Virtual Machine Rescheduling (VMR) plays a crucial role in maintaining service quality and resource efficiency in elastic cloud computing. However, existing datasets and benchmarks primarily focus on VM scheduling tasks, while lacking industrial-scale datasets and standardized evaluation for the more complex and crucial rescheduling problems. To address these challenges, we present IVMR suite, the first industrial-scale suite for VMR research comprising two core components: 1) IVMR-D, an industrial-grade VMR dataset mined from a real cloud data center, integrating complete resource specifications and complex operation constraints. The dataset is systematically structured based on data size and optimization objectives. 2) IVMR-B, a benchmark for the VMR problem that establishes seamless integration of consistent evaluation and the provision of baselines spanning optimization, metaheuristic, heuristic, and machine learning-based methodologies. Our comprehensive experimental evaluation demonstrates that all tested VMR algorithms struggle to effectively balance solution quality with computational efficiency while showing limited scalability across tasks with varying complexity levels. These findings emphasize the urgency of improving VMR algorithms for industrial deployments.

In order to address challenges related to the absence of visual modality and coarse-grained semantic inconsistency in multi-modal entity alignment, we present a novel framework named Context-based Image Generation and Fine-Grained Semantic Structure Embedding (CIGFSE). First, CIGFSE leverages the textual context of entities along with large language models to generate prompts for entities lacking images, which are then input into image generation models to produce auxiliary images enriching the multi-modal knowledge graph. Next, it captures structural information via semantic-augmented structure embedding and applies a feedforward neural network to the other modalities. Furthermore, CIGFSE adopts attention-guided modality fusion and contrastive learning to optimize the model. Extensive experiments on both monolingual and bilingual datasets demonstrate that CIGFSE achieves state-of-the-art performance in multi-modal entity alignment.

The GCD and Banerjee tests are the standard data dependence tests used to determine whether a loop may be parallelized/vectorized. In an earlier work, (1991) the authors presented a new data dependence test, the I test, which extends the accuracy of the GCD and the Banerjee tests. In the original presentation, only the case of general dependence was considered, i.e., the case of dependence with a direction vector of the form (*,*,...,*). In the present work, the authors generalize the I test to check for data dependence subject to an arbitrary direction vector.>

Article Free Access Share on Extending the I test to direction vectors Authors: Kleanthis Psarris Department of Electrical Engineering and Computer Science, Stevens Institute of Technology, Hoboken, New Jersey Department of Electrical Engineering and Computer Science, Stevens Institute of Technology, Hoboken, New JerseyView Profile , Xiangyun Kong Department of Electrical Engineering and Computer Science, Stevens Institute of Technology, Hoboken, New Jersey Department of Electrical Engineering and Computer Science, Stevens Institute of Technology, Hoboken, New JerseyView Profile , David Klappholz Department of Electrical Engineering and Computer Science, Stevens Institute of Technology, Hoboken, New Jersey Department of Electrical Engineering and Computer Science, Stevens Institute of Technology, Hoboken, New JerseyView Profile Authors Info & Claims ICS '91: Proceedings of the 5th international conference on SupercomputingJune 1991 Pages 330–340https://doi.org/10.1145/109025.109106Online:01 June 1991Publication History 1citation190DownloadsMetricsTotal Citations1Total Downloads190Last 12 Months3Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

The I test is a subscript dependence test which extends both the range of applicability and the accuracy of the GCD and Banerjee tests (U. Banerjee, 1976), standard subscript dependence tests used to determine whether loops may be parallelized/vectorized. It is shown that the I test is useful when, in the event that a positive result must be reported, a definitive positive is of more use than a tentative positive and when insufficient loop iterations are known for the Banerjee test to apply.>

The Banerjee test is commonly considered to be the more accurate of the two major approximate data dependence tests used in automatic vectorization/parallelization of loops, the other being the GCD test. From its derivation, however, there is no simple explanation of why the Banerjee test should be nearly as accurate as it is given credit for. We present a set of sufficient conditions for the Banerjee test's accuracy, and explain its perceived accuracy in actual practice by proving that under circumstances which occur extremely frequently in actual code, the Banerjee test is, in fact, not approximate, but perfectly accurate.

Refined Languages (Refined Fortran, Refined C, etc.) are extensions of their parent languages in which it is possible to express parallelism, but impossible to create races or deadlocks. Where strictly deterministic behavior is desired, multiple executions of a Refined Fortran program with the same input data can be guaranteed to either compute the same results or terminate with the same run-time errors regardless of differences in scheduling. Where asynchronous behavior is desired, freedom from races can be guaranteed. The Refined Languages approach achieves its goal by extending sequential imperative programming languages with data- (rather than control-) oriented constructs, and by viewing the expression of parallelism in data- (rather than control-) oriented terms. Earlier versions of Refined Fortran are discussed in [1]-[2]; the present work supersedes and extends work reported in these earlier publications.