When CPUs Just Can’t Keep Up
GPUs Overcome Processing Bottlenecks
A CPU (central processing unit) is the computer’s chief executive: it makes decisions and issues instructions. A GPU (graphics processing unit) is the computer’s assembly line: it does the same thing over and over again very fast, and it has lots of worker bees to do the task simultaneously. You need both, but when you want to absorb, analyze, and visualize massive, complex, streaming data in real-time, you benefit dramatically using the GPU – 100x faster than a CPU.
The Problem With CPUs Alone
Don’t Send a CPU to do a GPU’s Job
What You Can Do With A GPU
GPUs process data up to 100x faster than CPUs, making it possible to ingest, analyze, and visualize large, complex, and streaming data in real-time.
Scale at Speed
GPUs scale up or out to increase performance incrementally and predictably as needed. Memory and cores work in parallel to process data at unprecedented speed.
GPUs are designed to complement or integrate with existing applications and platforms to perform sophisticated analytics. GPUs deploy on premises, in the cloud, or a hybrid, and are available in servers, supercomputers, and cloud platforms around the world.
A GPU has a massively parallel architecture consisting of thousands of smaller, more efficient cores designed to handle multiple tasks simultaneously, ideal for accelerating processing-intensive data analysis.
The Mythbusters Demo GPU Versus CPU
The Mythbusters, Adam Savage and Jamie Hyneman, use art and science to demonstrate the power of GPU computing in this minute-and-a-half video.
Analysis & Visualization in Milliseconds
In both benchmark tests and real-world applications, GPU-accelerated solutions have proven their ability to ingest billions of streaming records per minute and perform complex calculations and visualizations in mere milliseconds.
Multiple GPU cards can be placed in a single server, and multiple servers can be configured in a cluster. Scaling like this results in more cores and more memory all working simultaneously and massively in parallel in order to process data at unprecedented speed.
For hardware, virtually all GPU-based solutions operate on commonly used industry-standard servers equipped with x86 CPUs, enabling the configuration to be scaled cost effectively both up and out to achieve the desired performance.
Artificial Intelligence or Machine Learning Workloads
GPU-based solutions run with applications that benefit from higher performance, like artificial intelligence, including machine learning and deep learning, and real-time analysis of streaming data—common in the IoT.