SPARTA

Abstract

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To meet the performance and energy efficiency demands of emerging complex and variable workloads, heterogeneous many-core architectures are increasingly being deployed, necessitating operating systems support for adaptive task allocation to efficiently exploit this heterogeneity in the face of unpredictable workloads. We present SPARTA, a throughput-aware runtime task allocation approach for Heterogeneous Many-core Platforms (HMPs) to achieve energy efficiency. SPARTA collects sensor data to characterize tasks at runtime and uses this information to prioritize tasks when performing allocation in order to maximize energy-efficiency (instructions-per-Joule) without sacrificing performance. Our experimental results on heterogeneous many-core architectures executing mixes of MiBench and PARSEC benchmarks demonstrate energy reductions of up to 23% when compared to state-of-the-art alternatives. SPARTA is also scalable with low overhead, enabling energy savings in large-scale architectures with up to hundreds of cores.