Abstract

In this article, we present a novel linear time algorithm for data remapping, that is, (i) lightweight; (ii) fully automated; and (iii) applicable in the context of pointer-centric programming languages with dynamic memory allocation support. All previous work in this area lacks one or more of these features. We proceed to demonstrate a novel application of this algorithm as a key step in optimizing the design of an embedded memory system. Specifically, we show that by virtue of locality enhancements via data remapping, we may reduce the memory subsystem needs of an application by 50%, and hence concomitantly reduce the associated costs in terms of size, power, and dollar-investment (61%). Such a reduction overcomes key hurdles in designing highperformance embedded computing solutions. Namely, memory subsystems are very desirable from a performance standpoint, but their costs have often limited their use in embedded systems. Thus, our innovative approach offers the intriguing possibility of compilers playing a significant role in exploring and optimizing the design space of a memory subsystem for an embedded design. To this end and in order to properly leverage the improvements afforded by a compiler optimization, we identify a range of measures for quantifying the cost-impact of popular notions of locality, prefetching, regularity of memory access and others. The proposed methodology will

Citations