Emerging memories as enablers for in-memory layout transformation acceleration and virtualization
Minli Julie Liao, Jack Sampson · 2020
Recent works have shown that certain classes of emerging memory technologies lend themselves to organizations that offer equally dense access support for patterns with multiple strides, such as row-column memories. However, with few exceptions, these prior works have only considered such multi-orientation memories (MOMs) and MOM-caching techniques in the context of traditional processor architectures. In this work, we explore the potential for leveraging the capabilities of MOMs to present multiple concurrent views of data organization within the memory hierarchy as a means to offload and overlap inter-kernel marshalling, a range of data layout transformations, and even lazy construction of derivative data structures to work performed by the MOM-capable memories and caches themselves. We demonstrate the potential of MOM-offloading to improve performance and reduce data movement for select computation patterns and describe the application of the approach to broader classes of processing in memory workloads.