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