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High-performance computing (HPC) storage comprises the low-latency networking with high-speed data access required for HPC projects. HPC is the use of computers and supercomputers clustered and connected to carry out complex tasks in parallel.?

But it¡¯s more than just computational speed that makes HPC so significant. It¡¯s HPC¡¯s ability to analyze massive data sets of exabyte scale that makes it responsible for so many modern breakthroughs. To carry out these complex tasks, HPC environments demand modern storage solutions for HPC clusters.?

Also, as artificial intelligence (AI) and HPC converge, traditional enterprises can benefit even more from understanding and architecting for HPC while embracing AI. Some business leaders are even choosing to abandon their traditional HPC teams in favor of a more stable and agile converged AI infrastructure that they deploy themselves or via system integrator partners.

Here¡¯s a deep dive into HPC¡¯s demands on storage and how enterprise infrastructures can be architected to support them.

HPC is the use of advanced computational systems (e.g., supercomputers or clusters of high-performance computers) to process complex tasks in parallel, usually in fields such as scientific research, engineering, manufacturing, and computer science. HPC powers scientific simulations, modeling, verifications, and generative AI, enabling researchers and professionals to analyze massive data sets and unravel complex problems efficiently.?

Scope is one aspect; the other is speed. And the faster the data infrastructure beneath these systems, the faster the computations.

There are different types of high-performance computing for various use cases. One thing they all have in common: They generate and process huge amounts of data. The most common types of high-performance computing are defined by how the computers work together and what they¡¯re working together on, including:

• Supercomputing: Designed for often used in scientific simulations, climate modeling, digital twins, augmented or virtual reality environments, and advanced research.
• Cluster computing: Networked computers working in parallel on tasks distributed across multiple machines, often used in academic and research institutions. High-performance computing (HPC) clusters are a collection of interconnected high-performance computers designed for parallel processing, often in scientific and engineering applications.
• Distributed computing: Multiple computers connected via a network can be harnessed when systems are idle, thanks to software that volunteers download to make their computers available when not in use. leverage these systems.?
• Cloud computing: Remote servers store, manage, and process data, offering scalable computing resources for various applications. Cloud-based HPC solutions provide on-demand access to high-performance computing resources so users can access computational power without large upfront investments.
• Quantum computing: Although still a new area of research and rarely used in the enterprise, quantum computing has the potential to perform computations on a massive scale to solve complex problems faster than classical computers.
• Accelerated computing: Using specialized hardware accelerators like graphics processing units (GPUs) and neural processing units (NPUs) to enhance computational performance, especially in tasks related to AI and also real-world simulations like digital twins and the omniverse.?

Yes and no. While AI projects almost always leverage HPC resources, most HPC projects are not strictly AI-related.

As enterprises look to rearchitect their IT infrastructures to support new AI projects, HPC infrastructures are often seen as models for AI infrastructures¡ªif only because they¡¯re similar in scope and scale. HPC is as close as many enterprises have gotten to building out data centers designed for projects of this scope, specialized hardware like GPUs, chips, and computational power; however, the two are not synonymous.

AI projects require a lot of computational power, hardware accelerators, and parallel processing architectures, and cluster computing during data transformation and model training, similar to HPC. They also leverage a variety of technologies and methods including HPC. (Others include deep learning, computer vision, machine learning, and natural language processing.)?

HPC can support AI, but it¡¯s also broader. While AI focuses on models and algorithms to aid in decision-making, pattern recognition, and language processing (like we¡¯re seeing with generative AI), HPC projects can be applied to a broader range of tasks beyond AI, including science, simulations, research, engineering, data analysis, and numerical modeling.

They also differ in how they handle data. AI works with large data sets, necessary to train models. HPC can and does handle large data sets, but its focus is more on the computations it carries out.?

¡°HPC has rarely been in the domain of enterprise IT, usually staying within the confines of academia and research. Most enterprises haven¡¯t even dabbled in HPC, but even for those that have, it doesn¡¯t often mingle with other workflows; it¡¯s treated like a silo and managed as a different beast.¡± -?

Is Cloud Computing the Same as HPC?

No, cloud computing is not synonymous with HPC. Cloud computing, as mentioned above, is more of a ¡°how,¡± providing resources that can be leveraged for HPC projects. In general, cloud computing is a concept that defines how services and infrastructures are hosted and delivered, and this can include HPC.

What Industries Rely on HPC?

As we mentioned earlier, the organizations most likely to be leveraging HPC grids and HPC storage environments are those in the fields of scientific research, environmental science, weather forecasting, aerospace and automotive engineering, financial services, oil and gas, manufacturing, and healthcare, including genomics research and pharmaceutical testing.

HPC is not limited to these fields, however, and can benefit any enterprise that needs to carry out complex computations; run data-heavy simulations; process high-definition ; or conduct big data analysis.

HPC environments typically have three core components: computer processors, networking, and storage. A core demand of HPC projects is fast access to data, which makes storage a critical component to the success of these environments.?

