Performance per watt | EPFL Graph Search

In computing, performance per watt is a measure of the energy efficiency of a particular computer architecture or computer hardware. Literally, it measures the rate of computation that can be delivered by a computer for every watt of power consumed. This rate is typically measured by performance on the LINPACK benchmark when trying to compare between computing systems: an example using this is the Green500 list of supercomputers. Performance per watt has been suggested to be a more sustainable measure of computing than Moore’s Law. System designers building parallel computers, such as Google's hardware, pick CPUs based on their performance per watt of power, because the cost of powering the CPU outweighs the cost of the CPU itself. Spaceflight computers have hard limits on the maximum power available and also have hard requirements on minimum real-time performance. A ratio of processing speed to required electrical power is more useful than raw processing speed. Definition

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

Al Mutaz Adileh, Anastasia Ailamaki, Mohammad Alisafaee, Babak Falsafi, Michael Ferdman, Dorde Jevdic, Ilknur Cansu Kaynak, Yusuf Onur Koçberber, Adrian Daniel Popescu, Stavros Volos

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.

Association for Computing Machinery2012

Clearing the Clouds: A Study of Emerging Workloads on Modern Hardware

Al Mutaz Adileh, Anastasia Ailamaki, Mohammad Alisafaee, Babak Falsafi, Michael Ferdman, Ilknur Cansu Kaynak, Yusuf Onur Koçberber, Adrian Daniel Popescu, Stavros Volos

Emerging scale-out cloud applications need 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 use. Therefore, 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 cloud applications. We use performance counters on modern servers to study a wide range of cloud applications, finding that today’s predominant processor architecture is inefficient for running these workloads. We find that inefficiency comes from the mismatch between the application needs and modern processors, particularly in the organization of instruction and data memory systems and the processor core architecture. Moreover, while today’s predominant architectures are inefficient when executing scale-out cloud applications, we find that the current hardware trends further exacerbate the mismatch. In this work, we identify the key micro-architectural needs of cloud applications, calling for a change in the trajectory of server processors that would lead to improved computational density and power efficiency in data centers.

2011

Clearing the Clouds: A Study of Emerging Scale-out Workloads on Modern Hardware

Al Mutaz Adileh, Anastasia Ailamaki, Mohammad Alisafaee, Babak Falsafi, Michael Ferdman, Ilknur Cansu Kaynak, Yusuf Onur Koçberber, Adrian Daniel Popescu, Stavros Volos

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. We use performance counters on modern servers to study a wide range of scale-out workloads, finding that today’s predominant processor micro-architecture 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 micro-architecture. Moreover, while today’s predominant micro-architecture 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 micro-architectural 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.

2012