18382767. NDP-SERVER: A DATA-CENTRIC COMPUTING ARCHITECTURE BASED ON STORAGE SERVER IN DATA CENTER simplified abstract (SAMSUNG ELECTRONICS CO., LTD.)

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NDP-SERVER: A DATA-CENTRIC COMPUTING ARCHITECTURE BASED ON STORAGE SERVER IN DATA CENTER

Organization Name

SAMSUNG ELECTRONICS CO., LTD.

Inventor(s)

Xiaojia Song of San Jose CA (US)

Stephen Garry Fischer of San Jose CA (US)

NDP-SERVER: A DATA-CENTRIC COMPUTING ARCHITECTURE BASED ON STORAGE SERVER IN DATA CENTER - A simplified explanation of the abstract

This abstract first appeared for US patent application 18382767 titled 'NDP-SERVER: A DATA-CENTRIC COMPUTING ARCHITECTURE BASED ON STORAGE SERVER IN DATA CENTER

Simplified Explanation

The abstract describes a server system that includes multiple mass-storage devices, a central processing unit (CPU), and at least one near data processing (NDP) engine. The CPU is connected to the first set of mass-storage devices, such as solid-state drive (SSD) devices, while the NDP engine is associated with a second set of mass-storage devices and placed between the CPU and the second set of mass-storage devices. The number of NDP engines is determined based on the available bandwidth associated with the CPU, network, communication fabric, and all NDP engines.

  • The server system includes multiple mass-storage devices, a CPU, and at least one NDP engine.
  • The CPU is connected to the first set of mass-storage devices, while the NDP engine is associated with the second set of mass-storage devices.
  • The second set of mass-storage devices is equal to or less than the first set of mass-storage devices.
  • The number of NDP engines is determined based on available bandwidth.
  • The available bandwidth includes CPU bandwidth, network bandwidth, communication fabric bandwidth, and NDP engine bandwidth.
  • The number of NDP engines is calculated by dividing the total bandwidth associated with all NDP engines by the bandwidth associated with a single NDP engine.

Potential applications of this technology:

  • Data centers and server farms can benefit from this technology to improve storage and processing capabilities.
  • High-performance computing systems can utilize this technology to enhance data processing efficiency.
  • Cloud computing providers can implement this technology to optimize resource allocation and improve overall system performance.

Problems solved by this technology:

  • The technology addresses the challenge of efficiently managing and processing large amounts of data in server systems.
  • It helps overcome bottlenecks in data processing and storage by distributing the workload across multiple NDP engines and mass-storage devices.
  • The technology improves overall system performance and reduces latency by optimizing the utilization of available bandwidth.

Benefits of this technology:

  • Enhanced data processing capabilities and improved system performance.
  • Efficient utilization of available bandwidth resources.
  • Scalability and flexibility in expanding storage and processing capabilities.
  • Reduced latency and improved response times for data-intensive applications.


Original Abstract Submitted

A server system includes a first plurality of mass-storage devices, a central processing unit (CPU), and at least one near data processing (NDP) engine. The CPU is coupled to the first plurality of the mass-storage devices, such as solid-state drive (SSD) devices, and the at least one NDP engine is associated with a second plurality of the mass-storage devices and interposed between the CPU and the second plurality of the mass-storage devices associated with the NDP engine. The second plurality of the mass-storage devices is less than or equal to the first plurality of the mass-storage devices. A number of NDP engines may be based on a minimum bandwidth of a bandwidth associated with the CPU, a bandwidth associated with a network, a bandwidth associated with the communication fabric and a bandwidth associated with all NDP engines divided by a bandwidth associated with a single NDP engine.

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