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In-memory processing

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Last Updated: 18 January 2022

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General | Latest Info

From conceptual standpoint, idea of embedding processing within main memory makes logical sense since it would eliminate many layers of latency between computer and memory in modern systems and make parallel processing inherent in many workloads overlay elegantly onto distributed computer and storage components to speed up processing. This is founding principle of French startup called Upmem, which uncloak its processing in Memory, or PIM, modules last year at Hot Chips 31 conference last summer at Stanford University. Last summer, Upmem had created some PIMs and was giving them out to early adopters to play around with, and now company has put together some early benchmarks that show off benefits of processing - in - Memory approach. There are all kinds of data processing systems that move compute closer to data - databases have been doing all kinds of tricks to move data closer to computer for decades within single system through various kinds of caching, sharding, and indexing, and Hadoop analytics framework literally dispatches compute tasks on chunks of data to servers that had disk drives that had that data. In these and other cases, idea is that you move compute to data, not data to compute as is typically done in standard server and its software stack. But with PIM architecture that Upmem has create, data processing units, or DPUs, are not on system bus or even on other side of memory bus, but are etched into Memory Chips themselves, which are then soldered onto DIMMs just like any other memory. Distances between DPUs and cells in DRAM Chips are short, and so energy to move that data back and forth between PIM computing circuits and Memory cells is very small. That means it does not take huge amounts of energy and that latencies, at least on PIM itself, very small. Pim Memory remains just as far away from CPU hosts and their various levels of cache, and storage outside of cache and main Memory remains just as far on PCI - Express bus. Upmem started sampling its 8 GB PIM modules last fall, which have eight DDR4 Memory Chips with total of 64 DPUs on each side of module, for total of 16 Chips and 128 baby compute elements on two - side module that deliver 8 GB of capacity at cost of around 10X of regular 8 GB DDR4 DIMMs. Memory runs at 2. 4 GHz and DPUs at 500 MHz. We went into architectural details last summer and we are not going to get all of these again right here. But two points emphasizing again. First is architecture of in - Memory computing and other is expected incremental cost of that compute.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Disk-based Business Intelligence

Sisense technology works differently. It was based on thorough analysis of strengths and weaknesses of both OLAP and in - memory technologies, while considering off - shelf hardware available today. Main Benefit Of Sisense Is That IT Provides Powerful OLAP - Like Functionality And Scalable Ad - Hoc Analytics - Without Hefty Projects And Without Compromising On Rapid Implementation And Fast Query Response Times Characterized By In - Memory Solutions. Our business Intelligence data visualization software implements Sisense technology in low - cost, easy - to - use package, with: unprecedented data volume and concurrent user scalability, on commodity hardware Natively Support shares data scenarios with high - speed query performance, without requiring OLAP cubes or pre - aggregations Ability to incorporate additional / change data, w / o rebuilding entire data model Support for dimensional model and multidimensional analysis Separation between BI application layer and physical data layer SQL layer available to conform to industry standards

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

In-memory processing tools

Memory processing can be accomplished via traditional databases such as Oracle, DB2 or Microsoft SQL Server or via NoSQL offerings such as in - Memory DATA grid like Hazelcast, Infinispan, Oracle Coherence ScaleOut Software. With both in - Memory database and DATA grid, all INFORMATION is initially loaded into Memory RAM or flash Memory instead of hard disks. Data grid processing occurs at orders of magnitude faster than relational databases which have advanced functionality such as ACID which degrades performance in compensation for additional functionality. Arrival Of Column Centric Databases, Which Store Similar INFORMATION Together, Allows DATA To Be Stored More Efficiently And With Greater Compression Ratios. This allows huge amounts of DATA to be stored in same physical space, reducing amount of memory needed to perform queries and increasing processing speed. Many users and software have integrated flash memory into their systems to allow systems to scale to larger DATA sets more economically. Oracle has integrating flash Memory into Oracle Exadata products for increased performance. Microsoft SQL Server 2012 / DATA Warehousing Software has been coupled with Violin Memory flash Memory arrays to enable in - Memory processing of DATA sets greater than 20TB. Users query DATA loaded into systems memory, avoiding slower database access and performance bottlenecks. This differs from caching, very widely used method to speed up query performance, in that caches are subsets of very 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 quickly by multiple concurrent users or applications at detailed level and offers potential for enhanced analytics and for scaling and increasing speed of application. Theoretically, improvement DATA access speed is 10 000 to 1 000 000 times compared to disk. It also minimizes need for performance tuning by IT staff and provides faster service for end users.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Sources

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

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