Last updated on February 6th, 2023

Manufacturing, Education, Transportation, Logistics – name any industry today and it can be reasonably demonstrated to have experienced a remarkable leap in productivity stemming from the influx of data analysis and automation. Even agriculture and animal husbandry! Data sciences are unarguably transformational and have become the key to unlocking every economic sector’s untapped potential. Potential that has remained camouflaged under enormous reams of datasets for decades.

Data science can be customized to forecast customer needs, detect financial fraud, offer personalized search and eCommerce recommendations, evaluate market risks, facilitate drug discoveries, and so on – the possibilities are endless. It is estimated that 2500 million GB data was being generated across the world per day in 2020! 2,500,000,000 GB! No wonder a research report predicts that the data analytics market will grow to more than 300 billion US Dollars by 2030.

Businesses have come to rely on customer analytics, predictive analytics, market analytics, and various other kinds of data-rich internal and external studies for optimizing their investments and marketing strategies. Tech-savvy political parties across the world have begun including insights gleaned from demographics analytics in their general scheme of electoral campaigning.

It is evident that as the demand for data analytics explodes across sectors, so will the underlying demand for concise, actionable 360-degree data of varying complexities emanating from different sources. And with exponential proliferation of data, the clamour for robust systems that can undertake high-performance processing will also increase.

Data scientists & Data analysts across enterprises and institutions now understand that it is exceedingly cumbersome to rely only on CPUs for big data analytics. Be it sieving through and sorting colossal datasets, or training and deploying Artificial Intelligence/ Machine Learning (AI/ ML) models based on billions of data points with endless interdependencies between them, CPUs just cannot cope. For accelerated data analytics and real-time computation for prediction and inference applications, enterprises must leverage powerful GPUs, whether on-premise or through Cloud.

This article delves into GPU-accelerated Data Analytics and will also discuss how Nvidia RAPIDS can magnify data analytics operations by leveraging several powerful Python libraries.

What is GPU-accelerated Data Analytics?

GPU-accelerated Data Analytics leverages high-end GPU cores and parallel processing to deliver a dynamic and interactive analytics experience. It employs the parallel computing architecture of GPUs to accelerate compute-intensive tasks required for data science-related operations and ML model training. In more ways than one, accelerated analytics with GPUs such as Nvidia A30, A100 and A6000 maximize productivity, reduce cost, and minimize the time to deliver analyses and conclusions. And in industries with cut-throat competition, capitalizing on information first can deliver unsurmountable leads.

GPU-accelerated Data Analytics expedite seamless ML/ DL model refactoring. Analysts and Data scientists can dynamically scale their existing data science toolchain without fretting over the burgeoning size of their datasets or the numerous changes they must continuously introduce in the model in response to more variables and/ or previously unaccounted/ unforeseen developments. More tools can be incorporated where necessary, and the model can be rapidly scaled to facilitate this incorporation.

A fascinating AI/ ML use case is Natural Language Processing – training computer systems to understand and decipher human languages. NLP modules require highly complex training algorithms. GPU-accelerated Data Analytics can support this by processing large unstructured datasets such as Wildcards, Exact phrases, Fuzzy search, Lexical grouping, etc.

Enterprises have come to lean on Cloud GPUs for repetitive ingestion of high volume and velocity of datasets as well as for dynamic analysis of trends. Cloud GPUs provided by Ace Cloud Hosting offer high scalability and extensive processing capabilities for accelerated data analysis.

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How Does GPU Accelerate Data Analysis and Data Science Operations?

GPUs are powerful processing units explicitly built for high-end graphics rendering and parallel processing. The functioning of a GPU is streamlined by dedicated VRAM or Video-RAM that is soldered into it at the time of manufacturing. It is very efficient and can support complex processing with minimal latency. Not only can GPUs handle sophisticated display vectors, textures and realistic imagery, but also algorithms leveraging massive datasets.

Typically, CPUs undertake sequential processing. Imagine a CPU with two cores and eight tasks to perform. The two cores will simultaneously complete one task each at a time and thus continue with all the tasks to achieve multitasking.

GPUs, on the other hand, often contain hundreds, if not thousands, of cores. All the cores dedicatedly work on similar tasks, with complete optimization for performing repeated instructions in parallel. Since Data Science and Data Analysis deal with tons of repetitive data processing in succession, GPUs best fit the purpose. A GPU can analyze massive chunks of data in real-time without any lag whatsoever.

