Scale-Out Processors & Energy Efficiency

TL;DR

This paper reevaluates scale-out server processor design by focusing on performance per power, demonstrating that optimizing for performance per area also maximizes performance per power, which is crucial for energy-efficient data centers.

Contribution

It introduces a performance-per-power metric for scale-out processor optimization and shows that maximizing performance per area also maximizes performance per power.

Findings

Maximum performance per area aligns with maximum performance per power.

Optimizing for power efficiency is essential for data center cost reduction.

Reevaluation of scale-out design methodology with a new metric.

Abstract

Scale-out workloads like media streaming or Web search serve millions of users and operate on a massive amount of data, and hence, require enormous computational power. As the number of users is increasing and the size of data is expanding, even more computational power is necessary for powering up such workloads. Data centers with thousands of servers are providing the computational power necessary for executing scale-out workloads. As operating data centers requires enormous capital outlay, it is important to optimize them to execute scale-out workloads efficiently. Server processors contribute significantly to the data center capital outlay, and hence, are a prime candidate for optimizations. While data centers are constrained with power, and power consumption is one of the major components contributing to the total cost of ownership (TCO), a recently-introduced scale-out design methodology optimizes server processors for data centers using performance per unit area. In this work, we use a more relevant performance-per-power metric as the optimization criterion for optimizing server processors and reevaluate the scale-out design methodology. Interestingly, we show that a scale-out processor that delivers the maximum performance per unit area, also delivers the highest performance per unit power.