Optimistic Simulation Research Articles

Modeling of the Policy Design to Build Public Confidence Facing Recession Issues

The condition of global economic uncertainty initiated the news that Indonesia is predicted to have the potential for recession. This study presents a model for designing policies to create public Confidence in East Java in the face of recession. The method used in this study begins with grounded theory and continues with modeling and policy simulation. Grounded theory is carried out with netnographic studies to find concepts and quantitative studies to test these concepts in the people of East Java. The netnography study was conducted by extracting sample data totaling 3,827 netizen comments using Nawala software and processed using NVIVO software. Quantitative studies are carried out by testing data collected with instruments in the form of questionnaires. The sample size amounted to 120, with respondents from East Java. Quantitative data were obtained using SPSS software to see how much influence factors affect public Confidence in East Java. Modeling is done using Powersim software, and policy simulation is done using Promethee software. The results show that low interest rates in optimistic simulations and Business as Usual (BAU) decisions can be made by Bank Indonesia (BI). The latest update in this research is that the modeling is carried out not only based on East Java's macroeconomic conditions but also on grounded theory studies in East Java community studies

From KMS Fusion to HB11 Energy and Xcimer Energy, a personal 50 year IFE perspective

Shortly after the laser was invented in 1960, scientists sought to use it for thermonuclear fusion. By 1963, Livermore had a classified laser inertial confinement fusion (ICF) program and leaders predicted scientific breakeven by 1973. In 1974, KMS Fusion, Inc. announced thermonuclear neutrons from a laser target and promised grid electricity within 10 years. Private capital was attracted, but the data fell far short of the optimistic simulations. Magnetic fusion energy has had civilian funding (DOE), while ICF has primarily received military funding (DOE Defense Programs and now NNSA). As bigger lasers have been built and better simulations performed, optimism about ICF breakeven has waxed and waned. The achievement of ignition and gain on NIF has validated ICF's scientific basis, and the DOE and venture capital funded private companies are again interested in inertial fusion energy (IFE). The new DOE Milestone-Based Fusion Development Program is creating public–private partnerships to accelerate progress toward fusion pilot plants. ARPA-E, DOE INFUSE, and DOE IFE STAR are also building a U.S. IFE program within DOE. The U.S. leads in ICF, but developing IFE is an international competition. Private companies are leading the way. HB11 Energy Pty Ltd. is pursuing the aneutronic proton–boron fuel cycle. Xcimer Energy is developing a disruptive IFE technology to achieve high laser energies at dramatically lower costs. This 50-year perspective discusses where the U.S. IFE program is headed and promising strategies for progress in establishing an effective U.S. IFE program from both public and private perspectives.

Analysis of the Impact of Electric Charging Infrastructure Provision Rules on the Calculation of Fast and Ultrafast Charging Service Costs at Time Base and Energy Base Charging Stations

This study aims to analyze the impact of the issuance of MEMR Regulation Number 1 of 2023 concerning the Provision of Electric Charging Infrastructure for Battery-Based Electric Motor Vehicles, especially focusing on provisions related to electricity tariffs on surcharge fees for KBLBB fast charging and ultrafast charging consumers/customers. The calculation of surcharge fees can be economically affected by investment costs, operational costs, electric vehicle volume, and charging energy per electric vehicle (EV), so this study on surcharge fees is calculated using several scenarios and simulations carried out. The scenario is based on investment costs and operational costs using high and low cost financing calculations, and the simulation in question is on the volume of energy and electric vehicles over the next 15 years with pessimistic, moderate and optimistic simulations. This study uses quantitative descriptive methods that aim to describe or descriptive about a situation objectively using numbers, starting from data collection, interpretation through analysis of the data and appearance and results. The results of calculations from scenarios and simulations that have been carried out, calculations that are close to economics and based on current conditions that low cost scenarios with moderate simulations are appropriate recommendations to be applied at this time. Of course, the amount of application of the service fee must be evaluated by the Government every year, because the calculation component greatly affects the amount of the service fee.

