Heterogeneous Models Of Computation Research Articles

A 3D Transmurally Heterogeneous Computational Model of the Brugada Syndrome Phenotype

A 3D Transmurally Heterogeneous Computational Model of the Brugada Syndrome Phenotype

Simple Run-Time Infrastructure (SRTI): An accessible distributed computing platform for interdisciplinary simulation

Distributed computing is a necessity for conducting cross-disciplinary research where field-specific computational models (simulators) are available, but have not been designed to work together. An example of this is natural hazards research. Simulators abound in the disparate fields that fall under this area, e.g. social science, engineering, economics, and health, but little progress has been made to integrate the simulators to study overarching and cross-disciplinary disaster scenarios. The reason for slow penetration of this technology is the high barrier to entry, which requires extensive knowledge of computer science and programming. Building upon an existing platform named Simple Run Time Infrastructure (SRTI v1), a new, fundamentally different version (SRTI v2) is developed to address the issues mentioned above. Designed to provide a low barrier to use, SRTI v2 is developed for users with limited programming experience and designed to simplify and streamline a user’s effort to compose a distributed simulation and handle time management. To achieve this primary objective, pre-compiled components are provided including the RTI Management Server, RTI Wrapper, and a GUI. By exploiting these pre-compiled components, users can compose a scalable distributed simulation with heterogeneous computational models. To demonstrate the concepts behind SRTI, a cross-language simulation, modified and extended from a time-dependent resilience analysis of an electric power system in the literature, is presented to show the scalability and usability of SRTI. Features of the different versions of SRTI are discussed and useful features to develop in the future are outlined.

Functionalized Anatomical Models for Computational Life Sciences.

The advent of detailed computational anatomical models has opened new avenues for computational life sciences (CLS). To date, static models representing the anatomical environment have been used in many applications but are insufficient when the dynamics of the body prevents separation of anatomical geometrical variability from physics and physiology. Obvious examples include the assessment of thermal risks in magnetic resonance imaging and planning for radiofrequency and acoustic cancer treatment, where posture and physiology-related changes in shape (e.g., breathing) or tissue behavior (e.g., thermoregulation) affect the impact. Advanced functionalized anatomical models can overcome these limitations and dramatically broaden the applicability of CLS in basic research, the development of novel devices/therapies, and the assessment of their safety and efficacy. Various forms of functionalization are discussed in this paper: (i) shape parametrization (e.g., heartbeat, population variability), (ii) physical property distributions (e.g., image-based inhomogeneity), (iii) physiological dynamics (e.g., tissue and organ behavior), and (iv) integration of simulation/measurement data (e.g., exposure conditions, “validation evidence” supporting model tuning and validation). Although current model functionalization may only represent a small part of the physiology, it already facilitates the next level of realism by (i) driving consistency among anatomy and different functionalization layers and highlighting dependencies, (ii) enabling third-party use of validated functionalization layers as established simulation tools, and (iii) therefore facilitating their application as building blocks in network or multi-scale computational models. Integration in functionalized anatomical models thus leverages and potentiates the value of sub-models and simulation/measurement data toward ever-increasing simulation realism. In our o2S2PARC platform, we propose to expand the concept of functionalized anatomical models to establish an integration and sharing service for heterogeneous computational models, ranging from the molecular to the organ level. The objective of o2S2PARC is to integrate all models developed within the National Institutes of Health SPARC initiative in a unified anatomical and computational environment, to study the role of the peripheral nervous system in controlling organ physiology. The functionalization concept, as outlined for the o2S2PARC platform, could form the basis for many other application areas of CLS. The relationship to other ongoing initiatives, such as the Physiome Project, is also presented.

Semantical Markov Logic Network for Distributed Reasoning in Cyber-Physical Systems

The challenges associated with developing accurate models for cyber-physical systems are attributable to the intrinsic concurrent and heterogeneous computations of these systems. Even though reasoning based on interconnected domain specific ontologies shows promise in enhancing modularity and joint functionality modelling, it has become necessary to build interoperable cyber-physical systems due to the growing pervasiveness of these systems. In this paper, we propose a semantically oriented distributed reasoning architecture for cyber-physical systems. This model accomplishes reasoning through a combination of heterogeneous models of computation. Using the flexibility of semantic agents as a formal representation for heterogeneous computational platforms, we define autonomous and intelligent agent-based reasoning procedure for distributed cyber-physical systems. Sensor networks underpin the semantic capabilities of this architecture, and semantic reasoning based on Markov logic networks is adopted to address uncertainty in modelling. To illustrate feasibility of this approach, we present a Markov logic based semantic event model for cyber-physical systems and discuss a case study of event handling and processing in a smart home.

