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From Clouds to Exascale: Understanding the Future of Scalable Data Analysis

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From Clouds to Exascale: Understanding the Future of Scalable Data Analysis

Exascale computing: the way of the not-too-distant future.

· Big Data Zone ·
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Before we transition directly to the concept of Exascale computing, it would only be appropriate to understand a bit more about scalability— especially from the perspective of big data, machine learning, and other analytical realms. To be exact, applications and diverse computing frameworks need to work with massive data sets on a daily basis, including chunks of real-time insights from social media platforms, sensor networks, data repositories, and more. The massive surge, therefore, calls for scalable solutions, including ones concerning High Performance Computing. This is where we cross paths with Exascale Systems, as at present, they are the only capable structures for handling extreme-scale big data analysis.

Introduction to Exascale Computing

Needless to say, a discussion regarding Exascale Computing would clearly suggest that the existing technology will be able to solve diverse problems associated with limiting prowess of sequential machines. Exascale systems will be more like high-performance entities that would be capable of supporting almost a billion billion intricate calculations or rather one exaFLOPS, at least. This suggests that any possible implementation would make way for the newest breed of supercomputers. Moreover, what needs to be understood is that although the large-scale designing and even development of Exascale machines are distant realities, developers are well on track with the goal of creating a large number of high-performance devices, going towards 2020.

Why Exascale Computing Is Necessary

Although cloud computing, in its entirety, is a massively powerful tool, in regard to storing high volumes of actionable and raw data, it falters when it comes to extracting higher performance out of insights. This means cloud computing is partially inefficient when it comes to processing large quantities of data at enviable speeds. This is why Exascale systems are desirable, as they would help mitigate the issues proliferating both at the software and hardware levels of computing.

With that said, in the next few years, it is expected that the Exascale Infrastructure would be the most sought-after platform, in regard to handling big data analytics, mining algorithms, applications, and even tools pertaining to the most elusive data discovery realms.

Data Analysis: Cloud vs. Exascale

Quite clearly, clouds make room for elastic services, which eventually stand for scalable storage and commendable performances. Be it rendering functionality to peer-to-peer networks, web systems, and other areas, the role of cloud computing is far from obsolete, as this technology works in cohesion with diverse data repositories for analyzing insights with efficiency and minimal levels of latency. Moreover, what’s essential to take a note of is that cloud-based structures have adopted SaaS, IaaS, and PaaS models for actually implementing analytical solutions. This, eventually, has made way for DAaaS or Data Analysis as a Service — based on the three existing cloud models.

Needless to say, data analysis has been efficiently dealt with by the cloud infrastructure. However, it has been seen that cloud models falter minimally when it comes to designing extremely scalable solutions that are fit to exploit the ever-increasing data arena. Based on estimates released by IDC, approximately 45 zettabytes of data is expected to be generated by 2020, and cloud computing would be sufficient to process the same and provide analytical solutions. Exascale solutions, therefore, can sort these issues out by implementing best-in-class scalable solutions, featuring fine-grained parallel Exascale models for the job. This would include better load balancing, failure handling, distributed memory, and finer programming constructs. Moreover, even the Exascale technology wouldn’t necessarily be one-dimensional and would support the creation of scalable models on the basis of abstraction and formalism levels.

Practical Applications

With the world moving rapidly towards Blockchain and cryptocurrency as go-to, popular technological ideas, it is only a matter of time that massive data sets are procured, in regard to facilitating ICOs, diverse startups, and even trading platforms. While some of the best trading software solutions do rely on the intuitiveness of cloud computing, the adoption of Exascale computing would certainly play a pivotal role in increasing the existing efficacy and even the safety standards associated with trading.

Inference

Although we are on the verge of making way for powerful Exascale solutions for seamless data analysis, we need to be wary of network latency, initial reliability, and overall resilience before moving ahead with large-scale adoption. That said, Exascale computing is the future, and it can easily strengthen the likes of AI intuitiveness, precisely for spurring innovation and accelerating discovery.

Topics:
big data ,exascale ,cloud ,computing ,high-performance computing ,practical applications

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