Insights into the photovoltaic properties of indium sulfide as an electron transport material in perovskite solar cells

According to recent reports, planar structure-based organometallic perovskite solar cells (OPSCs) have achieved remarkable power conversion efficiency (PCE), making them very competitive with the more traditional silicon photovoltaics. A complete understanding of OPSCs and their individual parts is still necessary for further enhancement in PCE. In this work, indium sulfide (In2S3)-based planar heterojunction OPSCs were proposed and simulated with the SCAPS (a Solar Cell Capacitance Simulator)-1D programme. Initially, OPSC performance was calibrated with the experimentally fabricated architecture (FTO/In2S3/MAPbI3/Spiro-OMeTAD/Au) to evaluate the optimum parameters of each layer. The numerical calculations showed a significant dependence of PCE on the thickness and defect density of the MAPbI3 absorber material. The results showed that as the perovskite layer thickness increased, the PCE improved gradually but subsequently reached a maximum at thicknesses greater than 500 nm. Moreover, parameters involving the series resistance as well as the shunt resistance were recognized to affect the performance of the OPSC. Most importantly, a champion PCE of over 20% was yielded under the optimistic simulation conditions. Overall, the OPSC performed better between 20 and 30 °C, and its efficiency rapidly decreases above that temperature.

TauSSA

In this paper, we present TauSSA, a discrete-event simulation tool for stochastic queueing networks integrated in the LINE solver. TauSSA combines Gillespie's stochastic simulation algorithm with tau leaping, a methodology for optimistic simulation acceleration. Although tau leaping is frequently used in chemical reaction network simulation, it has so far found limited application in queueing theory. TauSSA offers one of the very first attempts to make this method broadly applicable to analyze extended queueing network models, which include class switching, fork-join, and non-exponential service and arrival distributions. We conceptualize various strategies for handling ordering and illegal states in tau leaping that arise specifically within queueing network models, and compare their performance through numerical experiments. Our main finding is that strategies that sort events based on the network topological order incur a better trade-off between speedup and approximation error.

On the Investigation of Monthly River Flow Generation Complexity Using the Applicability of Machine Learning Models

Streamflow is associated with several sources on nonstationaries and hence developing machine learning (ML) models is always the motive to provide a reliable methodology to understand the actual mechanism of streamflow. The current research was devoted to generating monthly streamflows from annual streamflow. In this study, three different ML models were applied for this purpose, including Multiple Additive Regression Trees (MART), Group Methods of Data Handling (GMDH), and Gene Expression Programming (GEP). The models were developed based on annual streamflow and monthly time index of three rivers (i.e., Upper Zab, Lower Zab, and Diyala) located in the north region of Iraq. The modeling results indicated an optimistic simulation for generating the monthly streamflow time series from annual streamflow time series. The potential of the MART model was superior to the GMDH and GEP models for Upper Zab River (R2 0.84, 0.64, and 0.47), Lower Zab River (R2 0.75, 0.46, and 0.40), and Diyala River (R2 0.78, 0.42, and 0.5). The results of RMSE were 113, 169, and 208 for Upper Zab River, 95, 149, and 0.5 for Lower Zab River, and 73, 118, and 109 for Diyala River. The results have proved the possibility of changing the timescale in generating streamflow data.

Guiding Robot Exploration in Reinforcement Learning via Automated Planning

Reinforcement learning (RL) enables an agent to learn from trial-and-error experiences toward achieving long-term goals; automated planning aims to compute plans for accomplishing tasks using action knowledge. Despite their shared goal of completing complex tasks, the development of RL and automated planning has been largely isolated due to their different computational modalities. Focusing on improving RL agents' learning efficiency, we develop Guided Dyna-Q (GDQ) to enable RL agents to reason with action knowledge to avoid exploring less-relevant states. The action knowledge is used for generating artificial experiences from an optimistic simulation. GDQ has been evaluated in simulation and using a mobile robot conducting navigation tasks in a multi-room office environment. Compared with competitive baselines, GDQ significantly reduces the effort in exploration while improving the quality of learned policies.

Local time evolution in Personal Communication Service model

We investigate the local time evolution in the Personal Communication Service (PCS) model simulated with the parallel discrete event simulation method’s optimistic algorithm. We propose a model for the optimistic local virtual time evolution (OLVT) in PCS, which is reminiscent of statistical physics’s surface growth. We use Rensselaer’s optimistic simulation system with the Time Warp implementation. We compare the results of the simulations of both PCS and OLVT models and found good agreement. We discuss the highlights of our approach in the analysis of scalability and synchronization using the OLVT model.

SEECSSim: A toolkit for parallel and distributed simulations for mobile devices

ABSTRACT Increase of the computing power on handheld devices has made these devices attractive for many applications including simulation systems. There are Parallel Discrete-Event Simulation (PDES) frameworks exists but most of these are designed for cluster systems, not suitable for battery operated devices. In this paper, the well- known Rensselaer’s Optimistic Simulation System (ROSS) framework has been instrumented for a detailed analysis in terms of CPU usage, memory consumption, energy and power requirements. This profiling helps in selecting the appropriate synchronisation algorithm for running the PDES frameworks on the mobile devices. Further, we propose a simulation framework (referred as SEECSSim1) that is specifically designed for handheld devices. It is the first one designed keeping in mind the characteristics and the constraints of mobile devices. SEECSSim includes the support for a number of state-of-the-art synchronisation protocols. The performance of SEECSSim has been studied using a PHOLD simulation model for different synchronisation algorithms.

