What is an HPC Workload?
An HPC workload is a highly complex, data-intensive task that is spread across compute resources, which each run parts of the task in parallel. An HPC system can run millions of scenarios at once, using terabytes (TB) of data at a time, which helps organizations get to insights faster.
What are the different components of an HPC workload?
Every HPC workload is different and requires varying levels of CPU and reserved memory to complete its tasks, which depend on the effort involved—its duration, intervals, and magnitude. At the most basic level, one workload, or query, gathers input (I) and produces output (O). It can be broken down into the following components:
· Request: The “work” in workload refers to what is being requested of an application. It involves a series of read and write operations (I/O commands) and the associated payload to and from a storage system.
· Application(s) and VMs: Every workload is tied to what is being used to achieve the work, or the ongoing effort of an application. How the application processes the data and what software limits are inherent will shape the characteristics of the workload itself.
· Working set: The volume of data created/consumed during a workload is referred to as a working set. A typical HPC workload consumes massive quantities of data, mostly in unstructured formats. The data used by HPC models is increasing exponentially as scientists and engineers work on fine-tuning the accuracy of their workloads.
· Duty cycle: When a set of processes occurs and then reoccurs, that’s referred to as a duty cycle. The time of approximate repeatability of that effort is highly dependent on who’s consuming the data and the application’s purpose, as well as storage performance.
How do you manage HPC workloads?
A traditional HPC system uses a command line interface (CLI) to manage the submission and management of jobs. The process of managing an HPC workload starts much like any data workload—with identifying and preparing relevant data—followed by submitting the request, then running the application, and collecting and storing the findings generated.
Prepare the data
The accuracy of any HPC workload depends on data hygiene. Organizations need to perform data scrubbing on the data sets to be analyzed to update/remove data that is inaccurate, incomplete, improperly formatted, or duplicated.
Set up data access
While HPC workloads require easy and fast access to data, organizations need to implement policies to deliver data in a secure and efficient manner. The same encryption and access controls are executed across all resources used, whether data lakes, data fabrics, lakehouse architectures, or neural networks.
Selecting the algorithms to use and then building, training, and deploying analytical models requires extensive expertise and should be defined by the data scientists submitting the requests.
Run the queries
Many applications are often tapped to generate findings in HPC. Distributed computing software platforms, such as Apache Hadoop, Databricks, and Cloudera, are used to split up and organize such complex analytics.
What are the different types of HPC workloads?
There are several categories of HPC workloads, which look at enormous amounts of data, searching for trends, making predictions, and generating recommended adjustments to operations or relationships.
At its simplest, artificial intelligence (AI) is where machines simulate human intelligence when processing information. It focuses on the cognitive skills humans use every day to navigate billions of decisions a day, including learning, reasoning, and self-correction. Learning itself involves taking input data and creating rules to turn it into actionable information. Reasoning involves determining the right algorithm to use to achieve a desired outcome. Self-correction is the single most valuable part of the AI process, where each decision helps to fine-tune the algorithms on an ongoing basis.
A type of artificial intelligence, machine learning (ML) uses algorithms to become increasingly accurate at predicting outcomes. The most common use of ML is the recommendation engine that powers media organizations, such as Netflix, Spotify, Facebook, etc. Other uses include customer relationship management systems, business intelligence, virtual assistants, human resources information systems, and self-driving cars.
This is a subset of machine learning and refers to the automation of the predictive analytics in ML. It uses layers of information processing, building more sophisticated understanding with each layer and gradually learning more complex information about a data set. Typical use cases include self-driving cars, where the supercomputer under the hood builds automated skills at piloting the vehicle.
How do HPC workloads function in cloud environments?
The cloud is an ideal platform for HPC, because by moving HPC workloads to the cloud, an organization can take advantage of almost limitless computing and services on demand. That means you can use as many resources as needed for a single workload and then release the resources when it’s complete.
In addition, you can assemble an infrastructure of cloud-based compute instances and storage resources, managing as many as hundreds of thousands of servers spread across a fleet of global data centers. This allows data and processing activity to take place close to where the big data task is located or in a certain region of a cloud provider. The infrastructure and software services are on the cloud, and users can assemble the infrastructure for a big data project of almost any size.
The main benefit to running an HPC system in the cloud is that resources can be added/removed as needed dynamically and in real time. Being able to scale so quickly eliminates the problem of capacity bottlenecks and allows customers to right-size their infrastructure to match workloads more precisely. And with the underlying infrastructure served up via cloud, users are able to process more workloads with fewer staff, leading to cost savings as well as more staff time freed up for higher business value tasks.
HPE and HPC workloads
HPE offers the most comprehensive software portfolio for HPC and converged workflows in the market. And our extensive array of hardware includes solutions with more flexibility to open up AI, ML, and other HPC techniques, as well as scalable, high-performance storage and interconnection technologies that are unmatched in the industry. These systems include HPE Apollo, Slingshot, and our Parallel Storage, which deliver unprecedented throughput and GPU enhancements.
HPE Pointnext Services deliver and support a complete range of solutions and consumption models for HPC and converged workflows. We also manage and optimize the entire solution, aligned with HPE’s best-practice technology, to meet your organization’s HPC requirements.
HPE GreenLake for HPC is an on-premises, end-to-end solution for HPC applications, which has been designed to deliver incredible industry-leading performance, with no need for your teams to spend time integrating and tuning components. It makes it easier and faster for you to deploy HPC and AI workloads and allows end users, developers, and data scientists to run pure HPC, pure AI, and converged HPC/AI workflows on high-performance clusters, leveraging the full HPE GreenLake customer experience.