'Columbia University Libraries/Information Services'
Doi
Abstract
In this paper, we propose a method to program divide-and-conquer problems on multicore systems that is based on a data-driven recursive programming model. Data intensive programs are difficult to program on multicore architectures because they require efficient utilization of inter-core communication. Models for programming multicore systems available today generally lack the ability to automatically extract concurrency from a sequential style program and map concurrent tasks to efficiently leverage data and temporal locality. For divide-and-conquer algorithms, a recursive programming model can address both of these problems. Furthermore, since a recursive function has the same behavior patterns at all granularities of a problem, the same recursive model can be used to implement a multicore program at all of its levels: 1. the operations of a single core, 2. how to distribute tasks among several cores, and 3. in what order to schedule tasks on a multicore system when it is not possible to schedule all of the tasks at the same time. We present a novel selective execution technique that can enable automatic parallelization and task mapping of a recursive program onto a multicore system. To verify the practicality of this approach, we perform a case-study of bitonic sort on the Cell BE processor
