing computational performance using large number of processors of the Earth Simulator.

Diagnosis of Multiple Scan Chain Timing Faults

A diagnosis technique is presented to locate multiple timing faults in scan chains. Jump simulation is a novel parallel simulation technique which quickly searches for the upper and the lower bounds of every individual fault. The proposed technique takes into account the interaction of multiple faults so the diagnosis results are deterministic, not probabilistic. This technique is very useful in the production test environment because it requires only regular automated test pattern generator patterns, not specialized diagnosis patterns. Experiments on ISCAS'89 benchmark circuits show that this technique can successfully pinpoint almost every single one of 16 hold-time faults in a scan chain of more than 800 scan cells. The proposed technique is still effective when failure data are limited or faults are clustered.

Simple continuous and discrete models for simulating replica exchange simulations of protein folding.

The efficiency of temperature replica exchange (RE) simulations hinge on their ability to enhance conformational sampling at physiological temperatures by taking advantage of more rapid conformational interconversions at higher temperatures. While temperature RE is a parallel simulation technique that is relatively straightforward to implement, kinetics in the RE ensemble is complicated, and there is much to learn about how best to employ RE simulations in computational biophysics. Protein folding rates often slow down above a certain temperature due to entropic bottlenecks. This "anti-Arrhenius" behavior represents a challenge for RE. However, it is far from straightforward to systematically explore the impact of this on RE by brute force molecular simulations, since RE simulations of protein folding are very difficult to converge. To understand some of the basic mechanisms that determine the efficiency of RE, it is useful to study simplified low dimensionality systems that share some of the key characteristics of molecular systems. Results are presented concerning the efficiency of temperature RE on a continuous two-dimensional potential that contains an entropic bottleneck. Optimal efficiency was obtained when the temperatures of the replicas did not exceed the temperature at which the harmonic mean of the folding and unfolding rates is maximized. This confirms a result we previously obtained using a discrete network model of RE. Comparison of the efficiencies obtained using the continuous and discrete models makes it possible to identify non-Markovian effects, which slow down equilibration of the RE ensemble on the more complex continuous potential. In particular, the rate of temperature diffusion and also the efficiency of RE is limited by the time scale of conformational rearrangements within free energy basins.

Simulating replica exchange simulations of protein folding with a kinetic network model.

Replica exchange (RE) is a generalized ensemble simulation method for accelerating the exploration of free-energy landscapes, which define many challenging problems in computational biophysics, including protein folding and binding. Although temperature RE (T-RE) is a parallel simulation technique whose implementation is relatively straightforward, kinetics and the approach to equilibrium in the T-RE ensemble are very complicated; there is much to learn about how to best employ T-RE to protein folding and binding problems. We have constructed a kinetic network model for RE studies of protein folding and used this reduced model to carry out "simulations of simulations" to analyze how the underlying temperature dependence of the conformational kinetics and the basic parameters of RE (e.g., the number of replicas, the RE rate, and the temperature spacing) all interact to affect the number of folding transitions observed. When protein folding follows anti-Arrhenius kinetics, we observe a speed limit for the number of folding transitions observed at the low temperature of interest, which depends on the maximum of the harmonic mean of the folding and unfolding transition rates at high temperature. The results shown here for the network RE model suggest ways to improve atomic-level RE simulations such as the use of "training" simulations to explore some aspects of the temperature dependence for folding of the atomic-level models before performing RE studies.