1 research outputs found
Heterogeneous processor composition: metrics and methods
Heterogeneous processors intended for mobile devices are composed of a number of
different CPU cores that enable the processor to optimize performance under strict
power limits that vary over time. Design space exploration techniques can be used
to discover a candidate set of potential cores that could be implemented on a heterogeneous
processor. However, candidate sets contain far more cores than can feasibly
be implemented. Heterogeneous processor composition therefore requires solutions
to the selection problem and the evaluation problem. Cores must be selected from
the candidate set, and these cores must be shown to be quantitatively superior to
alternative selections. The qualitative criterion for a selection of cores is diversity.
A diverse set of heterogeneous cores allows a processor to execute tasks with varying
dynamic behaviors at a range of power and performance levels that are appropriate for
conditions during runtime.
This thesis presents a detailed description of the selection and evaluation problems,
and establishes a theoretical framework for reasoning about the runtime behavior
of power-limited, heterogeneous processors. The evaluation problem is specifically
concerned with evaluating the collective attributes of selections of cores rather than
evaluating the features of individual cores. A suite of metrics is defined to address the
evaluation problem. The metrics quantify considerations that could otherwise only be
evaluated subjectively. The selection problem is addressed with an iterative, diversity-preserving
algorithm that emphasizes the flexibility available to programs at runtime.
The algorithm includes facilities for guiding the selection process with information
from an expert, when available. Three variations on the selection algorithm are defined.
A thorough analysis of the proposed selection algorithm is presented using data
from a large-scale simulation involving 33 benchmarks and 3000 core types. The three
variations of the algorithm are compared to each other and to current, state-of-the-art
selection techniques. The analysis serves as both an evaluation of the proposed
algorithm as well as a case study of the metrics