Quantifying the Mismatch between Emerging Scale-Out Applications and Modern Processors

  • Authors:

  • Michael Ferdman;Almutaz Adileh;Onur Kocberber;Stavros Volos;Mohammad Alisafaee;Djordje Jevdjic;Cansu Kaynak;Adrian Daniel Popescu;Anastasia Ailamaki;Babak Falsafi

  • Affiliations:

  • Stony Brook University;École Polytechnique Fédérale de Lausanne;École Polytechnique Fédérale de Lausanne;École Polytechnique Fédérale de Lausanne;École Polytechnique Fédérale de Lausanne;École Polytechnique Fédérale de Lausanne;École Polytechnique Fédérale de Lausanne;École Polytechnique Fédérale de Lausanne;École Polytechnique Fédérale de Lausanne;École Polytechnique Fédérale de Lausanne

  • Venue:
  • ACM Transactions on Computer Systems (TOCS)
  • Year:
  • 2012

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Abstract

Emerging scale-out workloads require extensive amounts of computational resources. However, data centers using modern server hardware face physical constraints in space and power, limiting further expansion and calling for improvements in the computational density per server and in the per-operation energy. Continuing to improve the computational resources of the cloud while staying within physical constraints mandates optimizing server efficiency to ensure that server hardware closely matches the needs of scale-out workloads. In this work, we introduce CloudSuite, a benchmark suite of emerging scale-out workloads. We use performance counters on modern servers to study scale-out workloads, finding that today’s predominant processor microarchitecture is inefficient for running these workloads. We find that inefficiency comes from the mismatch between the workload needs and modern processors, particularly in the organization of instruction and data memory systems and the processor core microarchitecture. Moreover, while today’s predominant microarchitecture is inefficient when executing scale-out workloads, we find that continuing the current trends will further exacerbate the inefficiency in the future. In this work, we identify the key microarchitectural needs of scale-out workloads, calling for a change in the trajectory of server processors that would lead to improved computational density and power efficiency in data centers.