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{{Short description|Processing data technology}}
{{advert|date=November 2018}}
The term is used for two different things:
# In [[computer science]], '''in-memory processing''', also called '''compute-in-memory''' (CIM), or '''processing-in-memory''' (PIM), is a [[computer architecture]] in which data operations are available directly on the data memory, rather than having to be transferred to [[Central processing unit|CPU]] registers first.<ref>{{Cite journal |last=Ghose |first=S. |date=November 2019 |title=Processing-in-memory: A workload-driven perspective |url=https://www.pdl.cmu.edu/PDL-FTP/associated/19ibmjrd_pim.pdf |journal=IBM Journal of Research and Development |volume=63 |issue=6 |pages=3:1–19|doi=10.1147/JRD.2019.2934048 |s2cid=202025511 }}</ref> This may improve the [[Electric power|power usage]] and [[Computer performance|performance]] of moving data between the processor and the main memory.
# In [[software engineering]], '''in-memory processing''' is a [[software architecture]] where a database is kept entirely in [[random-access memory]] (RAM) or [[flash memory]] so that usual accesses, in particular read or query operations, do not require access to [[disk storage]].<ref>{{cite journal|first=Hao|last=Zhang|author2=Gang Chen|author3=Beng Chin Ooi|author4=Kian-Lee Tan|author5=Meihui Zhang|title=In-Memory Big Data Management and Processing: A Survey|journal=IEEE Transactions on Knowledge and Data Engineering|date=July 2015|volume=27|issue=7|pages=1920–1948|doi=10.1109/TKDE.2015.2427795|bibcode=2015ITKDE..27.1920Z |doi-access=free}}</ref> This may allow faster data operations such as "joins", and faster reporting and decision-making in business.<ref>{{cite book|last1=Plattner|first1=Hasso|last2=Zeier|first2=Alexander|title=In-Memory Data Management: Technology and Applications|date=2012|publisher=Springer Science & Business Media|isbn=9783642295744|url=https://books.google.com/books?id=HySCgzCApsEC&q=%22in-memory%22|language=en}}</ref>
Extremely large datasets may be divided between co-operating systems as in-memory [[data grid]]s.
Though SQL is a very powerful tool running complex queries took very long time to execute and often resulted in bringing down transactional processing. To improve query performance multidimensional databases or cubes also called multidimensional online analytical processing (MOLAP) were formed. Designing a cube design involved an elaborate and lengthy process which took a significant amount of time from IT staff. Changing the cubes structure to adapt to dynamically changing business needs was cumbersome. Cubes are pre populated with data to answer specific queries and although it increased performance it still failed to answer ad hoc queries.<ref>{{cite journal|last=Gill|first=John|title=Shifting the BI Paradigm with In-Memory Database Technologies|journal=Business Intelligence Journal|year=2007|month=Second Quarter|volume=12|issue=2|pages=58–62|url=http://www.highbeam.com/doc/1P3-1636785121.html}}</ref>▼
==Hardware (PIM)==
PIM could be implemented by:<ref>{{cite web |title=Processing-in-Memory Course: Lecture 1: Exploring the PIM Paradigm for Future Systems - Spring 2022 | website=[[YouTube]] | date=10 March 2022 |url=https://www.youtube.com/watch?v=R-sEqnOmDT4 |language=en}}</ref>
Traditional BI tools couldn’t keep up with the ever growing BI requirements and were unable to deliver real time data for end users.<ref>{{cite web|title=In_memory Analytics|url=http://www.yellowfinbi.com/Document.i4?DocumentId=104879|publisher=yellowfin|pages=6}}</ref>▼
* Processing-using-Memory (PuM)
** Adding limited processing capability (e.g., floating point multiplication units, 4K row operations such as copy or zero, bitwise operations on two rows) to conventional memory modules (e.g., DIMM modules); or
** Adding processing capability to memory controllers so that the data that is accessed does not need to be forwarded to the CPU or affect the CPU' cache, but is dealt with immediately.
* Processing-near-Memory (PnM)
Most in-memory tools use compression algorithms which reduce the size of in-memory data than what would be needed for hard disks. Users query the data loaded into the system’s memory thereby avoiding slower database access and performance bottlenecks. This is different from caching, a very widely used method to speed up query performance, in that caches are subsets of very specific pre-defined organized data. With in-memory tools, data available for analysis can be as large as data mart or small data warehouse which is entirely in memory. This can be accessed within seconds by multiple concurrent users at a detailed level and offers the potential for excellent analytics. Theoretically the improvement in data access is 10,000 to 1,000,000 times faster than from disk. It also minimizes the need for performance tuning by IT staff and provides faster service for end users.▼
** New 3D arrangements of silicon with memory layers and processing layers.
=== Application of in-memory technology in everyday life ===
In-memory processing techniques are frequently used by modern smartphones and tablets to improve application performance. This can result in speedier app loading times and more enjoyable user experiences.
