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Parallel processing capabilities are highly sought after, and admins can apply the technology across many industry sectors. With a data center designed to handle this workload type, companies can derive insights from massive amounts of data -- and fast. But to upgrade any data center infrastructure to support parallel processing use cases brings serious cost, power and rack space considerations.
Parallel processing is a computing technique often used in high-performance computing (HPC). It is a type of computation that takes a complex task and breaks it down into many smaller tasks, which then run on at least two separate processors. These tasks are distributed to and processed on individual machines working in parallel; once those tasks are completed, the results are combined at the end.
Parallel processing is often confused with concurrent processing, but with concurrent processing, the distributed tasks are unrelated. The easiest way to think of parallel processing is to imagine a single, large, very complex problem.
To solve such a problem, it is easier to break computations into smaller pieces for multiple machines to simultaneously solve rather than task one machine to solve the entire problem by itself. Even the most powerful supercomputer could not compete with many less powerful supercomputers working in tandem on the same problem.
The positives of parallel processing
Processing speed is the primary benefit of parallel processing. Today, many organizations demand real-time data analysis. Previously, intense data crunching and hardware limitations made such analysis either time-consuming or expensive.
Parallel processing with HPC can reduce the amount of time required to process large data sets and can handle larger data sets at a more regular cadence. With the right data center infrastructure, organizations can use parallel processing to benefit their operations.
Certain sectors include:
- Retail. Parallel processing can help create predictive models based on consumer behavior data collected over dozens of years.
- Financial services. Firms can apply the technology to track stock trends in real time, which helps to automate trading.
- Engineering. Companies can use HPC to design more accurate and realistic prototype designs based on data collected from products in the field and incorporate end-user feedback.
- Healthcare. Doctors could examine data insights to develop cures for diseases and make faster, more accurate patient diagnoses.
- Automotive. The artificial intelligence in autonomous vehicles collects continuous streams of data from its surroundings, and faster data analysis can inform the operation and function of the vehicle, which makes for safer rides.
Before any organization decides to invest in a hardware upgrade to support parallel processing, IT teams and managers should determine if such a computing model and its supporting software can help meet the desired business needs from infrastructure parallelization.
Ways to upgrade infrastructure
To support parallel processing, an organization must build a data center that contains many compute servers networked together in a cluster.
To deliver this level of HPC, the infrastructure requires hundreds -- or thousands -- of nodes working together, and each must keep compute, network and storage in pace with one another for maximum performance and speed.
The standard server architecture requires some upgrades to handle the level of processing power required to run these complex compute operations in parallel. More organizations are now exploring the use of GPUs in the data center to support such parallel processing capabilities.
To support parallel processing in the data center, organizations should:
- Build out available server nodes
- Increase rack densities
- Supply more power per rack
- Implement more sophisticated cooling systems, such as liquid cooling
These basic requirements also equate a need for more facility space to ensure efficient operations and reduce the possibility of overheating any infrastructure.
However, not many organizations can afford the price tag that comes with higher-density equipment acquisition, let alone data center expansion and the hiring of experienced personnel. This is why some companies choose to partner with colocation data centers that specifically support HPC workloads.
This is a much cheaper and easier way of getting access to higher levels of processing and computing power. If an organization doesn't have the resources to transform its data center to support parallel processing, then working with a colocation center may be the ideal option.
If an organization upgrades its own hardware, then IT admins must also factor in software, which is an important part of the equation.
Admins must have software whose architecture can take a complex problem, break it down into small parts, distribute it to the servers and then combine the results to find the solution to the initial problem seamlessly.
The software evaluation process should also account for licensing requirements, visibility into parallel processing workloads, hardware configuration features and available data flow frameworks. If the infrastructure uses GPUs, then the software must also have code to effectively take advantage of and activate the processing hardware.