Crucial Differences Between Enterprise, HPC and AI Storage

Choosing the correct I/O storage application is all about understanding three crucial things: the differences in how enterprise storage, high-performance computing (HPC) storage and Artificial Intelligence (AI) storage function. While enterprise workloads are significantly dissimilar from HPC and AI workloads, the latter two require different data management architectures as well.

Enterprise Storage Profile

Overall, enterprise storage systems employ transactional data processes. In data management terms, transactional data is merely data recorded from sequences of information exchanges. Devices such as Hard Disk Drives (HDD) and Solid State Drives (SSD) are perfect examples of enterprise storage systems that use transactional data processes.

Since such storage systems are mechanical, most traditional enterprise storage systems measure performance requirements by Input/Output Operations Per Second (IOPS). Typically, file enterprise storage is small in size with random file access. These above-mentioned factors mean that data movement is quite minimal.

Lastly, since enterprise storage machines operate transactionally, their “read/write intensity” is mixed.

HPC and AI Storage Profiles

Both HPC and AI storage machines deal with very hefty workloads (also called “batch jobs”) where large data sets are spread across any given number of computing machines (also referred to as “distributed computer clusters”). Distributed memory systems are supported by distributed computer clusters which are all connected via high-speed networks.

Thus, rather than being measured by IOPS, HPC and AI storage systems are measured by bandwidth. If distributed computer clusters are connected via scalable full Multi-Gigabit switch, they earn the moniker “Beowulf clusters.”

And while typical file access is sequential for HPC, file access for AI storage architectures are flexible. This flexibility allows AI storage architectures to scale out as needed.

When it comes to data movement, HPC and AI completely crush transactional data movement. However, HPC data movement is slightly more voluminous than that of AI, which is on the moderate end of the scale. This is because HPC systems are “write-intensive” while AI systems are focused primarily on reading and analyzing data.

Network Attached Storage (NAS) Systems Are Not Sufficient for AI

Pundits believe that HPC and AI possess a number of complimentary strengths leading to an eventual convergence. Adnan Khaleel, an opinion writer for cio.com, wrote AI has finally reached an “inflection point in its maturity.” Khaleel further wrote, “momentum from HPC and data analytics convergence is leading the way for AI convergence.”

However, HPC storage vendors have not yet bridged critical gaps between HPC and AI workloads. For instance, data in AI workloads is comprised of millions and sometimes billions of small files that create lots of metadata. At some point, all of that accumulated metadata cause file systems to reach their threshold, significantly affecting their performance.

And since AI collects and analyzes data in real-time, storage performance is a huge deal. Users expect AI to remain just as responsive as the human brain. This means a file system that stores AI data requires a scalable metadata management engine.

While HPC workloads process data that has already been analyzed, AI workloads process raw data from devices and sensors – maybe hundreds or thousands of them. Thus, AI file systems must offer an ultra smooth ingest and egress performance in real time.

This now brings us to the issue of bandwidth; though both HPC and AI workloads rely on bandwidth, HPC systems (or network attached storage systems) aren’t sufficiently suited for streaming millions or billions of tiny files that AI workloads are comprised of. Some IT have attempted to resolve this issue by employing burst buffers as caches. However, this solution is expensive and overly complex.

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