Businesses are working with more data-intensive applications in order to remain competitive, but these resources require improved performance. This article looks at how NVMe can help.
The demand for more IT resource-intensive applications has significantly increased today, whether it is to process quicker transactions, gain real-time insight, crunch big data sets, or to meet customer expectations.
Many businesses select non-volatile memory express (NVMe) storage when their data-intensive applications demand fast access to data. That’s because NVMe provides 6x higher bandwidth and IOPS advantage compared to SAS/SATA SSD. This article takes a look at some sector-specific examples of NVMe deployment for processes in which performance really matters.
Financial Services
Latency poses a major problem for financial sector companies, whether on high-frequency trading platforms or quantitative trading applications. In both cases, market data is only relevant for a limited period of time, and there is a tight time window for extracting intelligence and making accurate decisions.
To compound this problem, IDC estimates up to 450 billion online business transactions each day by 2020, with financial services firms already generating billions and trillions of data records over time and creating an explosive escalation in data volumes.
But this is not a new problem. Historically, the analytics environment for financial firms has never been able to scale with the growth in transactional data, at least not without massive disruption and downtime. Adding more capacity to the existing architecture was extremely expensive and could mean unacceptable periods of IT system maintenance.
Because of this, financial sector companies are now turning to NVMe storage systems. It is a much faster and reliable storage technology that doesn’t compromise on availability or scalability – and is relatively quick and unobtrusive to install.
NVMe makes real-time data analytics possible even when transaction volumes continue to increase, allowing immediate action on events, such as risk management, sales, and trades. Through NVMe, financial firms are monetizing data-driven insights and solving challenges in real-time, which ultimately leads to faster business growth.
Genomics
Away from the financial services sector, NVMe is a making personalized medicine and advanced cancer treatments a reality. Initial analysis of one person’s genome produces approximately 300GB-1TB of data. Then the crucial secondary analysis generates a large random-access load on the genomes, which requires a large capacity and quick-access storage.
Without NVMe, detailed genome analysis can take five days or more to complete, especially if the data is stored on a spinning disk. By comparison, NVMe makes it possible to get the results in just one day.
That’s a massive step forward, but to make the medical breakthroughs that genome research and life sciences companies are working toward.
NVMe and Artificial Intelligence
Artificial intelligence (AI) and machine learning (ML) are gaining traction across the economy, but particularly in the financial sector where they are used to predict investment trends. It is also becoming crucial in the manufacturing sector for AI-based image recognition software and checks for defects during product assembly.
Whichever sector it is applied to, AI needs a high level of computing power coupled with a high-performance and low-latency architecture in order to enable parallel processing of data in real time. Without this in place, AI and ML simply cannot function effectively.
The speed and processing power delivered by NVMe is critical during training and inference. Without NVMe to prevent bottlenecks and latency issues, these stages can take time, causing the software to malfunction or make incorrect decisions further down the line.
Shared NVMe storage solves the performance challenge once the AI or ML applications are up and running by giving shared read/write data access to all nodes in the cluster at the performance of local SSDs. The need to cache or replicate datasets to all nodes in the GPU cluster is eliminated, improving overall storage efficiency.
With some networks supporting as much as 1PB, the GPU cluster can tackle massive in-depth learning and training for improved results and act as a catalyst for developing deeper neural networks and advanced AI applications.
Conclusion
The need for increased network processing speed is driving enterprises to look for faster storage and connectivity. It indeed looks like 2019 could be the year in which every sector of the economy would want to gain quicker insight from the data they hold, even when data sets are continuing to grow in size.
Regardless of which economic sector you look at, fast access to data helps build better business insight, enables companies to adopt the latest trends that can grow their profits and extract the value from the information they hold. With that in mind, it makes sense for enterprises to be exploring NVMe right now and gaining an understanding of what it can do for them.
This will enable them to integrate the technology into their own environments in the most cost-effective way as well as to take advantage of the benefits before their competitors.