Pervasive connectivity and new usage models are creating an explosion of data, and emerging applications such as real-time analytics, in-memory data bases, financial services, ad serving, and genome sequencing are all demanding instant access to exceedingly large sets of information. These new paradigms of accessing data have caused traditional server and data center architectures to fall out of balance, resulting in low CPU utilization due to a shortage of critical resources such as memory capacity. In some cases, the imbalance between processor cores and memory capacities results in servers running out memory capacity long before the CPU utilization ever becomes an issue. As a result, the CPU is forced to access the disk subsystem in order to access increasingly large data sets, requiring it to cross the large latency and bandwidth gap between memory and storage when processing these large data sets.  The result is decreased system performance and power efficiency, and increased Total Cost of Ownership (TCO).

In order to investigate ways to address these challenges, we have created the SDA engine and SDA platform which include software, firmware, FPGAs and large amounts of memory that can be harnessed in different ways to meet varying application needs. The SDA engine combines an FPGA and 24 DIMM sockets into a building block that places flexible computing capabilities next to high memory capacity. At the platform level, multiple SDA engines can be aggregated together into a solution that scales compute and memory capacity as needed by applications. The platform is being used to investigate system architectures that optimize data movement, enhance system performance, and improve power efficiency.