SPM-Sieve: A framework for assisting data partitioning in scratch pad memory based systems

Modern system architectures sometimes include scratch pad memories (SPM) in their memory hierarchy to take advantage of their simpler design, in an attempt to meet the system area, performance, and power budget. These systems employing SPM can be broadly categorized as: (a) cacheless systems with only SPM, (b) hybrid systems with both cache and SPM, and (c) reconfigurable systems with the provision to reconfigure local memory as either cache, SPM, or a combination of the two. However SPM based systems have needed larger efforts spent on their programming, mainly due to allocating data and orchestrating data transfers explicitly by soft-ware. Tight product development cycles require faster development and porting of diverse applications to multiple SPM based architectures. In this paper we present SPM-Sieve, a profile-based tool and framework targeted for SPM based architectures that generates partitioning decisions of the first level memory in the system hierarchy, and suggests object mapping amongst the memory partitions without resorting to detailed simulation of all configurations. This is done by natively executing an application and using minimal target architecture specification, which not only provides early information influencing data organization in the application, but also provides a foundation for other more sophisticated algorithms to produce optimized allocations. We demonstrate the utility and generality of SPM-Sieve by evaluating it on a large number of SPEC2000 benchmarks targeted for a 128KB first level memory. We evaluate its effectiveness by performing simulation studies comparing the partition suggested by the tool against varying partition sizes, and observe that its suggestions are very competitive for SPM based architectures with and without caches.