To understand High-Performance (HPC) you need to first understand supercomputers.
A supercomputer is a type of HPC solution which performs at the highest operational rate for computers.
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display(div-gpt-ad-1439400881943-0); }); Unlike traditional computers, supercomputers use parallel processing.
This multitasking capability enables supercomputers to process calculations at a much faster rate.
Supercomputers are typically used for handling large data sets or process-intensive computing.
HPC architectures typically consist of three components: the Compute Cluster, the Network and Data Storage.
In an HPC environment, the servers are connected as a cluster.
All servers in a cluster run software and algorithms simultaneously.
The cluster is then connected to the storage.
An HPC cluster can be formed of thousands of computer servers networked together.
From CPU to GPU: the Challenge of Data Sprawl Data sprawl is the everyday increase in data produced by organizations.
This massive quantity of data also increases in variety since it is produced by many sources.
Complex distribution of files and records also makes data collection a difficult endeavor.
To overcome this challenge, companies use tools that cross-reference multiple data sources.
The number of servers used by companies continues growing.
CPUs get more powerful every year.
However, companies continue adding servers to support the growth of their workloads or to accommodate new workloads.
In addition, application workloads are getting online at an increasingly fast rate.
To keep up with the growth in workloads, organizations are using Graphics Processing Units (GPUs).
A graphics processing unit can perform mathematical calculations much faster than CPUs.
GPUs are mostly used for deep learning and machine learning, as well as for rendering images.
To Cloud or Not to Cloud? Most companies have chosen to migrate their big data operations to the cloud.
Others adopted a hybrid approach, with some workloads on-premise and other workloads in the cloud.
For example, moving batch processing to the cloud while running machine learning on-premises.
Big data is too big to be processed using traditional database techniques.
The cloud provides the scalability and flexibility required to process such large data sets.
Hardware virtualization, for example, enables organizations to scale easily.
This is useful for data-intensive applications.
However, there are some considerations when moving big data operations to the cloud.
Such large and varied amounts of data can be problematic to synch between on-premises data centers and the cloud.
This can affect the I/O performance in the cloud environment.
Before going through with the migration, create a cloud migration strategy.
Supercomputing and Machine Learning: a Perfect Combination Machine learning software can analyze data sets and provide insights and predictions on its own, with minimal human intervention.
Scientists run machine learning techniques on supercomputers, for the purpose of extracting valuable information from complex data sets.
For example, physicists can analyze complex data produced by particle accelerators with the help of machine learning models.
Machine learning helps scientists improve supercomputer systems.
For instance, machine learning is an integral part of the Exascale project—a computer that can solve problems at a rate of a quintillion calculations per second—is expected to be ready by 2021.
Scientists are using machine learning techniques to improve the autotuning of exascale applications.
Autotuning is the process of automatically tuning parameters for an application, and it is critical for processes requiring high scalability.
The application of machine learning in autotuning highly improves the process, as explained in this paper.
Technologies such as deep learning (DL) with neural networks also benefit from the high capabilities of supercomputers.
DL requires scaling tens to thousands of nodes, and neural networks are computationally demanding, especially during image classification tasks.
High-performance computing can provide the needed scalability to quickly perform these tasks.
Benefits of high-performance computing for machine learning.
High-performance computing provides unique advantages for machine learning models, including: Large amounts of floating-point operations (FLOPS)—training neural networks requires a large amount of linear algebra, like floating-point operations, which use mathematical operations that require decimal numbers.
HPC supports floating-point performance.
Low-latency—when training neural networks in traditional server architectures, it can cause problems due to the delays between servers.
HPC is focused on achieving high-bandwidth interconnects, thus achieving low-latency.
Parallel I/O—neural networks use systems that can provide parallel I/O capabilities.
HPC can perform multiple input/output operations at the same time with high-performance.
The Bottom Line Machine learning and deep learning technologies will require scaling to larger node counts in the future.
This change in machine learning and deep learning models will require the utilization of supercomputers, to accommodate the need for high-performance and scalable environments.
Whether this transition includes a jump to the cloud or not, it is still uncertain.
There are many benefits of managing big data and machine learning in the cloud, but the constraints will likely drive companies to hybrid environments.
About the Author Gilad David Maayan is a technology writer who has worked with over 150 technology companies including SAP, Oracle, Zend, CheckPoint and Ixia, producing technical and thought leadership content that elucidates technical solutions for developers and IT leadership.
Gilad holds a B.
Sc.
in Economics from Tel Aviv University, and has a keen interest in psychology, Jewish spirituality, practical philosophy and their connection to business, innovation and technology.
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