Definition of Computational Storage
Computational storage refers to an emerging technology that integrates processing power directly into storage devices, allowing data processing tasks to be performed within the storage unit itself. This approach reduces data movement, increases efficiency, and improves overall system performance. It is particularly beneficial for applications that require extensive data analysis, such as artificial intelligence, big data analytics, and the Internet of Things.
The phonetic pronunciation of the keyword “Computational Storage” is:kəm-ˌpyü-tə-ˈshə-nəl stôr-ij
- Computational Storage enhances overall system performance by offloading data-intensive processing tasks from the main CPU to specialized storage devices, thus reducing data movement and latency.
- It enables scalable, parallel processing of data in storage devices which minimizes the need for data to be transferred back and forth between storage and computation resources, making it ideal for big data, AI, and IoT applications.
- By integrating compute and storage capabilities, Computational Storage can improve energy efficiency and reduce data center footprint, leading to cost and space savings while addressing storage performance challenges.
Importance of Computational Storage
Computational storage is an important term in technology as it represents a paradigm shift in the way data storage and processing tasks are approached.
By integrating processing capabilities directly within the storage device, computational storage greatly improves performance, reduces data movement, and minimizes latency issues associated with traditional storage systems.
This type of system architecture effectively addresses the massive growth in data volume, variety, and velocity, enabling organizations to analyze and derive insights from data more efficiently.
Ultimately, this enhanced efficiency leads to faster real-time analytics, improved decision making, and better overall outcomes for businesses and consumers alike.
Computational storage is an innovative technology primarily designed to address the increasing demands for efficient data processing within modern storage systems. As data generation and storage requirements grow dramatically across various sectors such as artificial intelligence, IoT, big data analytics, and cloud computing, the necessity to optimize data processing has driven the implementation of computational storage which aims to reduce latency, improve throughput, and minimize the burden on central processing units (CPU). By integrating computing resources within storage devices or across distributed storage systems, computational storage enables simultaneous storage and processing of data, essentially bringing the computation closer to the stored data and thereby reducing the time spent on moving data to and from CPUs.
The purpose of computational storage is to resolve performance bottlenecks and inefficiencies that arise from traditional storage systems. In these architectures, the CPU typically carries the responsibility of processing large volumes of data, which can lead to congestion and longer processing times.
By offloading these tasks to computational storage units, the CPU can focus on other critical computing functions, increasing overall system performance. Additionally, the technology fosters better resource utilization, reducing energy consumption, and providing scalability for different applications.
As a result, industries that demand real-time analytics, high-performance computing, and data-intensive functions benefit greatly from the implementation of computational storage systems, which have the potential to accelerate data processing and analysis, streamline operations, and enhance business decision making.
Examples of Computational Storage
Samsung’s SmartSSD: Samsung’s SmartSSD is a computational storage drive that combines high-performance Solid State Drives (SSDs) with an embedded Field-Programmable Gate Array (FPGA) to provide in-storage data processing capabilities. It is designed to accelerate data processing in large-scale data center applications such as big data analytics, artificial intelligence, and machine learning. By offloading data processing tasks to the SmartSSD, computing resources can be freed up for other tasks, thereby increasing overall system efficiency and performance.
NGD Systems’ Newport Platform: NGD Systems offers a computational storage product called the Newport platform, which features an NVMe SSD with an integrated Arm-based processor. This architecture enables a wide range of processing tasks, including data compression, encryption, and search, to be performed directly within the drive. The Newport platform allows users to save on both data center resources and power consumption by offloading processing tasks from the host CPUs to the drives.
ScaleFlux’s Computational Storage Subsystem (CSS): ScaleFlux is a leader in the development of computational storage solutions, and their CSS is specifically designed to enable data-driven applications to perform rapidly while minimizing infrastructure costs and complexity. The CSS integrates flash storage with transparent and scalable compute acceleration capabilities, which enables data processing tasks like database acceleration, real-time analytics, and content delivery optimization to be performed directly within the storage subsystem. This approach reduces data movement and improves overall system efficiency, resulting in significant performance and power savings for data centers.
Computational Storage FAQ
What is computational storage?
Computational storage refers to a class of storage devices that combine data storage with computing resources, enabling data processing operations to be performed directly on the storage device. This technology aims to reduce data movement and improve overall system performance, security, and efficiency.
Why is computational storage important?
Computational storage is important because it addresses the growing challenges of processing large and complex volumes of data, often referred to as big data. By integrating computing capabilities into storage devices, computational storage overcomes performance bottlenecks, reduces latency, and increases processing efficiency, ultimately enabling organizations to more effectively manage and process their data.
What are the main types of computational storage solutions?
The main types of computational storage solutions are:
- In-storage processing (ISP): This solution provides computational capabilities directly on the storage device. ISP allows data processing tasks to be outsourced from the main processor to the storage device, thereby reducing data movement.
- Computational RAM (C-RAM): This technology integrates processing capabilities into memory devices, enabling efficient data processing in real-time.
- Intelligent storage platforms (ISP): These platforms incorporate machine learning and artificial intelligence algorithms to analyze and categorize data more efficiently, without affecting storage system performance.
What are the key benefits of computational storage?
The key benefits of computational storage include:
- Improved performance: By reducing data movement and enabling parallel processing, computational storage can significantly improve system performance.
- Reduced latency: Computational storage enables faster data processing due to its capability to process data directly within the storage device.
- Enhanced security: By reducing data movement and processing data in-place, computational storage can help prevent unauthorized access to sensitive data.
- Reduced energy consumption: By offloading data processing tasks from the main processor, computational storage can reduce overall energy consumption and contribute to more energy-efficient systems.
What industries can benefit from computational storage technology?
Computational storage technology is beneficial to a wide range of industries that face challenges in managing and processing large volumes of data. Some of these industries include:
- Data centers and cloud service providers
- Internet of Things (IoT) and edge computing markets
- Big data analytics and machine learning applications
- Healthcare and genomics research
- Financial services and fraud detection
- Video processing and streaming
Related Technology Terms
- Data Processing
- Edge Computing
- In-storage Computing
- Storage-class Memory
- Distributed Storage Systems