GPU-Accelerated Analytics Definition

GPU-accelerated analytics (also known as GPU analytics) harnesses the massive parallelism of a Graphics Processing Unit (GPU) in order to accelerate processing-intensive operations such data science, deep learning, machine learning and other big data applications.

Diagram depicts how GPU Acceleration works to illustrate the benefit of GPU Accelerated Analytics.

 

FAQs

What Is GPU-Accelerated Analytics?

GPU analytics refers to a growing array of applications that require the fundamental capabilities of GPUs in order to efficiently handle big data and deliver a dynamic interactive analytics experience.

GPU-accelerated computing essentially functions by assigning compute-intensive portions of an application to the GPU, providing a supercomputing level of parallelism that bypasses costly, low-level operations employed by mainstream, analytics systems.

Where traditional architectures using CPUs involve a significant hardware footprint and require data scientists to perform downsampling, indexing, and pre-aggregating, GPU-accelerated analytics (GPU Analytics) ingests entire datasets into the system, enabling users to instantly interactively query, visualize, and power data science workflows over billions of records.

 

Big Data Analytics Using GPUs

In the era of growing Artificial Intelligence (AI), machine learning, and big data, incorporating the computing power of GPUs is vital for processing and extracting insights from enormous datasets with lightning speed and accuracy. Big data analytics use cases can be comprised of up to hundreds of billions of records within a single table, often ingesting data at millions of records a second, the majority of which contains spatio-temporal (location-time) data. These attributes represent a huge challenge for legacy systems, which cannot scale without GPUs.

Many big data analytics users are adopting the concept of a data lake architecture, holding vast amounts of raw data in its native format until it is needed -- GPUs provide the support required to render such high-cardinality data with zero latency, a vital feature for such use cases as autonomous vehicles and disaster response. Some features of GPU analytics include:

  • Server-Side Data Rendering: in-situ rendering of on-GPU query results to accelerate the visual rendering of grain-level data
  • Large Scale Rendering of Points and Polygons: a geospatial analysis solution that enables zero-latency pointmap visualization of millions of lines or polygons on a geo-chart
  • Visualization with APIs: a customizable visualization system that combines the agility of a lightweight frontend with the parallel power and rendering capabilities of a GPU engine
  • Advanced Memory Management and GPU Caching: render query results directly on the GPU, removing the slowdowns due to network and GPU-to-CPU transfers

GPU-accelerated analytics is employed throughout industries such as Defense and Intelligence, Telecommunications, Financial Services, Automotive, Utilities, Advertising, Oil and Gas, Logistics, and more.

 

GPU vs CPU for Analytics

A CPU is essentially the brains of a computer while a GPU acts as a specialized microprocessor. CPU to GPU Computing creates an overall faster application by combining the two processing powers. GPUs enhance CPU architecture by accelerating portions of an application while the rest continues to run on the CPU. The CPU remains a reliable processor for general-purpose computing while a GPU accelerated graphics card takes over when intensive computations are needed.

Comprised of large and broad instruction sets, CPUs can manage every input and output of a computer and are more versatile, performing many more kinds of tasks than a GPU. A CPU may contain only 10 to 30 cores, each one faster and smarter than an individual GPU core.
However, where a CPU excels at handling multiple tasks, a GPU is more powerful and can handle a few specific tasks very fast. With as many as 40,000 cores, GPUs offer a massive amount of parallelism, making them perfect for taking on repetitive and highly-parallel computing tasks.

GPU analytics is capable of providing unparalleled support for real-time interactive visualization for very large datasets, enabling deeper insights, dynamic correlation, and delivery of predictive outcomes at ultra-fast speed, accuracy, and scale.

 

Why Use GPU Analytics?

Organizations in nearly every industry are unlocking potential by updating their technology to high performance analytics tools to fully take advantage of the massive amount of data at their fingertips. Modern processing technologies such as GPU analytics offer greater insight, support, and power than traditional systems, and are deployed in a wide variety of use cases:

  • Telecommunications: network reliability analysis
  • Oil & Gas: acquisition and divestment
  • Automotive: understand driver behavior data
  • Investment Management: fraud detection
  • Utilities: smart meter data analysis
  • Pharmaceuticals: clinical trial analysis

 

GPU-Acceleration in Geospatial Analytics

The majority of big data analytics use cases today involve some measure of geospatial data. Telecommunications companies use geospatial data for network and infrastructure planning to maximize coverage for subscribers. Road developers use autonomous vehicle data to measure driver behavior and plan infrastructure accordingly. Unprecedented volumes of geospatial data flow throughout today’s industries requiring the power and support of GPU-accelerated analytics to facilitate unrestricted exploration of data across time and space.

GPU-accelerated analytics platforms enable users to instantly ingest massive amounts of geographic and geometric data types for backend polygon rendering, micro cross-filtering and dynamic real-time geospatial visualizations. Advanced location intelligence for big data facilitates such operations as: chart and map billions of data points from multiple sources with zero latency interactivity, view and gain insight from geospatial data in context, filter big geolocation datasets across time-series graphs. Use cases include:

  • Data Science in Telecommunications: detect network anomalies
  • Big Data in Military: logistics and readiness for military operations
  • Big Data in Government: respond rapidly to extreme weather
  • Actionable Intelligence and Analysis: AI and machine learning for the U.S. Intelligence Community
  • Big Data and Urban Planning/Development: geospatial visualization for urban mapping
  • Location Intelligence for Upstream Exploration: visualization tools for the Energy industry

Does OmniSci Offer GPU Analytics Solutions?

OmniSci is the pioneer in accelerated analytics, providing the tools to harness the massive parallelism of modern CPU and GPU hardware both in the cloud and on-premise. OmniSci helps you interactively query, visualize, and power data science workflows over billions of records with a wide range of accelerated analytics solutions:

  • OmniSciDB: The world’s fastest open source analytics SQL engine.
  • OmniSci Immerse: Visually explore big data at the speed of thought.
  • OmniSci Render: Visually-rich, GPU-rendering and mapping of massive datasets.
  • OmniSci Cloud: Extends accelerated analytics and visualization to the world.