Tobias Vinçon, Arthur Bernhardt, Lukas Weber, A. Koch, Ilia Petrov
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引用次数: 6
Abstract
Massive data transfers in modern data-intensive systems resulting from low data-locality and data-to-code system design hurt their performance and scalability. Near-Data processing (NDP) and a shift to code-to-data designs may represent a viable solution as packaging combinations of storage and compute elements on the same device has become feasible. The shift towards NDP system architectures calls for revision of established principles. Abstractions such as data formats and layouts typically spread multiple layers in traditional DBMS, the way they are processed is encapsulated within these layers of abstraction. The NDP-style processing requires an explicit definition of cross-layer data formats and accessors to ensure in-situ executions optimally utilizing the properties of the underlying NDP storage and compute elements. In this paper, we make the case for such data format definitions and investigate the performance benefits under RocksDB and the COSMOS hardware platform.
期刊介绍:
Distributed and Parallel Databases publishes papers in all the traditional as well as most emerging areas of database research, including:
Availability and reliability;
Benchmarking and performance evaluation, and tuning;
Big Data Storage and Processing;
Cloud Computing and Database-as-a-Service;
Crowdsourcing;
Data curation, annotation and provenance;
Data integration, metadata Management, and interoperability;
Data models, semantics, query languages;
Data mining and knowledge discovery;
Data privacy, security, trust;
Data provenance, workflows, Scientific Data Management;
Data visualization and interactive data exploration;
Data warehousing, OLAP, Analytics;
Graph data management, RDF, social networks;
Information Extraction and Data Cleaning;
Middleware and Workflow Management;
Modern Hardware and In-Memory Database Systems;
Query Processing and Optimization;
Semantic Web and open data;
Social Networks;
Storage, indexing, and physical database design;
Streams, sensor networks, and complex event processing;
Strings, Texts, and Keyword Search;
Spatial, temporal, and spatio-temporal databases;
Transaction processing;
Uncertain, probabilistic, and approximate databases.