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:
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.
HPC Product Manager, Xilinx Data Center Group
Distinguished Engineer, Ansys
Director of Business Development, TigerGraph
Chief Architect, Xilinx