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High-Performance Computing (HPC) And Artificial Intelligence (AI) – MarkTechPost

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A desktop or a laptop with a 3 GHz processor can perform about 3 billion (10^9)calculations per second. Although this is much faster than what a human can do, it pales in comparison to High-Performance computing solutions, which can perform quadrillions (10^15) of calculations per second.
High-Performance Computing (HPC) refers to the aggregation of computing power in ways that provide far more processing power than traditional computers and servers. It involves the ability to perform large-scale calculations to solve complex problems that require processing large amounts of data.
A key principle of HPC is the ability to run code massively in parallel to benefit from large-scale runtime acceleration. HPC systems can sometimes reach very large sizes because applications are accelerated when processes are parallelized, that is when more computing cores are added. A typical HPC capability is around 100,000 cores. 
Supercomputers are one of the best-known types of HPC solutions. A supercomputer contains thousands of computing nodes that work together to perform one or more tasks. It’s like connecting thousands of PCs and combining their computing power to process tasks faster.
Most HPC applications are complex tasks that require processors to exchange their results. HPC systems require extremely fast storage and communication systems with low latency and high bandwidth (> 100 Gb/s) between processors and associated storage.
A standard computer solves problems by dividing the workload into a series of tasks and executing it one after the other on the same processor. On the contrary, a High-Performance Computing system leverages massive parallel computing, computer clusters, and high-performance components.
Parallel Computing
In Parallel Computing, multiple tasks run simultaneously on multiple computer processors or servers. In HPC, parallel computing is done using millions of processors or processor cores.
Computer Clusters
A High-Performance Computing cluster consists of numerous high-speed servers networked together with a centralized scheduler managing parallel workloads. The Computers, called nodes, use high-performance multi-core CPUs or, more likely Graphical Processing Units (GPUs), which are well-suited for rigorous mathematical computations, graphics-intensive tasks, and machine-learning models. A single HPC cluster can contain over 100,000 such nodes.
High-Performance Components
The other components in an HPC cluster like networking, storage, memory, and file system are high-speed and low-latency components that can optimize and improve the performance of the cluster.
AI can be used in High-Performance Computing to augment the analysis of datasets and produce faster results at the same accuracy level. The implementation of HPC and AI requires similar architectures – both achieve results by processing large data sets that typically grow in size using high computing and storage capacity, large bandwidth, and high-bandwidth fabrics. The following HPC use cases can benefit from AI capabilities.
The integration of HPC and AI, however, requires a few changes to workload tools and management. Following are a few ways High-Performance Computing is evolving to address the challenges associated with integrating AI and HPC.
Programming Languages
HPC programs are generally written in languages like C, C++, and Fortran, and the extensions and libraries of these languages support the processes of HPC. AI, on the other hand, relies on python and Julia.
For HPC and AI to use the same infrastructure, the software and interface should be compatible. In most cases, AI frameworks and languages are overlaid on existing software to allow both sets of programmers to continue using their current tool without migrating to a different language.
Virtualization and Containers
Containers allow the developers to adapt their infrastructure to the changing work needs and enable them to deploy the same consistently. Containers allow the Julia and Python applications to be more scalable.
With containers, teams can create HPC configurations that are quick and easy to deploy without time-consuming configuration.
Increased Memory
Big data plays a significant role in AI, and the data sets are ever-increasing. The collection and processing of these datasets require a large amount of memory to maintain the efficiency and speed that HPC can provide. HPC systems tackle this problem with technologies that support a large amount of persistent and ephemeral RAM.
HPC can manage and scale large and complex datasets and is useful for healthcare computing operations.
To boost a machine’s performance, engineers first build and test a rather expensive prototype. To work around this issue, massive computer simulations mimic real-world variables like wind, heat, gravity, and even chaos.
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I am a Civil Engineering Graduate (2022) from Jamia Millia Islamia, New Delhi, and I have a keen interest in Data Science, especially Neural Networks and their application in various areas.

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