DBDB

Abstract

In heterogeneous multi-core systems, such as the Cell BE or certain embedded systems, the accelerator core has its own fast local memory without hardware supported coherence. It is software's responsibility to dynamically transfer the working set when the total data set is too large to fit in the local memory. The data can be transferred through a software controlled cache which maintains correctness and exploits reuse among references, especially when complicated aliasing or data dependence exists. However, the software controlled cache introduces the extra overhead of cache lookup. In this paper we present the design and implementation of a Direct Blocking Data Buffer (DBDB) which combines compiler analysis and runtime management to optimize local memory utilization. We use compile time analysis to identify regular references in a loop body, block the innermost loop according to the access patterns and available local memory space, insert DMA operations for the blocked loop, and substitute references to local buffers. The runtime is responsible for allocating local memory for DBDB, especially for disambiguating aliased memory accesses which could not be resolved at compile time. We further optimize noncontiguous references by taking advantage of the DMA-list feature provided by the Cell BE. A practical performance model is presented to guide the DMA transfer scheme selection among single-DMA, multi-DMA and DMA-list. We have implemented DBDB in the IBM XL C/C++ for Multicore Acceleration for Linux, and have conducted experiments with selected test cases from the NAS OpenMP and SPEC benchmarks. The results show that our method performs well compared with traditional software cache approach. We have observed a speedup of up to 5.3x and 4x in average.