* In-memory processing may be used by gaming consoles such as the [[PlayStation]] and [[Xbox]] to improve game speed.<ref>{{Cite web |last=Park |first=Kate |date=2023-07-27 |title=Samsung extends cut in memory chip production, will focus on high-end AI chips instead |url=https://techcrunch.com/2023/07/27/samsung-extends-cut-in-memory-chip-production-will-focus-on-high-end-ai-chips-instead/ |access-date=2023-12-05 |website=TechCrunch |language=en-US}}</ref>{{Failed verification|date=July 2024}} Rapid data access is critical for providing a smooth game experience.
* Certain wearable devices, like smartwatches and fitness trackers, may incorporate in-memory processing to swiftly process sensor data and provide real-time feedback to users. Several commonplace gadgets use in-memory processing to improve performance and responsiveness.<ref>{{Cite journal |last1=Tan |first1=Kian-Lee |last2=Cai |first2=Qingchao |last3=Ooi |first3=Beng Chin |last4=Wong |first4=Weng-Fai |last5=Yao |first5=Chang |last6=Zhang |first6=Hao |date=2015-08-12 |title=In-memory Databases: Challenges and Opportunities From Software and Hardware Perspectives |url=https://doi.org/10.1145/2814710.2814717 |journal=ACM SIGMOD Record |volume=44 |issue=2 |pages=35–40 |doi=10.1145/2814710.2814717 |s2cid=14238437 |issn=0163-5808|url-access=subscription }}</ref>
64-bits operating system: Though the idea of In-memory technology is not new, it is only recently emerging thanks to the widely popular and affordable 64-bit processors and declining memory chips prices. [[64 bit]] operating systems allows access to far more RAM (up to 100GB or more) than the 2 or 4 GB accessible on 32-bit systems. By providing Terabytes (1 TB = 1,024 GB) of space available for storage and analysis, 64-bit operating systems make in-memory processing scalable.▼
* In-memory processing is used by smart TVs to enhance interface navigation and content delivery. It is used in digital cameras for real-time image processing, filtering, and effects.<ref>{{Cite book |doi=10.1109/ISCAS48785.2022.9937475 |s2cid=253462291 |chapter=Approximate In-Memory Computing using Memristive IMPLY Logic and its Application to Image Processing |title=2022 IEEE International Symposium on Circuits and Systems (ISCAS) |date=2022 |last1=Fatemieh |first1=Seyed Erfan |last2=Reshadinezhad |first2=Mohammad Reza |last3=Taherinejad |first3=Nima |pages=3115–3119 |isbn=978-1-6654-8485-5 }}</ref> Voice-activated assistants and other home automation systems may benefit from faster understanding and response to user orders.
Data Volumes: As the data used by organizations grew traditional data warehouses just couldn’t deliver a timely, accurate and real time data. The extract, transform, load ([[Extract, transform, load|ETL]]) process that periodically updates data warehouses with operational data can take anywhere from a few hours to weeks to complete. So at any given point of time data is at least a day old. In-memory processing makes easy to have instant access to terabytes of data for real time reporting.▼
* In-memory processing is also used by embedded systems in appliances and high-end digital cameras for efficient data handling. Through in-memory processing techniques, certain IoT devices prioritize fast data processing and response times.<ref>{{Cite web |title=What is processing in memory (PIM) and how does it work? |url=https://www.techtarget.com/searchbusinessanalytics/definition/processing-in-memory-PIM |access-date=2023-12-05 |website=Business Analytics |language=en}}</ref>
==Software==
== Advantages of In-memory BI ==▼
=== Disk-based data access ===
Several in-memory vendors provide ability to connect to existing data sources and access to visually rich interactive dashboards. This allows business analysts and end users to create custom reports and queries without much training or expertise. Easy navigation and ability to modify queries on the fly is an appealing factor to many users. Since these dashboards can be populated with fresh data, it allows users to have access to real time data and create reports within minutes, which is a critical factor in any business intelligence application.▼
====Data structures====
With In-memory processing the source database is queried only once instead of accessing the database every time a query is run thereby eliminating repetitive processing and reducing the burden on database servers. By scheduling to populate In-memory database overnight the database servers can be used for operational purposes during peak hours.▼
With disk-based technology, data is loaded on to the computer's [[hard disk]] in the form of multiple tables and multi-dimensional structures against which queries are run. Disk-based technologies are often [[relational database management system]]s (RDBMS), often based on the structured query language ([[SQL]]), such as [[Microsoft SQL Server|SQL Server]], [[MySQL]], [[Oracle database|Oracle]] and many others. RDBMS are designed for the requirements of [[Software transactional memory|transactional processing]]. Using a database that supports insertions and updates as well as performing aggregations, [[join (SQL)|join]]s (typical in BI solutions) are typically very slow. Another drawback is that SQL is designed to efficiently fetch rows of data, while BI queries usually involve fetching of partial rows of data involving heavy calculations.
