NDS: N-Dimensional Storage

Abstract

Demands for efficient computing among applications that use highdimensional datasets have led to multi-dimensional computers- computers that leverage heterogeneous processors/accelerators offering various processing models to support multi-dimensional compute kernels. Yet the front-end for these processors/accelerators is inefficient, as memory/storage systems often expose only entrenched linear-space abstractions to an application, and they often ignore the benefits of modern memory/storage systems, such as support for multi-dimensionality through different types of parallel access. This paper presents N-Dimensional Storage (NDS), a novel, multidimensional memory/storage system that fulfills the demands of modern hardware accelerators and applications. NDS abstracts memory arrays as native storage that applications can use to describe data locations and uses coordinates in any application-defined multi-dimensional space, thereby avoiding the software overhead associated with data-object transformations. NDS gauges the application demand underlying memory-device architectures in order to intelligently determine the physical data layout that maximizes access bandwidth and minimizes the overhead of presenting objects for arbitrary applications. This paper demonstrates an efficient architecture in supporting NDS. We evaluate a set of linear/tensor algebra workloads along with graph and data-mining algorithms on custom-built systems using each architecture. Our result shows a 5.73× speedup with appropriate architectural support.