An efficient computational scheme for building operating maps for a flow reversal reactor

This paper describes the use of a two dimensional heterogeneous computational model for the catalytic reverse flow reactor for the combustion of lean methane mixtures. A uniform design method is used to generate a table of 41 operating points. The results from these points were used to generate a simple empirical model for the reactor at a single specified value of the superficial gas velocity. Stability maps that show the envelope for operation that satisfy a minimum level of conversion and a maximum operating temperature are generated using the empirical model. These maps delineate all possible combinations of methane concentration, switch time and mass extraction rate that are capable of giving stable operating conditions that meet these two criteria. The model is potentially useful in model predictive control of the reactor.

Embedding Polychrony into Synchrony

This paper presents an embedding of polychronous programs into synchronous ones. Due to this embedding, it is not only possible to deepen the understanding of these different models of computation, but, more importantly, it is possible to transfer compilation techniques that were developed for synchronous programs to polychronous programs. This transfer is nontrivial because the underlying paradigms differ more than their names suggest: Since synchronous systems react deterministically to given inputs in discrete steps, they are typically used to describe reactive systems with a totally ordered notion of time. In contrast, polychronous system models entail a partially ordered notion of time, and are most suited to interface a system with an asynchronous environment by specifying input/output constraints from which a deterministic controller may eventually be refined and synthesized. As particular examples for the mentioned cross fertilization, we show how a simulator and a verification backend for synchronous programs can be made available to polychronous specifications, which is a first step toward integrating heterogeneous models of computation.

Modeling Cyber–Physical Systems

This paper focuses on the challenges of modeling cyber–physical systems (CPSs) that arise from the intrinsic heterogeneity, concurrency, and sensitivity to timing of such systems. It uses a portion of an aircraft vehicle management system (VMS), specifically the fuel management subsystem, to illustrate the challenges, and then discusses technologies that at least partially address the challenges. Specific technologies described include hybrid system modeling and simulation, concurrent and heterogeneous models of computation, the use of domain-specific ontologies to enhance modularity, and the joint modeling of functionality and implementation architectures.

Heterogeneous Computational Model for Landform Attributes Representation on Multicore and Multi-GPU Systems

Mathematical models are often used to simplify landform representation. Its importance is due to the possibility of describing phenomena by means of mathematical models from a data sample. High processing power is needed to represent large areas with a satisfactory level of details. In order to accelerate the solution of complex problems, it is necessary to combine two basic components in heterogeneous systems formed by a multicore with one or more GPUs. In this paper, we present a methodology to represent landform attributes on heterogeneous multicore and multi-GPU systems using high performance computing techniques for efficient solution of two-dimensional polynomial regression model that allow to address large problem instances.

An asymmetric distributed shared memory model for heterogeneous parallel systems

Heterogeneous computing combines general purpose CPUs with accelerators to efficiently execute both sequential control-intensive and data-parallel phases of applications. Existing programming models for heterogeneous computing rely on programmers to explicitly manage data transfers between the CPU system memory and accelerator memory. This paper presents a new programming model for heterogeneous computing, called Asymmetric Distributed Shared Memory (ADSM), that maintains a shared logical memory space for CPUs to access objects in the accelerator physical memory but not vice versa. The asymmetry allows light-weight implementations that avoid common pitfalls of symmetrical distributed shared memory systems. ADSM allows programmers to assign data objects to performance critical methods. When a method is selected for accelerator execution, its associated data objects are allocated within the shared logical memory space, which is hosted in the accelerator physical memory and transparently accessible by the methods executed on CPUs. We argue that ADSM reduces programming efforts for heterogeneous computing systems and enhances application portability. We present a software implementation of ADSM, called GMAC, on top of CUDA in a GNU/Linux environment. We show that applications written in ADSM and running on top of GMAC achieve performance comparable to their counterparts using programmer-managed data transfers. This paper presents the GMAC system and evaluates different design choices. We further suggest additional architectural support that will likely allow GMAC to achieve higher application performance than the current CUDA model.

