“Rack ‘em and stack ‘em.”— a winning approach for a long time but not without its limitations. A generalized server solution works best when the applications running on those servers have generalized needs.
Enter “Big Data.” Today’s application and workload environments can be required to process massive amounts of granular data and, thus, often consist of applications that place high demands on different server hardware elements. Some applications are very compute intensive and place a high demand on the server’s CPU where others in the same environment are tasked with unique processing requirements performed on specialized graphical processing units (GPUs).
Whether it is customer, demographic, seismic data — or a whole host of other uses — the number crunching and processing required across the suite of applications can result in processing demands that are radically different from demands of prior years. Enter Hybrid High Performance Computing. These systems are built to serve two masters: CPU-intensive applications and GPU-intensive applications delivering a hybrid environment where workloads can be optimized and run-times reduced through ideal resource utilization.
The results of Hybrid CPU/GPU Computing adoption have been impressive. Just a few examples of how Hybrid CPU/GPU Computing is delivering real value include:
You can learn more about leveraging hybrid CPU/GPU computing in this whitepaper.