High-Performance Computing Trends
Emerging technologies like the Industrial Internet of Things (IIOT), AI, simulation, machine learning, and data storage are going mainstream.
Since these technologies demand massive computing power, your High-Performance Computing (HPC) resources need to be more accessible, efficient, and scalable.
Christopher Willard, chief research officer at Intersect 360 Inc., noted that more and more industries are expanding their budgets to adopt high-performance computing, giving a boost to HPC’s current market growth.
HPC solutions offer advanced problem-solving within a given timeframe across a range of industrial and healthcare sectors.
The HPC market is expected to keep growing at a strong pace over the coming years. It’s worth tracking all the major trends in HPC so you can get the best results from these systems.
In this article, we will explore the top 5 current market trends in High-Performance Computing (HPC):
1. HPC Services in the Cloud
Building and running an in-house HPC service requires big investments in infrastructure and specialized expertise.
So while a large chunk of computing still happens inside dedicated or private clouds, the demand for public cloud computing services is climbing fast.
Several major companies, including IBM and Amazon, offer services in this space, whether that’s raw compute cycles or cycles bundled with additional services.
Cloud computing in HPC takes workload pressure off companies so they can focus on other priorities.
HPC in the cloud also opens up the market, since more organizations can run research and experiments on high-end computing systems without owning them outright.
There are still questions and challenges around regulatory, compliance, and security issues that are being analyzed and worked through.
We break down Chromebook for business in a separate post that complements this one.
Still, publicly-managed cloud computing already solves several machine learning, data computing, and AI challenges today.
2. Data Collection and Analysis
The rise of the internet, Wi-Fi, and mobile connectivity has made data collection faster and easier than ever.
Users are active on social media, interacting through mobile devices, and generating more data than at any point in history.
That means the challenge of collecting large data sets for analysis and research has gotten smaller. The time saved can now go toward refining and extracting insights for data-driven results.
HPC has the ability to compile and process massive records of data that feed into further analysis and research on factors like human behavior, demographics, audience preferences, and more.
3. GPU Computing
Originally built for high-resolution gaming, GPUs (graphic processing units) are now used in a wide range of data-heavy fields, from machine learning to self-driving cars.
GPUs are designed for data computations and are excellent at processing HPC workloads because they have hundreds of processing units working in parallel.
That makes GPUs one of the core building blocks of high-performance computing.
They bring a large hardware architecture along with high performance in floating-point arithmetic and memory operations, making them well-suited for scientific and engineering workloads. That’s why they’ve become popular as HPC accelerators.
GPUs aren’t just cost-effective either. They also save space and power.
That means you need fewer operating systems compared to traditional CPU-only clusters with similar computational capacity.
4. Artificial Intelligence
We’re living in an age where Siri and AI-driven shopping experiences have become part of daily life.
For a different take, see our guide on computer courses for beginners online.
These advanced AI concepts have raised consumer expectations and made the business world more competitive.
Artificial Intelligence is now mainstream with machine learning models that can produce faster results while maintaining the same level of accuracy.
Several key HPC fields are benefitting from advanced AI capabilities, including:
- Life sciences
- Pattern clustering, weather
- Astronomy
- Medical research
- Risk and fraud detection in financial services
- Logistics
- Computational fluid dynamics (CFD), computer-aided engineering (CAE), and computer-aided design (CAD) to name a few.
HPC’s ability to process massive amounts of data, scale up, and maintain accuracy helps speed up AI to produce better outcomes.
Example: Applying expert-level heuristics through deep learning results in thousands of transactions, workloads, or simulations per second.
5. Edge Computing
For a long time, most processing happened in distant centralized data centers around the world.
Today, things are shifting back toward decentralization, where data can be made available closer to end-point devices for faster processing.
Speed is a key factor for modern gadgets.
Centralized data can slow things down because new-age applications need computation, storage, and network capacity to be near the device or app where the data is generated. That’s the only way to get the fast response times these applications require.
Think about Internet of Things gadgets like Alexa and self-driving cars. They need near-instant responses.
Edge computing cuts latency, reduces cloud processing and loading times, and minimizes network issues, which means faster response times overall.
Consumer attention spans are getting shorter and people expect quicker results. That’s why appliances and systems with edge computing built in will keep gaining ground over other technologies.
Takeaway
There’s a lot of effort going into modernizing traditional HPC interfaces so they’re easier for everyday users to work with.
Companies are investing in specialized resources for advanced computing technologies like AI, edge computing, GPUs, and more. These five big trends in HPC aren’t going anywhere anytime soon.
