science

In-situ processing

In-situ processing is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand In-situ processing rather than just read about it. In short: In-situ processing, also known as in-storage processing (ISP), is a computer science term that refers to processing data where it resides. In-situ means "situated in the original, natural, or existing place or position." An in-situ process processes data where it is stored, such as in solid-state drives (SSDs) or memory devices like NVDIMM, rather than sending the data to a computer's central processing unit (CPU).

Key takeaways

In-situ processing belongs to science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect In-situ processing to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of In-situ processing from memory before moving on to harder problems.

Reference excerpt

In-situ processing, also known as in-storage processing (ISP), is a computer science term that refers to processing data where it resides. In-situ means "situated in the original, natural, or existing place or position." An in-situ process processes data where it is stored, such as in solid-state drives (SSDs) or memory devices like NVDIMM, rather than sending the data to a computer's central processing unit (CPU). The technology utilizes embedded processing engines inside the storage devices to make them capable of running user applications in-place, so data does not need to leave the device to be processed. The technology is not new, but modern SSD architecture, as well as the availability of powerful embedded processors, make it more appealing to run user applications in-place. SSDs deliver higher data throughput in comparison to hard disk drives (HDDs). Additionally, in contrast to the HDDs, the SSDs can handle multiple I/O commands at the same time. The SSDs contain a considerable amount of processing horsepower for managing flash memory array and providing a high-speed interface to host machines. These processing capabilities can provide an environment to run user applications in-place. The computational storage device (CSD) term refers to an SSD which is capable of running user applications in-place. In an efficient CSD architecture, the embedded in-storage processing subsystem has access to the data stored in flash memory array through a low-power and high-speed link. The deployment of such CSDs in clusters can increase the overall performance and efficiency of big data and high-performance computing (HPC) applications.

Reducing data transfer bottlenecks Webscale data center designers have been trying to develop storage architectures that favor high-capacity hosts. In the following figure (from ), such a storage system is shown where 64 SSDs are attached to a host. For the sake of simplicity, only the details of one SSD are demonstrated. Modern SSDs usually contain 16 or more flash memory channels which can be utilized concurrently for flash memory array I/O operations. Considering 512 MB/s bandwidth per channel, the internal bandwidth of an SSD with 16 flash memory channels is about 8 GB/s. This huge bandwidth decreases to about 1 GB/s due to the complexity of the host interface software and hardware architecture. In other words, the accumulated bandwidth of all internal channels of the 64 SSDs reaches the multiplication of the number of SSDs, the number of channels per SSD, and 512 MB/s (bandwidth of each channel) which is equal to 512 GB/s. While the accumulated bandwidth of the SSDs’ external interfaces is equal to 64 multiply by 1 GB/s (the host interface bandwidth of each SSD) which is 64 GB/s. However, In order to talk to the host, all SSDs required to be connected to a PCIe switch. Hence, the available bandwidth of the host is limited to 32 GB/s.

Overall, there is a 16X gap between the accumulated internal bandwidth of all SSDs and the bandwidth available to the host. In other words, for reading 32 TB of data, the host needs 16 minutes while internal components of the SSDs can read the same amount of data in about 1 minute. Additionally, in such storage systems, data need to continuously move through the complex hardware and software stack between hosts and storage units, which imposes a considerable amount of energy consumption and dramatically decreases the energy efficiency of large data centers. Hence, storage architects need to develop techniques to decrease data movement, and ISP technology has been introduced to overcome the aforementioned challenges by moving the process to data.

Efficiency and utilization The computational storage technology minimizes the data movements in a cluster and also increases the processing horsepower of the cluster by augmenting power-efficient processing engines to the whole system. This technology can potentially be applied to both HDDs and SSDs; however, modern SSD architecture provides better tools for developing such technologies. The SSDs which can run user application in-place are called computational storage devices (CSDs). These storage units are augmentable processing resources, which means they are not designed to replace the high-end processors of modern servers. Instead, they can collaborate with the host's CPU and augment their efficient processing horsepower to the system. The scientific article “Computational storage: an efficient and scalable platform for big data and HPC applications” which is published by Springer Publishing under open access policy (free for the public to access) shows the benefits of CSD utilization in the clusters. Examples of in-storage processing can be seen in fields like visualization efforts, biology and chemistry. This showcases how this technology allows for actions and results to be seen more efficiently than through data movement, regardless of the data being moved. The following figures (from ) show how CSDs can be utilized in an Apache Hadoop cluster and on a Message Passing Interface-based distributed environment.

Industry In the storage industry, implementations from several companies are now available, including from NGD Systems, ScaleFlux and Eideticom. Other companies have tried to do similar work in the past, including Micron Technology and Samsung. The approach from all of these are the same direction, managing or processing data where it resided. NGD Systems was the first company to create in-situ processing storage and has produced two versions of the device since 2017. The Catalina-1 was a standalone SSD that offered 24 TB of flash along with processing. A second product called Newport was released in 2018 that offered up to 32 TB of flash memory. ScaleFlux uses a CSS-1000 NVMe device that uses host resourcing and kernel changes to address the device and use Host resources to manage up to 6.4 TB flash on the device, or base SSD. Eideticom utilizes a device called a No-Load DRAM-only NVMe device as an accelerator with no actual flash storage for persistent data. Micron called their version ‘Scale In’ at a Flash Memory Summit (FMS) event in 2013 but was never able to productize it and was based on a SATA SSD in production. Samsung has worked on various versions of devices from KV Store and others.

References

Illustrations

Worked examples

Example 1 — a first encounter with In-situ processing

Start with the simplest possible case. Write down what In-situ processing claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to In-situ processing before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about In-situ processing ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of In-situ processing

In research: In-situ processing appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses In-situ processing in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: In-situ processing is common in secondary-school and first-year university syllabi. It links to neighbouring topics Data storage, so understanding it makes those chapters shorter.
In everyday life: Look for In-situ processing outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study In-situ processing in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what In-situ processing means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain In-situ processing out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is In-situ processing in simple terms?

Why does In-situ processing matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study In-situ processing?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on In-situ processing.