To operate with speed and scale, HPC environments require modern file system architectures with hot and cold tiers and high-availability metadata servers. Integrating NVMe and object storage gives the HPC system the ability to meet modern workload demands with low latency and high bandwidth.

HPC data storage works by offloading data from CPUs, memory, and storage controllers quickly and efficiently, so CPUs can continue processing without interruption. The data platform for an HPC system also needs to be accessible and tiered, keeping hot data close to and accessible by the nodes.

HPC Storage Architecture: Parallel Processing, Clustering, and High-speed Interconnects

Within high-performance computing, there are three key fundamental concepts that explain how tasks are carried out:

• Parallel processing: This describes how computers (or, nodes) work together to carry out a task. In HPC, large problems can be divided into smaller tasks then solved by multiple processors or compute cores at once, which is how HPC is able to handle both massive data sets and computations so rapidly. Tasks can be handled independently by processors or processors may collaborate on a single task. However they divide and conquer, that it¡¯s happening in parallel is key.
• Clustering: Clustering is an architecture leveraged by HPC where multiple nodes work together as one, again allowing for parallel work to happen, just on a larger scale. It¡¯s also a way to build reliability into an HPC environment. Because the nodes are connected by a network into a unified, single system, tasks can be divided up and carried out, even if one node on the network fails. This includes orchestration and scheduling, where software manages the available cluster resources and intelligently delegates the work to the best-suited cluster.?
• High-speed interconnects: This describes the communication between nodes on a cluster, and these links (e.g., high-speed Ethernet) are the backbone of HPC¡¯s collaborative power and speed. High-speed interconnects allow rapid communication and parallel processing to happen quickly and efficiently between computers in the cluster and between storage nodes and compute nodes.

Features to Look for in HPC Storage

Storage is becoming increasingly important in the age of applications, big data, and HPC. What¡¯s needed is a new, innovative architecture to support advanced applications while providing best-of-breed performance in all dimensions of concurrency: IOPS, throughput, latency, and capacity. Ideally, HPC storage offers:

• A flash storage solution with an elastic scale-out system that can deliver all-flash performance to petabyte-scale data sets, ideal for big data analytics
• Massive horizontal scale to enable concurrent read/write operations while multiple nodes access storage at the same time
• Efficiency and simplicity for storage architects
• High-speed data access. Storage has to be able to handle fast and frequent requests.
• Redundancy and fault tolerance
• NVMe for low-latency access
• Object storage for simplicity and meeting cloud-native application needs

Advanced data management tools like that aid compression and deduplication

While both HPC storage and cloud storage manage data, they have key differences.

• Cloud is general; HPC is specific. HPC storage is tailored for high-performance computing applications, optimized for efficient parallel processing and rapid data access. Cloud storage offers general storage as a service for a wide range of applications (including HPC).
• Cloud is an operating model. Cloud storage is a service model for storing and managing data remotely.?
• HPC is tuned for performance. Cloud storage services may limit the amount of granular customization projects need for optimal performance. HPC storage will be optimized for speed and access, while the cloud favors more toward flexibility and scale.
• Cloud storage cost models force you to "buy" more capacity to get more performance, even if you don't need the extra storage space.

Of note, university and research center HPC workloads are increasingly moving to the cloud, while commercial and enterprise HPC workloads still tend to be on premises. However, the total cost of ownership (TCO) is high for cloud-based HPC workloads, and repatriation of HPC data sets to on premises or moving them to another cloud provider is also expensive.

High-performance computing is already complex and challenging, so it¡¯s no surprise that the storage environment required to support it can be, too. Complex workloads, high data volume in the exabyte range, data security requirements, integrations, and data tiering all make navigating HPC complicated business. However, solutions that offer both robust capabilities and ease of use, like Everpure FlashBlade, can handle and even offset that complexity without adding bottlenecks or delays.

HPC storage may not always be the most cost-effective solution for every system or network as not all workloads require storage specifically tuned for HPC challenges. However, as more workloads such as AI become commonplace in the enterprise, the same performance and scalability demanded of HPC storage could end up being more universally beneficial.

HPC storage is meant to meet the unique demands of large-scale computational tasks, simulations, and data-intensive applications, but not all workloads will require that speed and scale, and they may have other unique requirements. It¡¯s important to weigh the pros and cons, but in general, HPC storage is good for:

• Massive data sets and complex workloads
• Performance to support parallel processing and rapid data access
• Expected data growth
• Tight integrations with compute clusters

FlashBlade is used by more than 25% of Fortune 100 companies for its simplicity, agility, and ability to:

• Maximize utilization of GPUs and CPUs.
• Drive massive IOPS, throughput with high concurrency and low latency without compromising multidimensional performance.
• Support tens of billions of files and objects with maximum performance and rich data services.
• Leverage automated APIs and high-performance native NFS, SMB, and S3 protocol support to make deployments, management, and upgrades hassle-free.