Task Assignment Algorithms for Multicore Platforms with Process Variations

Abstract

We have moved into an era where modern multicore systems must deal with several critical challenges such as increased power density and high temperatures. In addition to this, ITRS projects that heterogeneity due to manufacturing process variations would continue to rapidly increase in sub-nanometer technology nodes. Spatial heat influence or the lateral heat transfer is becoming more prevalent in manycore systems and has a significant impact on the processor power consumption. Thus it is becoming increasingly difficult to meet the application throughput requirements while adhering to the system power budget and thermal constraints. Many of the existing task mapping schemes that does not take into account the underlying variations in core frequency and leakage power consumption will result in sub-optimal solutions. In this work, we first formulate the mapping problem as an optimization problem and then present a rank-based, process variation and lateral heat transfer aware application mapping algorithm to solve it heuristically. Our extensive experimental results reveal that the proposed algorithm produces near optimal results (average 6% away from optimal) while being orders of magnitude faster (4000x faster for 225 cores).