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About 10,000 papers for “model execution”

Model execution times.

Xiangying Miao (6091277), Hongli Miao (6091280), Yongjun Jia (6091283) et al. · Figshare · 2018

Cited by 0Open access

CUDA execution model.

Andrea Manconi (566020), Alessandro Orro (28770), Emanuele Manca (566021) et al. · Figshare · 2015

<p>Threads are grouped in blocks in a grid. Each thread has a private memory and runs in parallel with the others in the same block.</p>

Cited by 0Open access

OpenCL execution model.

Yupu Song (12501028), Cailin Li (17743113), Shiyang Xiao (17743116) et al. · Figshare · 2024

<div><p>In the process of Canny edge detection, a large number of high complexity calculations such as Gaussian filtering, gradient calculation, non-maximum suppression, and double threshold judgment need to be performed on the image, which takes up a lot of operation time, which is a great challenge to the real-time requirements of the algorithm. The traditional Canny edge detection technology mainly uses customized equipment such as DSP and FPGA, but it has some problems, such as long development cycle, difficult debugging, resource consumption, and so on. At the same time, the adopted CUDA platform has the problem of poor cross-platform. In order to solve this problem, a fine-grained parallel Canny edge detection method is proposed, which is optimized from three aspects: task partition, vector memory access, and NDRange optimization, and CPU-GPU collaborative parallelism is realized. At the same time, the parallel Canny edge detection methods based on multi-core CPU and CUDA architecture are designed. The experimental results show that OpenCL accelerated Canny edge detection algorithm (OCL_Canny) achieves 20.68 times acceleration ratio compared with CPU serial algorithm at 7452 × 8024 image resolution. At the image resolution of 3500 × 3500, the OCL_Canny algorithm achieves 3.96 times the acceleration ratio compared with the CPU multi-threaded Canny parallel algorithm. At 1024 × 1024 image resolution, the OCL_Canny algorithm achieves 1.21 times the acceleration ratio compared with the CUDA-based Canny parallel algorithm. The effectiveness and performance portability of the proposed Canny edge detection parallel algorithm are verified, and it provides a reference for the research of fast calculation of image big data.</p></div>

Cited by 0Open access

Model execution adaptation?

Eric Cariou, Franck Barbier, Olivier Le Goaër · 2012

One of the main goals of model-driven engineering (MDE) is the manipulation of models as exclusive software artifacts. Model execution is in particular a means to substitute models for code. On another way, MDE is a promising discipline for building adaptable systems thanks to models at runtime. When the model is directly executed, the system becomes the model, then, this is the model that is adapted. In this paper, we investigate the adaptation of the model itself in the context of model execution. We present a first experimentation where we study the constraints on a model to be able to determine if it is consistent (that is, adapted) with an execution environment, possibly including fail-stop modes. Then, we state some perspectives and open issues about model execution adaptation.

Cited by 8

CUDA execution model.

Yongfei Wang (608480), Junping Wang (397708), Jiarui Tian (9081026) et al. · Figshare · 2024

<div><p>Early warning of geological hazards requires monitoring extreme weather conditions, such as heavy rainfall. Atmospheric circulation models are used for weather forecasting and climate simulation. As a critical physical process in atmospheric circulation models, the Zhang-McFarlane (ZM) deep convective physical parameterization scheme involves computationally intensive calculations that significantly impact the overall operational efficiency of the model. However, many of these calculations are independent and can be computed in parallel. Therefore, this paper proposes a GPU-based acceleration algorithm for the ZM scheme. Based on the computation characteristics of the ZM scheme, we propose its one-demensional and two-demensional acceleration algorithms based on GPU. These algorithms are implemented using CUDA C and compared against a single Kunpeng-920 (Dual Socket) CPU core and the OpenMP version on multi-core CPUs. In the absence of I/O transmission, the proposed algorithm achieves a speedup of 413.6×. Experimental results demonstrate the significant acceleration effect of the proposed algorithms and methods. It is of great significance for the development of deep convective parameterization schemes and their further generalization in climate models. Additionally, we propose a performance optimization method utilizing the CUDA streaming technology to improve data transmission efficiency between CPU and GPU. In the presence of I/O transmission, the proposed algorithm achieves a speedup of 350.1× on A100 GPU.</p></div>

Cited by 0Open access

SIMD execution model.