PDES-A

In this article, we present experiences implementing a general Parallel Discrete Event Simulation (PDES) accelerator on a Field Programmable Gate Array (FPGA). The accelerator can be specialized to any particular simulation model by defining the object states and the event handling code, which are then synthesized into a custom accelerator for the given model. The accelerator consists of several event processors that can process events in parallel while maintaining the dependencies between them. Events are automatically sorted by a self-sorting event queue. The accelerator supports optimistic simulation by automatically keeping track of event history and supporting rollbacks. The architecture is limited in scalability locally by the communication and port bandwidth of the different structures. However, it is designed to allow multiple accelerators to be connected to scale up the simulation. We evaluate the design and explore several design trade-offs and optimizations. We show that the accelerator can scale to 64 concurrent event processors relative to the performance of a single event processor. At this point, the scalability becomes limited by contention on the shared structures within the datapath. To alleviate this bottleneck, we also develop a new version of the datapath that partitions the state and event space of the simulation but allows these partitions to share the use of the event processors. The new design substantially reduces contention and improves the performance with 64 processors from 49x to 62x relative to a single processor design. We went through two iterations of the design of PDES-A, first using Verilog and then using Chisel (for the partitioned version of the design). We report in this article on some observations in the differences in prototyping accelerators using these two different languages. PDES-A outperforms the ROSS simulator running on a 12-core Intel Xeon machine by a factor of 3.2x with less than 15% of the power consumption. Our future work includes building multiple interconnected PDES-A cores.

Optimistic Modeling and Simulation of Complex Hardware Platforms and Embedded Systems on Many-Core HPC Clusters

The large size and complexity of the modern digital hardware impose great challenges to design and validation. Hardware Description Languages (HDLs) and System-Level Description Languages (SLDLs) rely on sequential discrete event semantics. Parallel Discrete Event Simulation (PDES) has recently gained extensive attention for parallelizing these languages due to the ever-increasing complexity of embedded and cyber physical systems. However, PDES application has not yet reached acceptable maturity and pervasiveness for accelerating computer architecture problems. This is due to inherent complexity of hardware components that require using different advanced PDES techniques. In this paper, we look at the main problem from a radically different angle. First, we suppose there is only a single universal discrete event model of computation for simulation purely defined by distributed optimistic PDES, i.e., logical-process-based event scheduling worldview. Second, we construct a new parallel system-level simulation language called OSML for ESL. Third, we propose a unified Cloud-based CAD tool called Troodon to automatically parallelize existing hardware languages atop OSML. To the best of our knowledge, OSML is the first work on optimistic synchronization applied to hardware-specific SLDLs and hardware models at different levels of abstraction in ESL, which contain complex data structures by proposing a hybrid checkpointing scheme.

Modeling Large-Scale Slim Fly Networks Using Parallel Discrete-Event Simulation

As supercomputers approach exascale performance, the increased number of processors translates to an increased demand on the underlying network interconnect. The slim fly network topology, a new low-diameter, low-latency, and low-cost interconnection network, is gaining interest as one possible solution for next-generation supercomputing interconnect systems. In this article, we present a high-fidelity slim fly packet-level model leveraging the Rensselaer Optimistic Simulation System (ROSS) and Co-Design of Exascale Storage (CODES) frameworks. We validate the model with published work before scaling the network size up to an unprecedented 1 million compute nodes and confirming that the slim fly observes peak network throughput at extreme scale. In addition to synthetic workloads, we evaluate large-scale slim fly models with real communication workloads from applications in the Design Forward program with over 110,000 MPI processes. We show strong scaling of the slim fly model on an Intel cluster achieving a peak network packet transfer rate of 2.3 million packets per second and processing over 7 billion discrete events using 128 MPI tasks. Enabled by the strong performance capabilities of the model, we perform a detailed application trace and routing protocol performance study. Through analysis of metrics such as packet latency, hop count, and congestion, we find that the slim fly network is able to leverage simple minimal routing and achieve the same performance as more complex adaptive routing for tested DOE benchmark applications.

