Introduction to GPUs for Data Analytics

Introduction

After decades of achieving steady gains in price and performance, Moore’s Law has finally run its course for CPUs. The reason is simple: the number of x86 cores that can be placed cost-effectively on a single chip has reached a practical limit, and the smaller geometries needed to reach higher densities are expected to remain prohibitively expensive for most applications.

This limit has given rise to the use of server farms and clusters to scale both private and public cloud infrastructures. But such brute force scaling is also expensive, and it threatens to exhaust the finite space, power, and cooling resources available in data centers.

Fortunately, for database, big data analytics, and machine learning applications, there is now a more capable and cost-effective alternative for scaling compute performance: the graphics processing unit, or GPU. GPUs are proven in practice in a wide variety of applications, and advances in their design have now made them ideal for keeping pace with the relentless growth in the volume, variety, and velocity of data confronting organizations today.

The purpose of this book is to provide an educational overview of how advances in accelerated computing technology are being put to use addressing current and future database and big data analytics challenges. The content is intended for technology executives and professionals, but it is also suitable for business analysts and data scientists.

The ebook is organized into eight chapters:

• Chapter 1, The Evolution of Data Analytics provides historical context leading to today’s biggest challenge: the shifting bottleneck from memory I/O to compute.

• Chapter 2, GPUs: A Breakthrough Technology describes how graphics processing units overcome the compute-bound limitation to enable continued price and performance gains.

• Chapter 3, New Possibilities highlights the many database and data analytics applications that stand to benefit from GPU acceleration.

• Chapter 4, Machine Learning and Deep Learning explains how GPU databases with user-defined functions simplify and accelerate the machine learning/deep learning pipeline.

• Chapter 5, The Internet of Things and Real-Time Data Analytics describes how GPU-accelerated databases can process streaming data from the Internet of Things and other sources in real time.

• Chapter 6, Interactive Location-Based Intelligence explores the performance advantage GPU databases afford in demanding geospatial applications.

• Chapter 7, Cognitive Computing: The Future of Analytics provides a vision of how even this, the most compute-intensive application currently imaginable, is now within reach using GPUs.

• Chapter 8, Getting Started outlines how organizations can begin implementing GPU-accelerated solutions on-premise and in public, private, and hybrid cloud architectures.