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Scalable data management for future hardware

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Scalable data management for future hardware/ edited by Kai-Uwe Sattler ... [et al.].
其他作者:	Sattler, Kai-Uwe.
出版者:	Cham :Springer Nature Switzerland : : 2025.,
面頁冊數:	xxi, 240 p. :ill. (chiefly color), digital ;24 cm.
內容註:	1. ADAMANT: Hardware-Accelerated Query Processing Made Easy -- 2. Query Processing on Heterogeneous Hardware -- 3. Efficient Event Processing on Modern Hardware -- 4. Hybrid Transactional/Analytical Graph Processing in Modern Memory Hierarchies -- 5. MxKernel: A Bare-Metal Runtime System for Database Operations on Heterogeneous Many-Core Hardware -- 6. Scaling beyond DRAM without Compromising Performance -- 7. ReProVide: Query Optimisation and Near-Data Processing on Reconfigurable SoCs for Big Data Analysis -- 8. Scalable Data Management on Next-Generation Data Center Networks -- 9. Managing Very Large Data Sets on Directly-Attached NVMe Arrays.
Contained By:	Springer Nature eBook
標題:	Computer engineering. -
電子資源:	https://doi.org/10.1007/978-3-031-74097-8
ISBN:	9783031740978

Scalable data management for future hardware
Scalable data management for future hardware[electronic resource] /edited by Kai-Uwe Sattler ... [et al.]. - Cham :Springer Nature Switzerland :2025. - xxi, 240 p. :ill. (chiefly color), digital ;24 cm.

1. ADAMANT: Hardware-Accelerated Query Processing Made Easy -- 2. Query Processing on Heterogeneous Hardware -- 3. Efficient Event Processing on Modern Hardware -- 4. Hybrid Transactional/Analytical Graph Processing in Modern Memory Hierarchies -- 5. MxKernel: A Bare-Metal Runtime System for Database Operations on Heterogeneous Many-Core Hardware -- 6. Scaling beyond DRAM without Compromising Performance -- 7. ReProVide: Query Optimisation and Near-Data Processing on Reconfigurable SoCs for Big Data Analysis -- 8. Scalable Data Management on Next-Generation Data Center Networks -- 9. Managing Very Large Data Sets on Directly-Attached NVMe Arrays.

Open access.

This open access book presents the results of the DFG priority program on Scalable Data Management for Future Hardware. It details requirements and solutions of how modern and future hardware architectures can be leveraged to address the challenges in modern data management. The nine chapters of the book present a wide range of data management architectures in conjunction with current hardware developments, often related to applications in data analytics or machine learning. They cover topics such as hardware-accelerated query or event processing on FPGA, GPU, and multicore CPUs, scalable data management in data center networks or on modern memory and storage technologies, and operating system support. This book provides researchers in academia and industry with a comprehensive combination of data management, operating systems, distributed systems and computer architecture issues necessary to address the requirements from practice as well as to propel innovative ideas and challenging research questions.

ISBN: 9783031740978

Standard No.: 10.1007/978-3-031-74097-8doiSubjects--Topical Terms:

621879
Computer engineering.

LC Class. No.: TK7885

Dewey Class. No.: 621.39

Scalable data management for future hardware
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505 0 $a 1. ADAMANT: Hardware-Accelerated Query Processing Made Easy -- 2. Query Processing on Heterogeneous Hardware -- 3. Efficient Event Processing on Modern Hardware -- 4. Hybrid Transactional/Analytical Graph Processing in Modern Memory Hierarchies -- 5. MxKernel: A Bare-Metal Runtime System for Database Operations on Heterogeneous Many-Core Hardware -- 6. Scaling beyond DRAM without Compromising Performance -- 7. ReProVide: Query Optimisation and Near-Data Processing on Reconfigurable SoCs for Big Data Analysis -- 8. Scalable Data Management on Next-Generation Data Center Networks -- 9. Managing Very Large Data Sets on Directly-Attached NVMe Arrays.
506 $a Open access.
520 $a This open access book presents the results of the DFG priority program on Scalable Data Management for Future Hardware. It details requirements and solutions of how modern and future hardware architectures can be leveraged to address the challenges in modern data management. The nine chapters of the book present a wide range of data management architectures in conjunction with current hardware developments, often related to applications in data analytics or machine learning. They cover topics such as hardware-accelerated query or event processing on FPGA, GPU, and multicore CPUs, scalable data management in data center networks or on modern memory and storage technologies, and operating system support. This book provides researchers in academia and industry with a comprehensive combination of data management, operating systems, distributed systems and computer architecture issues necessary to address the requirements from practice as well as to propel innovative ideas and challenging research questions.
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