Architecting for Efficiency: Optimize Storage for AI, ML, and DL
A lot goes into a task as ostensibly manageable as relocating a block of data from a disk drive to a computer cluster, which threatens to slow down compute processing. However, this is not a completely unmanageable dilemma and can be avoided. The volume and location of the reads and writes to a storage system have a significant influence on the capacity of said storage system to function efficiently for the application that it is maintaining. A storage system can be tuned to satisfy the requirements of machine learning. Nevertheless, it is essential to remember that those requirements are likely quite dissimilar from most other business applications.
There exist three principal determinants that impact the capability of a storage system to implement data to a machine learning infrastructure quickly and efficiently:
- The location of the data. One of the most consequential causes of latency is the time it takes to bring data from storage devices to the processors that will utilize it. Positioning data near the machine learning cluster that will utilize it is a requirement, which is one of the main predicaments of using the public cloud for machine learning. Employing an array that is able to expand its data across a large number of storage processing machines will further reduce latency while also having a net result of improving overall throughput.
- The access Input/Output (I/O) pattern. The input/output (I/O) access pattern has historically been the most challenging design point for any storage system to adhere to. Storage arrays that can optimize themselves to respond to I/O patterns are fundamental requirements of any machine learning architecture.
- The storage capacity. Machine learning flourishes on data. The more data it has, the more reliable the outcomes. Transferring data between multiple storage devices and the compute elements hosting the machine learning algorithms is a significant influence on overall performance. Furthermore, machine learning tends to develop increased appetites for more data. Future-proofing with flexible storage capacities achieved through a single footprint is a conclusive advantage for machine learning solutions.
In addition to the aforementioned, it is more common for machine learning applications when consuming data using object storage semantics. Object storage varies from conventional block storage in that data is within a navigable namespace, which enables applications to quickly classify and utilize those objects.
Object storage varies from conventional file systems in that it is a very flat pool of data on the storage device. At present, many object storage systems are optimized for the long term, archival storage and not for high performance.
The Solution: Raid Incorporated + Xanadu X-AI
Enterprises need a storage solution that acts as the principal storage for the complete machine learning workflow, establishing a base for data-centric infrastructures. AI solutions need storage that is optimized to process and deliver small, metadata-heavy workloads and sequential I/O. It should support native object store as well. RAID Inc.’s X-AI Accelerated, Any–Scale (AI) solutions for artificial intelligence (AI) and deep learning (DL), provides unmatched versatility for your enterprise’s AI requirements. Engineered with AI-enabled data centers in mind, X-AI solutions are optimized for ingest, training, data transformations, replication, metadata, and small data transfers.
RAID Inc. offers adaptability in platform options with the all-flash NVMe X2-AI/X4-AI or the X5-AI, a hybrid flash and hard drive storage platform which supports parallel access to flash and exceedingly expandable HDD storage. The X-AI storage platform supports a scale-out model with solutions beginning at a few terabytes but scalable to tens of petabytes. If you’re looking for a storage solution specially designed for AI, ML, and DL then contact us today to learn more about RAID Inc.’s X-AI Accelerated.
