Hardware technologies for high-performance data-intensive computing

Data-intensive problems challenge conventional computing architectures with demanding CPU, memory, and I/O requirements. Using benchmarks that draw on three data types - scientific imagery, unstructured text, and semantic graphs representing networks of relationships - the authors demonstrate that emerging hardware technologies to augment traditional microprocessor-based computing systems can deliver 2 to 17 times the performance of general-purpose computers on a wide range of data-intensive applications by increasing compute cycles and bandwidth and reducing latency.