Generation of Reversible C++ Code for Optimistic Parallel Discrete Event Simulation

The reversible execution of C/C++ code has been a target of research and engineering for more than a decade as reversible computation has become a central notion in large-scale parallel discrete event simulation (PDES). The simulation models that are implemented for PDES are of increasing complexity and size and require various language features to support abstraction, encapsulation, and composition when building a simulation model. In this paper, we focus on parallel simulation models that are written with user-defined C++ abstractions and abstractions of the C++ Standard Library. We present an approach based on incremental state saving for establishing reversibility of C++ and an evaluation for a kinetic Monte-Carlo simulation implemented in C++. Although a significant runtime overhead is introduced with our technique, it is an enormous win that it allows using the entire C++ language, and has that code automatically transformed into reversible code to enable parallel execution with the Rensselaer’s optimistic simulation system (ROSS).

Semi‐asynchronous approximate parallel DEVS simulation of web search engines

SummaryDiscrete Event System Specification (DEVS) is a formalism for the modeling and analysis of discrete event systems. Parallel DEVS (PDEVS) is an extension of DEVS for supporting Parallel and Discrete Event Simulation, which is a powerful tool for evaluating the performance of large scale systems. In this work, we propose an optimistic approximate and semi‐asynchronous parallel strategy. The level of optimism is efficiently managed throughout the simulation execution, and it is automatically adjusted based on the simulation evolution. Load balance and model partitioning is automatically made by means of an algorithm that takes advantage of the communication pattern of the simulated model. Our proposal is designed for Web search engines, which are complex and highly optimized systems devised to operate on large clusters of processors and dealing with dynamic and unpredictable user query bursts. The results show that our proposal is able to reduce both execution times and memory usage of standard optimistic simulations of Web search engine models, at the expense of small errors.

Automatic Model Generation for Gate-Level Circuit PDES with Reverse Computation

Gate-level circuit simulation is an important step in the design and validation of complex circuits. This step of the process relies on existing libraries for gate specifications. We start with a generic gate model for Rensselaer’s Optimistic Simulation System, a parallel discrete-event simulation framework. This generic model encompasses all functionality needed by optimistic simulation using reverse computation. We then describe a parser system that uses a standardized gate library to create a specific model for simulation. The generated model is composed of several functions, including those needed for an accurate model of timing behavior. To quantify the improvements that an automatically generated model can have over a hand written model, we compare two gate library models: an automatically generated lsi -10 k library model and a previously investigated, handwritten, simplified gtech library model. We conclude that the automatically generated model is a more accurate model of actual hardware. In comparison to previous results, we find that the automatically generated model is able to achieve better optimistic simulation performance when measured against conservative simulation. To test the automatically generated model, we evaluate the performance of a simulation of a full-scale OpenSPARC T2 processor model. This model consists of nearly 6 million LPs. We achieve a peak performance of 1.63 million events per second during a conservative simulation. To understand the relatively weaker performance of optimistic simulation, we investigate hot spots of event activity and visually identify a workload imbalance.

Enabling Parallel Simulation of Large-Scale HPC Network Systems

With the increasing complexity of today's high-performance computing (HPC) architectures, simulation has become an indispensable tool for exploring the design space of HPC systems-in particular, networks. In order to make effective design decisions, simulations of these systems must possess the following properties: (1) have high accuracy and fidelity, (2) produce results in a timely manner, and (3) be able to analyze a broad range of network workloads. Most state-of-the-art HPC network simulation frameworks, however, are constrained in one or more of these areas. In this work, we present a simulation framework for modeling two important classes of networks used in today's IBM and Cray supercomputers: torus and dragonfly networks. We use the Co-Design of Multi-layer Exascale Storage Architecture (CODES) simulation framework to simulate these network topologies at a flit-level detail using the Rensselaer Optimistic Simulation System (ROSS) for parallel discrete-event simulation. Our simulation framework meets all the requirements of a practical network simulation and can assist network designers in design space exploration. First, it uses validated and detailed flit-level network models to provide an accurate and high-fidelity network simulation. Second, instead of relying on serial time-stepped or traditional conservative discrete-event simulations that limit simulation scalability and efficiency, we use the optimistic event-scheduling capability of ROSS to achieve efficient and scalable HPC network simulations on today's high-performance cluster systems. Third, our models give network designers a choice in simulating a broad range of network workloads, including HPC application workloads using detailed network traces, an ability that is rarely offered in parallel with high-fidelity network simulations.

Multithreaded Stochastic PDES for Reactions and Diffusions in Neurons.

