Adaptive Performance Optimization under Power Constraint in Multi-thread Applications with Diverse Scalability

Energy consumption has become a core concern in computing systems.
In this context, power capping is an approach that aims at
ensuring that the power consumption of a system does not overcome
a predefined threshold. Although various power capping
techniques exist in the literature, they do not fit well the nature
of multi-threaded workloads with shared data accesses and nonminimal
thread-level concurrency. For these workloads, scalability
may be limited by thread contention on hardware resources and/or
data, to the point that performance may even decrease while increasing
the thread-level parallelism, indicating scarce ability to
exploit the actual computing power available in highly parallel
hardware. In this paper, we consider the problem of maximizing
the performance of multi-threaded applications under a power cap
by dynamically tuning the thread-level parallelism and the power
state of CPU-cores in combination. Based on experimental observations,
we design a technique that adaptively identifies, in linear
time within a bi-dimensional space, the optimal parallelism and
power state setting. We evaluated the proposed technique with
different benchmark applications, and using different methods for
synchronizing threads when accessing shared data, and we compared
its effectiveness with other state-of-the-art power capping
techniques.