It’s Time to Stick a Fork in Software-Defined
Software-Defined Compute Hits a Wall: A Look at the Scaling Limits and the Path Forward
For two decades, scale-out and software-defined architectures have been the stalwarts of data centers. They achieve economies of scale by leveraging commoditized hardware with software overlays that define the behaviors and capabilities. The benefits of this are unmistakable: flexibility, cost-effectiveness, and a comfortable adherence to Moore's Law.
However, we've reached an inflection point where the traditional scale-out model buckles under its own heft. AI workloads, significant culprits for explosive compute and memory demands, have ushered in a new era where space, cooling, and power requirements are unsustainable.
The Dawn of Hardware-Accelerated Compute
Enter hardware acceleration—a solution for data centers grappling with the current conundrum. It's no longer tenable from a power, space, and cooling perspective to brute-force solutions through scaling; a more innovative strategy must satiate today's hunger for performance.
GPUs are the poster child of this movement, skillfully handling AI workloads thanks to their parallel processing prowess. This transition marks a broader acceptance that hardware-augmented systems, including DPUs, FPGAs, and specialized ASICs, can no longer be the exception but the norm for burgeoning high-performance applications—they assist by providing power-efficient, specialized, high-performance solutions that lift considerable burdens off general-purpose CPUs.
Storing Up Trouble
The storage domain, specifically regarding SSDs, appears to be charting a different course—one that's regrettably inverse to the beneficial trend of computational offload. Here, we see an effort to simplify the devices while heaping additional workload onto the CPU. This means general-purpose compute is now performing more garbage collection and other storage management, drastically increasing the required CPU cycles for storage. The primary reason is the legacy block interface, which does not match real-world needs. Database records, files, and everything else do not nicely fit into the fixed-sized allocation units we have clung to for the last 50+ years (Yes, I’m looking at you 512-byte sectors). So, everyone runs a storage engine on top of block storage to offer a better abstraction, e.g., key-value.
Correcting Course: The Storage Solution
While the storage conundrum might be well-documented, the direction of innovation needs recalibration. Despite the valuable attempts by the computational storage sector, the real computational workload we should be offloading is storage compute, i.e., exposing key-value to the system. Hence, potential improvements would be groundbreaking if we moved the storage engine into the hardware domain.
At QiStor, we believe in this mission. Key-Value in hardware acceleration is a game changer. If you want to know more, come and talk to us—the opportunity is unbounded.
About QiStor
QiStor is a tech company specializing in Key Value Store acceleration technology. Key-Value enables every modern app in social media, mobile, web, AI, and gaming spaces to store data at scale. QiStor’s Key-Value Store solution will reduce compute power by 10x for web-scale applications, providing businesses with the most efficient and cost-effective database solutions. QiStor’s services can be easily deployed as a Platform-as-a-Service (PaaS), empowering businesses to achieve their goals faster, more efficiently, and sustainably.