Xilinx (United States) - As big data pushes the need for high-performance computing (HPC) toward the exascale threshold, the pressure is on to find computing architectures that meet the right mix of price, performance, and power efficiency to support:

• Cost-effective data center scalability:
• Accelerated applications that drive higher end-user productivity and faster time to insights
• Lower power consumption

The datacenter landscape has changed dramatically in the last decade and one thing is clear: CPUs and GPUs alone can no longer meet the ever-growing demands of big-data-driven workloads.

Join us for a panel discussion with real-world examples of how compute-intensive workloads such as computer-aided engineering, massive streaming sensor data, and graph database analytics benefit from new HPC clustering methodologies to cost-effectively deliver higher levels of scale, power efficiency, and speed to insights and science.

Featured Panelists:

• Nathan Chang - HPC Product Manager, Xilinx Data Center Group
• Bob Lucas - Distinguished Engineer, Ansys
• Dan Eaton - Director of Business Development, TigerGraph
• Vinod Kathail - Chief Architect, Xilinx