Summary: This interview discusses the operational and customer experience enhancements to satellite imagery products resulting from investments in GPU computing architecture.
Geospatial technologies, and therefore geospatial solutions (especially those dealing with large imagery files) have always been burdened by high computing challenges. These challenges have translated into latency issues. Over the last several years, DigitalGlobe has invested in GPU technology to accelerate speed to market in delivering imagery products and services. We spoke with Jason Bucholtz about how DigitalGlobe is using GPU technology to improve operations and create a technology infrastructure to support next-gen imagery-based applications. We also spoke with Kevin Berce of NVIDIA, a leader in GPU technology, on the future of advanced computer processing power.
LBx: What is your focus at DigitalGlobe?
Jason: Iím an architect who deals with data. Iím interested in and responsible for determining how quickly raw data can be turned into information. Imagery that hits the ground from the satellite is ìBig Data;î it needs to be processed, and the faster it is processed, the faster organizations have access to the data to answer all sorts of questions about our changing world, and to respond to emergency situations and save lives.
LBx: How do you achieve faster processing?
Jason: DigitalGlobe invested quite a bit over the last several years in GPU (Graphic Processing Unit) technology. GPUs are high-performance computing processors that accelerate image processing applications to enable more, higher quality and faster work. GPUís enhance traditional high performance computing architectures, by breaking down complex instructions into simpler steps and enabling more simple steps to be executed at the same time. This simplification dramatically increases computational speed. Our goal is to achieve faster processing of pixels. GPU technology has primarily been used in the video gaming sector, and is incredibly well suited to geospatial imagery files.
LBx: Kevin, how would you describe GPUs in a nutshell?
Kevin: GPUs are being used to power everything from supercomputers to smartphones. Technically speaking, GPUs excel at parallel processing, and that makes them ideal to accelerate image processing and other computationally intensive applications. The goal of employing GPU technology is to reduce the processing time on algorithms (improved customer experience) and reduce the amount of computer rack space, which reduces the sheer number of systems required (reduced operational costs).
Jason Bucholtz has been designing and implementing large scale high-performance compute systems and large data infrastructures for 15 years. At DigitalGlobe, Jason designs the next generation compute and storage systems that power the companyís leadership in automated image processing.
Kevin Berce has more than 15 years of experience in High Performance computing as Director of sales, business development and other customer-facing roles in the United States. Kevin is currently Manager of Business Development for NVIDIA. In his current role, he has responsibility for NVIDIAís Defense and Intelligence business inside the United States.
LBx: What is driving GPU adoption?
Kevin: There are lots of drivers, including the need to increase raw performance of computationally intensive applications, the need to do more processing with less hardware and power, and enabling greater mobility. Big Data and graphically complex video games are driving the need for more horsepower to run analytics and simulations faster. Relative to geospatial applications and the federal government, GPUs are being used to accelerate the processing of Big Data and analytics in the cloud. The issue with Big Data is that the massive volumes of source data canít be processed quickly enough with typical CPU-only computing solutions.
LBx: What does the faster processing get you?
Jason: It lowers our operating costs for one, and most importantly, it delivers faster analysis to our customers. With GPU computing we can deliver a meaningful geographic image from the tasking of the satellite to delivery to the customer within as little as 120 minutes. That may still seem like a long time in a real-time tweeting world, but that is amazing speed given the complexity of satellite imagery processing workflows. Feature extraction takes more time, but with advanced algorithms we can identify objects on the ground faster ñ for example, cars in parking lots.
GPUs enable us to speed up common preprocessing steps such as orthorectification and pan sharpening. See Figure 1. Orthorectification is the process of tying a pixel to a known location on the earth to render an accurate geographic reference from an image. Pan sharpening merges high-resolution black and white images with lower-precision color bands and fills in the color for a precise high-resolution color image. Intermediate imagery products that include feature extraction and the type of analysis I mentioned earlier all depend on this base-level processing. And thatís the heavy lifting that is performed extremely well by GPUs.
Kevin: Speed gains depend on the application, but can be as much as 20x or more. Cost savings on the reduced hardware footprint can be as much ...
Full article available in LBx Journal Winter 2012 digital edition.