Nvidia GPUs have been demonstrated to be doing wonders in accelerated Data Analytics and are even now scaling their computational power over time. Nvidia A100 GPU can ordinarily perform operations at a speed of 9.7 TeraFLOPS (Floating Point Operations Per Second) and achieve as much as 312 TFLOPS under certain conditions.

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TFLOPS comparison Intel x86 CPU vs. Nvidia GPUs across generations. Whereas V100 GPU operates at 7.8 TFLOPS, the more advanced A100-80GB GPUs can achieve 9.7 TFLOPS.

Data Science and ML model training based on tremendously large datasets are no different from other monotonously repetitive tasks. Obviously, data analysis tools and training algorithms deployed on powerful CPUs or low-end GPUs won’t even deliver the bare minimum performance requirement. Datasets upwards of 100 GB harbor data points with millions, if not billions, interrelationships and edges. Many such datasets necessitate GNN applications for decipherment and eventual analysis. Analysts and Data scientists must fall back on the massive parallel processing of modern GPUs in order to process such colossal datasets.

Benefits of Using Nvidia GPUs for Accelerated Analytics

In cut-throat competitive spheres, streamlined data processing capabilities and easy access to business solutions drawn from wide-ranging datasets are prerequisites for enterprises. Accelerated Analytics with Nvidia GPUs deliver these and other notable benefits –

  • Nvidia GPUs can help accelerate interactive analytics and high data-driven predictions up to 215x faster with more iteration. It can ease the number of experimentation and algorithm training sessions needed and enable deeper use case exploration.
  • High-performance GPUs reduce waiting time for rendering analytics and visualization. Less time for processing and testing equals prompt deployment of business solutions.
  • Besides unbelievable speeds, Nvidia GPU-accelerated Analytics also provides remarkable accuracy even with multi-TB data environments and billions of variables.
  • Vis-a-vis on-premise GPU infrastructure and networking deployment, accessing Cloud GPUs services minimize infrastructure costs, magnify scaling capabilities, improve data center efficiency, and are inherently environment-friendly.

Accelerating GPU-based Data Analytics with Nvidia RAPIDS

RAPIDS is a powerful Open-source software suite offered by Nvidia to enable enterprises to develop highly accelerated, end-to-end data science and analytics pipelines in multi-node, multi-GPU environments. Built on proprietary CUDA primitives, it delivers extraordinary performance by channeling GPU parallelism and high-bandwidth memory speed through user-friendly Python APIs. A unique data science acceleration suite, it can seamlessly integrate with data science libraries allowing professionals to also rely on it for standard data preparation tasks for ML algorithms and analytics.

Furthermore, the RAPIDS suite also has access to several Python-based Opensource APIs and sub-libraries such as –

  1. cuDF API – A Python-based data manipulation library for loading, aggregating, filtering, and otherwise manipulating data.
  2. CuGraph API – A GPU Graph processing library that delivers accelerated graph analytics. It features several well-known graph analytics algorithms such as PageRank besides various similarity metrics that make complex data analysis effortless.
  3. CuML API – A library suite containing various ML algorithms and pre-defined mathematical primitives functions.

RAPIDS can also integrate seamlessly with data visualization libraries like Matplotlib and Seaborn to generate analytics-based charts and graphs within minutes.

How Can Enterprises Deploy GPU-accelerated Analytics?

The numerous advantages that GPUs deliver in Data analytics, AI/ ML Algorithm training and model development are self-evident. Data science & Analytics companies prefer Nvidia GPUs over AMD and Intel primarily because Nvidia offers numerous integrations, support libraries, suites (such as RAPIDS) and software drivers. These not only deliver a complete GPU utilization environment for Data analytics, but also make the primary setup and preparation effortless. Once the user/ company plugs in Nvidia GPUs and installs the essential drivers with APIs, they can immediately start working on extensive Data analytics and Data science operations.

But if your enterprise relies on Big Data analytics, or requires dynamic real-time or interactive analytics, industry experts recommend Cloud GPUs. These can deliver highly accelerated data analytics and high-bandwidth memory, besides allowing scaling up or down as required in line with market conditions and developments. Relying on CPUs or even on-prem GPUs has become a relic of the past.

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