Maximizing Data Analytics Price/Performance... with GPU Acceleration
After 50 years of achieving steady gains in price/performance, Moore’s Law has finally run its course for CPUs, where the number of x86 cores that can be placed on a single IC has reached a practical limit. This limit has given rise to the use of server farms or clusters to scale both private and public cloud infrastructures.
But such brute force scaling is expensive, and threatens to exhaust the finite space, power and cooling resources available in many data centers.
Fortunately, for database and big data analytics applications there is now a more capable and cost-effective alternative for scaling performance: the Graphics Processing Unit. 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 and velocity of data confronting organizations today.
This white paper is intended for both technical and business decision-makers. This paper covers how GPUs have become such a disruptive technology, it provides an overiew of Kineticas GPU-accelerated architecture, and then highlights benchmarks and real-world experiences.