Cells exhibit stochastic behavior when the number of molecules is small. Hence a stochastic reaction-diffusion simulator capable of working at scale can provide a more accurate view of molecular dynamics within the cell. This paper describes a parallel discrete event simulator, Neuron Time Warp-Multi Thread (NTW-MT), developed for the simulation of reaction diffusion models of neurons. To the best of our knowledge, this is the first parallel discrete event simulator oriented towards stochastic simulation of chemical reactions in a neuron. The simulator was developed as part of the NEURON project. NTW-MT is optimistic and thread-based, which attempts to capitalize on multi-core architectures used in high performance machines. It makes use of a multi-level queue for the pending event set and a single roll-back message in place of individual anti-messages to disperse contention and decrease the overhead of processing rollbacks. Global Virtual Time is computed asynchronously both within and among processes to get rid of the overhead for synchronizing threads. Memory usage is managed in order to avoid locking and unlocking when allocating and de-allocating memory and to maximize cache locality. We verified our simulator on a calcium buffer model. We examined its performance on a calcium wave model, comparing it to the performance of a process based optimistic simulator and a threaded simulator which uses a single priority queue for each thread. Our multi-threaded simulator is shown to achieve superior performance to these simulators. Finally, we demonstrated the scalability of our simulator on a larger CICR model and a more detailed CICR model.

Autonomic State Management for Optimistic Simulation Platforms

We present the design and implementation of an autonomic state manager (ASM) tailored for integration within optimistic parallel discrete event simulation (PDES) environments based on the C programming language and the executable and linkable format (ELF), and developed for execution on ×86_64 architectures. With ASM, the state of any logical process (LP), namely the individual (concurrent) simulation unit being part of the simulation model, is allowed to be scattered on dynamically allocated memory chunks managed via standard API (e.g., malloc/free). Also, the application programmer is not required to provide any serialization/ deserialization module in order to take a checkpoint of the LP state, or to restore it in case a causality error occurs during the optimistic run, or to provide indications on which portions of the state are updated by event processing, so to allow incremental checkpointing. All these tasks are handled by ASM in a fully transparent manner via (A) runtime identification (with chunk-level granularity) of the memory map associated with the LP state, and (B) runtime tracking of the memory updates occurring within chunks belonging to the dynamic memory map. The co-existence of the incremental and non-incremental log/restore modes is achieved via dual versions of the same application code, transparently generated by ASM via compile/link time facilities. Also, the dynamic selection of the best suited log/ restore mode is actuated by ASM on the basis of an innovative modeling/optimization approach which takes into account stability of each operating mode with respect to variations of the model/environmental execution parameters.

Prospects and challenges of touchless electrostatic detumbling of small bodies

The prospects of touchlessly detumbling a small, multiple meters in size, space object using electrostatic forces are intriguing. Physically capturing an object with a large rotation rate poses significant momentum transfer and collision risks. If the spin rate is reduced to less than 1deg/s, relative motion sensing and control associated with mechanical docking becomes manageable. In particular, this paper surveys the prospects and challenges of detumbling large debris objects near Geostationary Earth Orbit for active debris remediation, and investigates if such electrostatic tractors are suitable for small asteroids being considered for asteroid retrieval missions. Active charge transfer is used to impart arresting electrostatic torques on such objects, given that they are sufficiently non-spherical. The concept of touchless electrostatic detumbling of space debris is outlined through analysis and experiments and is shown to hold great promise to arrest the rotation within days to weeks. However, even conservatively optimistic simulations of small asteroid detumbling scenarios indicate that such a method could take over a year to arrest the asteroid rotation. The numerical debris detumbling simulation includes a charge transfer model in a space environment, and illustrates how a conducting rocket body could be despun without physical contact.

A low-overhead constant-time Lowest-Timestamp-First CPU scheduler for high-performance optimistic simulation platforms

An approach to high-performance discrete event simulation consists of exploiting parallelization techniques. These rely on partitioning the simulation model into multiple, interacting simulation objects, also known as Logical Processes (LPs), which concurrently execute events on different CPUs and/or multiple CPU-cores. However, despite the tendency towards high degree of hardware parallelism, for relatively large models, multi-programming schemes are still needed in order to share a single CPU-core across multiple LPs. Consequently, priority management and CPU-scheduling remain central issues for the effectiveness of any parallel simulation environment. This article focuses on the optimistic approach to parallelism, which is based on speculative processing and maintains event-causality across concurrent LPs via rollback techniques. Specifically, the article presents a low-overhead constant-time implementation of the well known Lowest-Timestamp-First algorithm for the identification of the next LP to be CPU-dispatched. This proposal is suited for contexts where the optimistic simulation system conforms to the best-practice of keeping separate event lists for the hosted LPs. The implementation has been integrated in the open source ROOT-Sim (ROme OpTimistic Simulator) package. The effectiveness of the presented proposal is assessed via an extended performance study, carried out by relying on the game of life as the test-bed application.