GPU Trends: The Quest for Performance, Latency, and Flexibility (.PDF Download)

For military intelligence, surveillance, and reconnaissance (ISR) applications, such as radar, EO/IR (electro-optic/infrared), or wideband ELINT (electronic intelligence), the ongoing problem is how best to handle the expanding “firehose” of data, fed by an increasing number of wide-bandwidth platform sensors.  To handle this massive inflow of data, and the complex algorithms required to process it, state-of-the-art computational engines and data-transport mechanisms are essential.

Deployed High Performance Embedded Computer (HPEC) systems designed to support these applications typically have a heterogeneous architecture of high-performance FPGAs, GPUs, and digital signal processors, or DSPs (today, often Intel Xeon-D based modules). GPUs provide a large number of floating-point cores tuned for complex mathematical algorithms, which makes them ideal for processing the complex algorithms used in ISR applications. In comparison, a single Intel Xeon-D processor can provide a peak throughput of ~600 MFLOPS, while NVIDIA’s Pascal P5000 GPU sports 6.4 TFLOPS of peak performance.