It is becoming increasingly important to efficiently orchestrate HPC (High Performance Computing) storage for AI workloads. Artificial Intelligence has blown up and is being used in virtually every industry to push the boundaries of what is possible.
High-Performance AI Workloads Are Pervasive
AI is advancing healthcare, for example through clinical decision support systems to help prevent stroke. It’s being used in agriculture to enable precision farming and help farmers produce more food, for example through smart drones that water only the parts of a farm that need watering. In finance, guys in suits are using AI to predict the performance of markets and make a lot of money in the process.
AI has found uses in marketing, banking, gaming, space exploration, autonomous vehicles, and chatbots among other industries.
All these AI applications are being fuelled by data, and in massive amounts. For instance, consider web intelligence. Companies are using AI-powered solutions to monitor the entire internet in real-time, including the deep web, and keep abreast of all events related to their brand. That way, if a hacker successfully steals their data and attempts to sell it on the dark web, the company will be notified immediately and take action to mitigate damage.
For that to happen, streams of data from multiple internet sources have to be collated and analyzed quickly. This is the case in other applications as well.
Fortunately, current processing power can handle such compute-intensive workloads.
GPUs in Artificial Intelligence
The effective application of artificial intelligence is dependent on having the right hardware, without which you’ll be limited, frustratingly so. Hardware providers are equipping modern servers with advanced processing, and legacy hardware is being upgraded with chipsets.
AI needs fast processing. The faster the processing speed, the more the algorithms and processes that can run simultaneously, and the more use we can get from AI-powered systems.
When it comes to fast processing, nothing fits the bill like a graphics processing unit. (GPU)
GPUs, which were originally used in gaming for graphics rendering, are now being used in conjunction with CPUs to handle AI workloads. A GPU is able to manage huge amounts of data and with the ability to process thousands of threads simultaneously, can execute operations at an advanced speed.
GPUs excel in pattern matching, content analysis, and image recognition, all of which are essential in today’s AI workloads.
However, even with the advanced computational capability made possible by GPUs, organizations have been struggling to make the most of them.
Achieving Optimal ROI With GPUs Has Been a Challenge
The success of modern AI applications is contingent on the ability to process huge amounts of collected and simulated data in the shortest time possible. For such high-performance computing, GPU-based systems have become the go-to setup.
However, GPUs are incredibly data-hungry, with some being able to process up to 16Gb per second. Traditional storage solutions are incapable of servicing the massive data appetite of GPUs. Bandwidth limitations lead to lower throughput in terms of data transfer. If you can’t get enough data to the GPU, then you can’t maximize its potential and you’ll have a problem with ROI.
To efficiently utilize GPUs and unleash their potential for AI workloads, more efficient storage and data transfer solutions have to be put in place. Using NVMe storage is one such solution.
What is NVMe Storage?
NVMe is a storage access protocol that unlocks the potential of SSD storage and the PCIe interface. SSD storage involves the electronic storage of data, as opposed to traditional HDD storage, which stores data sequentially on magnetic disks. SSD enables fast retrieval and storage of data, as there is no sequential access.
The PCIe interface allows more data to be transferred at a time. Together, SSD and PCIe, used in conjunction with the NVMe protocol, result in faster data transfer, higher throughput, and lower latency. Such a setup is perfect for data-hungry AI applications on a local device.
However, at the network level, with traditional data transfer mechanisms, the benefits of NVMe are lost.
To enjoy the speed and performance of local NVMe at the network level, software-defined NVMe block storage has to be implemented.
NVMe Block Storage
This is a software-defined solution that aggregates multiple NVMe units across a network, combining them into one block of virtual storage. One of the benefits of this is that it increases the storage available to any AI application. The alternative would be for an NVMe unit to only be dependent on the resources in one node.
Such a solution is appropriate as AI applications usually run on networked devices. The data being processed is not necessarily generated at the node that’s processing it. Using block NVMe makes it possible to enjoy the speed and performance of local NVMe at the network level. This results in much better performance than traditional alternatives. For example, “CPU tax” is not imposed and memory can be accessed directly, significantly reducing latency.
By helping eliminate storage and compute overhead, this block storage solution results in huge gains for data processing ability.
The global High Performance Computing (HPC) Market research report, published by Value Market Research, is designed to offer various market framework such as market size, portion, trends, growth path, value and factors that impact the current market dynamics over the forecast period 2021-2027.
Most importantly, this report also provides the latest significant strategies adopted by major players along with their market share.The report also covers detailed competitive landscape including company profiles of key players operating in the global market.
The key players in the high performance computing (HPC) market includes Atos SE, Advanced Micro Devices, Inc. (AMD), Cray Research, Inc., Cisco Systems, Inc., Dell Technologies, Inc., Fujitsu Limited, Hewlett Packard, Inc. (HP), Intel Corporation, International Business Machines Corporation (IBM) and Silicon Graphics International Corp.. An in-depth view of the competitive outlook includes future capacities, key mergers & acquisitions, financial overview, partnerships, collaborations, new product launches, new product developments and other developments with information in terms of H.Q.
Get more information on "Global High Performance Computing (HPC) Market Research Report" by requesting FREE Sample Copy at https://www.valuemarketresearch.com/contact/high-performance-computing-hpc-market/download-sampleMarket DynamicsThe rising need for processing data with speed and accuracy across the globe is driving the market growth.
Additionally, the growing adoption of cloud-based deployment is further fuelling market growth.
Whereas, ongoing development of hybrid HPC solutions for maximizing benefits of HPC is expected to offer the opportunity during the forecast period.This detailed market study is centered on the data obtained from multiple sources and is analyzed using numerous tools including porter’s five forces analysis, market attractiveness analysis and value chain analysis.
Global High-Performance Computing Market Research Report, by Deployment (On Premise, On Cloud), by Vertical (BFSI, Manufacturing, Retail, Healthcare), by Component (Storage, Software, Server) — Forecast till 2023Market ScenarioThe data around enterprises in bulging up and therefore requires a high amount of mathematical calculations and computational capabilities to resolve business issues.
High-performance computing was earlier used for training and simulation, a navigation system, and mostly in defense and aerospace.
As this industrial vertical required high-performance computing capabilities, resulting in high-performance computing.This scenario is changed in recent times as there is huge demand from the industrial, and government.
These sectors are using the data to churn out specific insights helpful for their organizations to carry out.
The high-performance computing technology is emerging prominently in government for national defense and security requirements.
HPC is proving beneficial for development and design of advanced vehicles, weapons, high-resolution image processing, satellite mapping, and cryptographic analysis.
Market Analysis The global high-performance computing HPC as a Service industry is predicted to touch USD 9.50 billion at a healthy 12.8% CAGR between 2020- 2027, states the recent Market Research Future (MRFR) analysis.
High-performance computing or HPC, simply put, is the ability of processing data and performing complex calculations at high speed.
According to the recent MRFR report, such factors include the deployment of new services and products, access to better technologies, reduced capital costs, need for flexible computing services, rise in government initiatives, and growing need for high-speed and efficient computation.
On the contrary, the high price of HPC services and the on-going COVID-19 pandemic are factors that may limit the global high-performance computing (HPC) as a service market growth over the forecast period.
Get a Free Sample @ https://www.marketresearchfuture.com/sample_request/7345 Market SegmentationThe MRFR report highlights an inclusive segmental analysis of the global high performance computing as a service market based on deployment, component, organization size, verticals, and others.
The service segment is again segmented into maintenance and support services, consulting service, and integration service.