Jose-Ignacio Agulleiro (124327), José Jesús Fernández (124330) · Figshare · 2015

<p><i>Source 1</i> and <i>Source 2</i> are vector registers. Each one contains several data elements, which can be integer or real numbers. The same operation (<i>op</i>) is carried out between the two registers as indicated and the result is stored in another one (<i>Destination</i>).</p>

Cited by 0Open access

The Execution Model

June Johan Montelius · 2007

Contents 1 The execution model 1 2 The execution state 2 3 Workers 2 3.1 Tasks : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 3 3.2 Stacks : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 4 4 The Handlers 4 4.1 The goal handler : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 5 4.2 The guard handler : : : : : : : : : : : : : : : : : : : : : : : : : : : : 7 4.3 The choice handler : : : : : : : : : : : : : : : : : : : : : : : : : : : : 9 4.4 The fail handler : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 9 4.5 The split handler : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 11 4.6 The scheduler : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 13 5 Initial state 13 6 Properties of the Execution Model 14 6.1 Environment stacking :

Cited by 0

CLR Execution Model II

Mohammad Saidur Rahman · Apress eBooks · 2014

The CLR execution model contains different components for implementation. As you saw in Chapter 6, the assembly loader of the CLR is used to load the assembly, which contains the IL code and metadata for the application that is executing the CLR. The class loader component loads any type defined in the application assembly or external types that reside in other assemblies. When the types are laid out in the memory, the execution engine can execute the code of that assembly. But, as mentioned earlier, the IL code is not directly executable by the CPU but rather requires compiling into native code. The CLR component piece that does this is called the JIT compiler. In this chapter, you will learn about the JIT compiler.

Cited by 0

3The Sequential Execution Model

Mujahed Eleyat · 2014

• Define of a parallel programming model • Classify parallel programming models • Explain data parallelism and task parallelism • Explain several approaches for parallel programming • Present examples of parallel programming models

Cited by 0

An Execution Model 2

Jim Gray · 1988

Abstract: Distributed systems can be modeled as processes communicating via messages. This model abstracts the three degrees of distribution: shared memory, local network, and wide area network. Although these three forms of distribution are qualitatively the same, there are huge quantitative differences in their message transport costs and message transport reliability. This paper quantifies these differences for past, current, and future technologies.

Cited by 0

A DCI Execution Model

Trygve Reenskaug · 2011

Computers provide three essential services: Data transformation, data storage, and data communication. Transformation and storage have long been well entrenched in algorithmic and data languages. Unaccountably, communication is only seen as an I/O operation without explicit language support. At long last, this deficiency has now been remedied with the new Data- Context- Interaction (DCI) paradigm. Alan Kay introduced the notion of object orientation in the early seventies. He regarded an object as a virtual computer and an object system as thousands of computers hooked together by a very fast network. The locus of DCI is in the DCI Context where we find the specification of object system behavior. The Context declares a network of communicating objects where the nodes are called Roles and the edges Connectors. The system behavior algorithm is composed from RoleMethods; methods that are associated with the Roles. This article is about an execution model for DCI. An important aspect is the handling of DCI Contexts at runtime; where they are stored and how they are accessed. DCI in general and the DCI Context in particular make data communication a first class citizen of computer

Cited by 0

The EurValve model execution environment

Marian Bubak, Krzysztof Czechowicz, Tomasz Gubała et al. · Interface Focus · 2020

The goal of this paper is to present a dedicated high-performance computing (HPC) infrastructure which is used in the development of a so-called reduced-order model (ROM) for simulating the outcomes of interventional procedures which are contemplated in the treatment of valvular heart conditions. Following a brief introduction to the problem, the paper presents the design of a model execution environment, in which representative cases can be simulated and the parameters of the ROM fine-tuned to enable subsequent deployment of a decision support system without further need for HPC. The presentation of the system is followed by information concerning its use in processing specific patient cases in the context of the EurValve international collaboration.

Cited by 5Open access

Alpha Execution Model Of Physics

Cleary, Daniel J · Zenodo (CERN European Organization for Nuclear Research) · 2025

Execution Physics is a complete first-principles reconstruction of physical reality, authored by Daniel J. Cleary. Rather than relying on speculative fields or patched frameworks, this work begins with a single identity—0^0 = 1—and logically unfolds a universal structure from that self-referential truth. The model, known as Execution Physics, unifies relativity, quantum mechanics, gravity, and thermodynamics into a single coherent system. It introduces the concept of the universe as a step-by-step crystallization of discrete spatial frames through Planck-scale execution, governed by charge delay, relativistic skew, and the fine-structure constant alpha. This book: Derives mass, charge, and gravity from geometric execution delays Resolves the observer effect, entanglement, and uncertainty via perception lag Eliminates the need for dark matter, dark energy, and inflation Provides a causal explanation for the Cosmic Microwave Background Demonstrates that space and time are emergent from execution, not fundamental Identifies the fine-structure constant (alpha = 0.00729927...) as the geometric seed of reality Also included is a mathematical appendix deriving all fundamental constants from execution geometry, including a direct explanation for mass, charge, and temperature using only length and time. Author Daniel J. Cleary 2025 – Original Author and Architect of the Execution Framework

Cited by 0Open access