Topic
Data access
About: Data access is a research topic. Over the lifetime, 13141 publications have been published within this topic receiving 172859 citations. The topic is also known as: Data access.
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TL;DR: GraphH, a PIM architecture for graph processing on the hybrid memory cube array, is proposed to tackle all four problems mentioned above, including random access pattern causing local bandwidth degradation, poor locality leading to unpredictable global data access, heavy conflicts on updating the same vertex, and unbalanced workloads across processing units.
Abstract: Large-scale graph processing requires the high bandwidth of data access. However, as graph computing continues to scale, it becomes increasingly challenging to achieve a high bandwidth on generic computing architectures. The primary reasons include: the random access pattern causing local bandwidth degradation, the poor locality leading to unpredictable global data access, heavy conflicts on updating the same vertex, and unbalanced workloads across processing units. Processing-in-memory (PIM) has been explored as a promising solution to providing high bandwidth, yet open questions of graph processing on PIM devices remain in: 1) how to design hardware specializations and the interconnection scheme to fully utilize bandwidth of PIM devices and ensure locality and 2) how to allocate data and schedule processing flow to avoid conflicts and balance workloads. In this paper, we propose GraphH, a PIM architecture for graph processing on the hybrid memory cube array, to tackle all four problems mentioned above. From the architecture perspective, we integrate SRAM-based on-chip vertex buffers to eliminate local bandwidth degradation. We also introduce reconfigurable double-mesh connection to provide high global bandwidth. From the algorithm perspective, partitioning and scheduling methods like index mapping interval-block and round interval pair are introduced to GraphH, thus workloads are balanced and conflicts are avoided. Two optimization methods are further introduced to reduce synchronization overhead and reuse on-chip data. The experimental results on graphs with billions of edges demonstrate that GraphH outperforms DDR-based graph processing systems by up to two orders of magnitude and $5.12 {\times }$ speedup against the previous PIM design.
135 citations
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14 Aug 1997
TL;DR: MineSet supports the knowledge discovery process from data access and preparation through iterative analysis and visualization to deployment, and third party vendors can interface to the MineSet tools for model deployment and for integration with other packages.
Abstract: MineSet™, Silicon Graphics' interactive system for data mining, integrates three powerful technologies: database access, analytical data mining, and data visualization. It supports the knowledge discovery process from data access and preparation through iterative analysis and visualization to deployment. Mine-Set is based on a client-server architecture that scales to large databases. The database access component provides a rich set of operators that can be used to preprocess and transform the stored data into forms appropriate for visualization and analytical mining. The 3D visualization capabilities allow direct data visualization for exploratory analysis, including tools for displaying high-dimensional data containing geographical and hierarchical information. The analytical mining algorithms help identify potentially interesting models of the data, which can be viewed using visualization tools specialized for the learned models. Third party vendors can interface to the MineSet tools for model deployment and for integration with other packages.
135 citations
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TL;DR: A data management method for digital twin of product based on blockchain technology is proposed and the results show that the proposed method can solve the abovementioned data management problems simultaneously.
135 citations
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01 May 2005TL;DR: Two dynamic replication algorithms, Simple Bottom-Up (SBU) and Aggregate Bottom- up (ABU) are proposed for the multi-tier Data Grid and comparing the two algorithms to Fast Spread dynamic replication strategy, ABU proves to be superior.
Abstract: Data replication is a common method used to improve the performance of data access in distributed systems. In this paper, two dynamic replication algorithms, Simple Bottom-Up (SBU) and Aggregate Bottom-Up (ABU), are proposed for the multi-tier Data Grid. A multi-tier Data Grid simulator called DRepSim is developed for studying the performances of the dynamic replication algorithms. The simulation results show that both algorithms can reduce the average response time of data access greatly compared to the static replication method. ABU can achieve great performance improvements for all access patterns even if the available storage size of the replication server is very small. Comparing the two algorithms to Fast Spread dynamic replication strategy, ABU proves to be superior. As for SBU, although the average response time of Fast Spread is better in most cases, Fast Spread's replication frequency is too high to be applicable in the real world.
134 citations
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TL;DR: This paper surveys performance models for distributed and replicated database systems and selects a combination of these proven modeling concepts and gives an example of how to compose a balanced analytical model of a replicated database.
Abstract: The paper surveys performance models for distributed and replicated database systems. Over the last 20 years (1980-2000), a variety of such performance models have been developed and they differ in: (1) which aspects of a real system are or are not captured in the model (e.g., replication, communication, nonuniform data access, etc.); and (2) how these aspects are modeled. We classify the different alternatives and modeling assumptions and discuss their interdependencies and expressiveness for the representation of distributed databases. This leads to a set of building blocks for analytical performance models. To illustrate the work that is surveyed, we select a combination of these proven modeling concepts and give an example of how to compose a balanced analytical model of a replicated database. We use this example to show how to derive meaningful performance values and to discuss the applicability and expressiveness of performance models for distributed and replicated databases. Finally, we compare the analytical results to measurements in a distributed database system.
133 citations