Many-core needs fine-grained scheduling : a case study of query processing on Intel Xeon Phi processors

Abstract

Emerging many-core processors feature very high memory bandwidth and computational power. For example, Intel Xeon Phi many-core processors of the Knights Corner (KNC) and Knights Landing (KNL) architectures embrace 60 to 64 x86-based CPU cores with 512-bit SIMD capabilities and high-bandwidth memories like the GDDR5 on KNC and on-package DRAMs on KNL. In this paper, we study the performance main-memory database operators and online analytical processing (OLAP) on such many-core architectures. We find that even the state-of-the-art database operators suffer severely from memory stalls and resource underutilization on those many-core processors. We argue that a software approach decomposing a coarse-grained operator into fine-grained phases and executing two independent phases with complementary resource requirements concurrently can address this problem. This approach allows more fine-grained control of resource utilization. Our experiments demonstrate significant performance gain and high resource utilization achieved by our proposed approaches on both KNC and KNL.