How energy efficient is In-memory computing?

In-memory computing is the best approach to boost energy efficiency and performance in computer systems today.
Published: Fri 14 Jun 2019
A blog entry by Edward Huskin

Contributed by:

Edward Huskin
Big Data Expert
Independent

Edward Huskin's Blog

Computing systems today are mostly running on efficient Artificial Intelligence algorithms which are based on the von Neumann architecture. When it comes to building computer systems with sufficient memory and processing speed, the rising In-memory computing architecture is the best approach which is implemented in order to boost energy efficiency and performance of the systems.

Basically, In-memory computing is the storage of information in RAM of dedicated services and is basically used for detecting patterns, analyzing massive data volumes and perform quick operations with the database. Furthermore, the technology is mostly used to maintain a competitive edge and meet the demands for optimal customer experience. In-Memory computing not only gives benefits to the organization, but also ensures safe and efficient operation for customers as well.

Why do we use In-memory computing?

Business and enterprise level systems are supposed to be energy efficient and deliver high performance. As they have to cope with the constant upsurge of data and increasing customer demands, In-memory computing is the best way to analyze and maintain data handling. In the last few years, tech companies and engineers are designing algorithms that can take advantage of in-memory computing and efficiently solve AI related tasks by the support of suitable hardware.

Moreover, there is a huge requirement for energy efficient non-von Neumann computing systems that are suitable for highly data-centric artificial intelligence related applications. In-memory computing is not only used to enhance data handling capabilities of a system, but the technology is also an energy efficient approach making it best suitable for data centers and enterprise level systems.

State dynamics and physical attributes of memory devices are made to perform certain computational tasks with the help of In-memory computing. In order to maintain the standard of operation, systems must be equipped with the latest hardware so that companies and businesses are able to handle their routine operations without any hassle. The technology can be used to manage data up to 10 times faster as compared to the speed of conventional Big Data and data warehousing practices.

Advantages of In-Memory Computing

In-Memory computing is a great solution for enterprise level data centers and systems. As it delivers better performance and power to complete millions of operations in a fraction of a second, the technology is designed to deliver outstanding benefits to enterprises in real time. Starting with the key advantage of using In-Memory computing, companies can easily understand customer expectations and demands in order to give their services in a better way.

Furthermore, the technology can be used to adjust pricing on the move to boost sales and create responsive supply chains as well. Along with these benefits, you can also identify and sort out production line problems and create an effective solution to continue the operations in a better way. The traditional analytic tools and big data technologies are limited by the speed and storage capacities which can result in future problems for companies and businesses.

Most of the today’s technologies would not be able to achieve best energy efficiency rate without the integration of In-Memory computing. When working with In-Memory data stores, the system can handle mixed workloads within the same architecture instead of separating transactional databases from analytics databases. This approach can also result in disk bottlenecks and disk I/O problems when used for multiple transactions at a single time.

Energy efficiency and performance

In-Memory computing supports high ingestion rates and is implemented to achieve lower latency for reports and queries. When it comes to energy efficiency, the technology can handle peak events and reduce infrastructure costs when scaling which is all possible due to its high efficiency and great performance. Moreover, it can also run real ML modes on both historical and transactional data to ensure enhanced distributed cloud edge efficiency.
By optimizing transactional processing and machine learning, In-Memory computing can be implemented for running real-time analytics and machine learning models for streaming data without any kind of interruption. Furthermore, companies can avail enhanced business operations and regulatory compliance to address time sensitive decisions.

In-Memory computing has the capability to handle large volumes of data more rapidly and efficiently as compared to the conventional CPM and BI systems. By giving reliable planning, decision making and fasted results, these systems can maintain massive data volumes that are required to perform accurate planning and business forecasting for companies.

Effectiveness and reliability

Over the past few decades, technology has made a huge impact on business operations and work procedure of companies. Relational technology, data warehousing and big data have enabled businesses to ensure smooth operation and easy handling of millions of transactions for each day. To get best benefit out of In-Memory computing, organizations need to upgrade their IT architecture and take advantage of the low latency processing.
No matter what the company size is, In-Memory computing is considered as the best way to deliver high performance and ensure increased availability of high-performance multi-core processors. This in return reduces operational costs and delivers greater efficiency as compared to the systems that are working on traditional data handling technologies.

There are various reviews about the performance and efficiency of In-Memory computing. As there are different types of In-Memory technologies, you have to learn their strengths and weaknesses so that you can select the best suitable option for your business needs. This technology can be used at an analytic server, database management system and is also suitable for client or server level data handling.

Conclusion

In-Memory computing is a super-fast, reliable and efficient technology for real time data handling, transactions and processing. When designing enterprise level data centers and systems, there are several important aspects that need to be focused including performance, reliability, security and efficiency.

As there are millions of transactions that need to be processed within fraction of a second, In-Memory computing is the ultimate data handling approach that is designed to deliver matchless performance. Businesses today need systems that deliver deeper real-time insight, greater scalability and faster performance which is all possible due to the high end In-Memory technologies.