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Information technology (IT) staff may spend substantial development time on optimizing databases, constructing [[index (database)|index]]es and [[aggregate (data warehouse)|aggregate]]s, designing cubes and [[star schema]]s, [[data modeling]], and query analysis.<ref>{{cite book|last=Earls|first=A|title=Tips on evaluating, deploying and managing in-memory analytics tools|year=2011|publisher=Tableau|url=http://www.analyticsearches.com/site/files/776/66977/259607/579091/In-Memory_Analytics_11.10.11.pdf |archiveurl=https://web.archive.org/web/20120425232535/http://www.analyticsearches.com/site/files/776/66977/259607/579091/In-Memory_Analytics_11.10.11.pdf |archivedate=2012-04-25}}</ref>
====Processing speed====
Reading data from the hard disk is much slower (possibly hundreds of times) when compared to reading the same data from RAM. Especially when analyzing large volumes of data, performance is severely degraded. Though SQL is a very powerful tool, arbitrary complex queries with a disk-based implementation take a relatively long time to execute and often result in bringing down the performance of transactional processing. In order to obtain results within an acceptable response time, many [[data warehouse]]s have been designed to pre-calculate summaries and answer specific queries only. Optimized aggregation algorithms are needed to increase performance.
=== In-memory data access ===
With both in-memory database and [[data grid]], all information is initially loaded into memory RAM or flash memory instead of [[hard disk]]s. With a [[data grid]] processing occurs at three [[orders of magnitude|order of magnitude]] faster than relational databases which have advanced functionality such as [[ACID]] which degrade performance in compensation for the additional functionality. The arrival of [[Column-oriented DBMS|column centric databases]], which store similar information together, allow data to be stored more efficiently and with greater [[Data compression|compression]] ratios. This allows huge amounts of data to be stored in the same physical space, reducing the amount of memory needed to perform a query and increasing processing speed. Many users and software vendors have integrated flash memory into their systems to allow systems to scale to larger data sets more economically.
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* Following [[Moore's law]], the number of transistors per square unit doubles every two or so years. This is reflected in changes to price, performance, packaging and capabilities of the components. [[Random-access memory]] price and CPU computing power in particular have improved over the decades. CPU processing, memory and disk storage are all subject to some variation of this law. As well, hardware innovations such as [[Multi-core processor|multi-core architecture]], [[NAND flash memory]], [[Parallel computing|parallel servers]], and increased memory processing capability, have contributed to the technical and economic feasibility of in-memory approaches.
== References ==▼
* In turn, software innovations such as column centric databases, compression techniques and handling aggregate tables, enable efficient in-memory products.<ref>{{cite web|last=Kote |first=Sparjan |title=In-memory computing in Business Intelligence |url=http://www.infosysblogs.com/oracle/2011/03/in-memory_computing_in_busines.html |url-status=dead |archiveurl=https://web.archive.org/web/20110424013629/http://www.infosysblogs.com/oracle/2011/03/in-memory_computing_in_busines.html |archivedate=April 24, 2011 }}</ref>
{{Reflist}}▼
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* In-memory processing may be available at a ''lower cost'' compared to disk-based processing, and can be more easily deployed and maintained. According to Gartner survey,<ref>{{Cite web |title=Survey Analysis: Why BI and Analytics Adoption Remains Low and How to Expand Its Reach |url=https://www.gartner.com/en/documents/3753469 |access-date=2023-12-05 |website=Gartner |language=en}}</ref> deploying traditional BI tools can take as long as 17 months.
*Decreases in power consumption and increases in throughput due to a lower access latency, and greater memory bandwidth and hardware parallelism.<ref>{{Cite book|last1=Upchurch|first1=E.|last2=Sterling|first2=T.|last3=Brockman|first3=J.|title=Proceedings of the ACM/IEEE SC2004 Conference |chapter=Analysis and Modeling of Advanced PIM Architecture Design Tradeoffs |date=2004|chapter-url=https://ieeexplore.ieee.org/document/1392942|___location=Pittsburgh, PA, USA|publisher=IEEE|pages=12|doi=10.1109/SC.2004.11|isbn=978-0-7695-2153-4|s2cid=9089044 |url=https://resolver.caltech.edu/CaltechAUTHORS:20170103-172751346 }}</ref>
==== Application in business ====
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▲With
With a large number of users, a large amount of [[Random-access memory|RAM]] is needed for an in-memory configuration, which in turn affects the hardware costs. The investment is more likely to be suitable in situations where speed of query response is a high priority, and where there is significant growth in data volume and increase in demand for reporting facilities; it may still not be cost-effective where information is not subject to rapid change. [[Computer security|Security]] is another consideration, as in-memory tools expose huge amounts of data to end users. Makers advise ensuring that only authorized users are given access to the data.
== See also ==
* [[Computational RAM]]
* [[System on a chip]]
* [[Network on a chip]]
▲== References ==
▲{{Reflist}}
[[Category:
[[Category:Database management systems]]
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