The advent of high-performance, high-capacity flash storage has changed the dynamics of the storage-compute relationship. Today, a handful of NVMe flash devices can easily saturate the PCIe bus complex of most servers. To address this mismatch, a new paradigm is required that moves computing capabilities closer to the data. This concept, which is known as “In-Situ Processing”, provides storage platforms with significant compute capabilities, reducing the computing demands on servers. For applications with large data stores and significant search, indexing, or pattern matching workloads,

In-Situ Processing offers much quicker results than the traditional scenario of moving data into memory and having the CPU scan these large data stores. In-Situ Processing can also enable existing applications to scale to much greater levels than is currently possible with discrete storage and computing resources. Because computation capabilities scale linearly as storage is added into compute nodes, In-Situ Processing can enable new classes of applications for enterprises and cloud service providers.
At NGD Systems, we are blazing the trail of in- situ processing for storage devices. Our in-situ storage architecture and Catalina products make the deployment of many large dataset applications possible and practical, whether from an access time, power/cooling, or real estate standpoint. If you would like to find out more on how you can benefit, please contact us for further discussions on how In-Situ Processing can help solve your data center and business issues.
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The point about a handful of NVMe devices saturating the PCIe bus clearly shows why conventional server architectures are falling behind. Moving compute closer to the data through in-situ processing seems like a practical way to reduce that bottleneck—much like how Videoinu AI brings processing into a more streamlined workflow.
The point about a few NVMe devices saturating the PCIe bus makes the case for in-situ processing especially clear—moving computation closer to the data could reduce unnecessary server bottlenecks. This Face AI tool is an interesting example of how specialized processing can support data-intensive workloads: this Face AI AI tool.
The point about a few NVMe devices saturating the PCIe bus clearly shows why moving computation closer to data matters. The in-situ processing model also offers an interesting parallel to efficient video workflows like seedvr.online.
The point about a few NVMe devices saturating the server’s PCIe bus clearly shows why moving compute closer to storage matters. It’s an interesting parallel to how Flixier approaches resource-intensive processing through a streamlined platform.
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