In-memory computing for scalable data analytics

Abstract

Current data analytics software stacks are tailored to use large number of commodity machines in clusters, with each machine containing a small amount of memory. Thus, significant effort is made in these stacks to partition the data into small chunks, and process these chunks in parallel. Recent advances in memory technology now promise the availability of machines with the amount of memory increased by two or more orders of magnitude. For example, The Machine [1] currently under development at HP Labs plans to use memristor, a new type of non-volatile random access memory with much larger memory density at access speed comparable to today's dynamic random access memory. Such technologies offer the possibility of a flat memory/storage hierarchy, in-memory data processing and instant persistence of intermediate and final processing results. Photonic fabrics provide large communication bandwidth to move large volume of data between processing units at very low latency. Moreover, the multicore architectures adopt system-on-chip (SoC) designs to achieve significant compute performance with high power-efficiency.