An asymmetric distributed shared memory model for heterogeneous parallel systems

Heterogeneous computing combines general purpose CPUs with accelerators to efficiently execute both sequential control-intensive and data-parallel phases of applications. Existing programming models for heterogeneous computing rely on programmers to explicitly manage data transfers between the CPU system memory and accelerator memory. This paper presents a new programming model for heterogeneous computing, called Asymmetric Distributed Shared Memory (ADSM), that maintains a shared logical memory space for CPUs to access objects in the accelerator physical memory but not vice versa. The asymmetry allows light-weight implementations that avoid common pitfalls of symmetrical distributed shared memory systems. ADSM allows programmers to assign data objects to performance critical methods. When a method is selected for accelerator execution, its associated data objects are allocated within the shared logical memory space, which is hosted in the accelerator physical memory and transparently accessible by the methods executed on CPUs. We argue that ADSM reduces programming efforts for heterogeneous computing systems and enhances application portability. We present a software implementation of ADSM, called GMAC, on top of CUDA in a GNU/Linux environment. We show that applications written in ADSM and running on top of GMAC achieve performance comparable to their counterparts using programmer-managed data transfers. This paper presents the GMAC system and evaluates different design choices. We further suggest additional architectural support that will likely allow GMAC to achieve higher application performance than the current CUDA model.

A computational study of mother rotor VF in the human ventricles.

Sudden cardiac death is one of the major causes of death in the industrialized world. It is most often caused by a cardiac arrhythmia called ventricular fibrillation (VF). Despite its large social and economical impact, the mechanisms for VF in the human heart yet remain to be identified. Two of the most frequently discussed mechanisms observed in experiments with animal hearts are the multiple wavelet and mother rotor hypotheses. Most recordings of VF in animal hearts are consistent with the multiple wavelet mechanism. However, in animal hearts, mother rotor fibrillation has also been observed. For both multiple wavelet and mother rotor VF, cardiac heterogeneity plays an important role. Clinical data of action potential restitution measured from the surface of human hearts have been recently published. These in vivo data show a substantial degree of spatial heterogeneity. Using these clinical restitution data, we studied the dynamics of VF in the human heart using a heterogeneous computational model of human ventricles. We hypothesized that this observed heterogeneity can serve as a substrate for mother rotor fibrillation. We found that, based on these data, mother rotor VF can occur in the human heart and that ablation of the mother rotor terminates VF. Furthermore, we found that both mother rotor and multiple wavelet VF can occur in the same heart depending on the initial conditions at the onset of VF. We studied the organization of these two types of VF in terms of filament numbers, excitation periods, and frequency domains. We conclude that mother rotor fibrillation is a possible mechanism in the human heart.

GiPSi: A Framework for Open Source/Open Architecture Software Development for Organ-Level Surgical Simulation

This paper presents the architectural details of an evolving open source/open architecture software framework for developing organ-level surgical simulations. Our goal is to facilitate shared development of reusable models, to accommodate heterogeneous models of computation, and to provide a framework for interfacing multiple heterogeneous models. The framework provides an application programming interface for interfacing dynamic models defined over spatial domains. It is specifically designed to be independent of the specifics of the modeling methods used, and therefore facilitates seamless integration of heterogeneous models and processes. Furthermore, each model has separate geometries for visualization, simulation, and interfacing, allowing the model developer to choose the most natural geometric representation for each case. Input/output interfaces for visualization and haptics for real-time interactive applications have also been provided.

A stochastic model for heterogeneous computing and its application in data relocation scheme development

In a dedicated, mixed-machine, heterogeneous computing (HC) system, an application program may be decomposed into subtasks, then each subtask assigned to the machine where it is best suited for execution. Data relocation is defined as selecting the sources for needed data items. It is assumed that multiple independent subtasks of an application program can be executed concurrently on different machines whenever possible. A theoretical stochastic model for HC Is proposed, in which the computation times of subtasks and communication times for intermachine data transfers can be random variables. The optimization problem for finding the optimal matching, scheduling, and data relocation schemes to minimize the total execution time of an application program is defined based on this stochastic HC model. The global optimization criterion and search space for the above optimization problem are described. It is validated that a greedy algorithm-based approach can establish a local optimization criterion for developing data relocation heuristics. The validation is provided by a theoretical proof based on a set of common assumptions about the underlying HC system and application program. The local optimization criterion established by the greedy approach, coupled with the search space defined for choosing valid data relocation schemes, can help developers of future practical data relocation heuristics.