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Energy efficient computing & computational services David Wallom

Energy Efficiency in Computing

• Basic rule: An application being faster does not imply being energy efficient

Runtime/Energy Performance of Gromacs(MPI)

Energy Efficiency in Computing

• Aim to:

– Achieve best possible balance of performance with energy consumption

– Use hardware features to achieve this goal. E.g.

• Dynamic Concurrency Throttling (DCT)

• Dynamic Voltage and Frequency Scaling (DVFS)

• Efficient mapping of processes

Achieving Energy Efficiency

• Profiling and Tuning–Profile applications for their energy/power footprint–Optimize software components for reducing this footprint

• Operational reduction–Understand the usage pattern of computing systems–Manage their usage using algorithms

Profiling using EMPPACK

• EMPPACK (Energy Measurement and Profiling Package) facilitates Code and application profiling

• Ability to obtain energy footprint of whole system, GPUs and Nodes of a cluster

• Ability to compare performance behavior vs. energy behavior

• Supports

–C/C++(+MPI), FORTRAN(+MPI) and MATLAB

EMPPACK: A Preview

EMPPACK: A Preview

Uses of EMPPACK

• Data processing – ground segments

• Drive on-board software design and improvements

Energy Efficiency in SKA

Impact on other operations on Energy consumption

Enhancements

• Power

– In-band

• EMPACK

• Intel tools/API's (http://software.intel.com/en-us/blogs/2013/06/18/measuring-application-power-consumption-on-linux-operating-system)

– Out of band

• IPMI (Chassis)

• Hardware monitor e.g. Watts-On

• Cycles

– Oprofile

– Perf

– Intel tools/API's

– Paraver (http://www.bsc.es/computer-sciences/performance-tools/paraver)

• Network

– OSU Micro-Benchmarks suite

– Netperf

– Sockperf

Energy Efficiency through Operational Management

• Combining the knowledge of a system with high resolution energy consumption information

– Use historic data to

• Detect the trend in usage of computing systems

Times of days, days of weeks where systems peaks, idles etc

• Schedule systems management using a framework

–Holistic investigation to cover all behavior and contributions

• Applied analytics to identify features in data matching known activities to allow for identification on unknown activities

Computational Services/Integrated Applications

Computation and storage as a

service

DMS integration

Self contained HPC Engine

with stable interfaces

Data flow

All data requested

All data stored in HPCDS

Current suggested

infrastructure

Federation of clusters

Resilience

Scalability

Future utilisation of cloud

computing with seamless

transition

e-Infrastructure as a Service

HPC Engine and Storage

Next Generation Infrastructure

The Smart Grid

High Speed Communications System

Service

Restoration

Voltage

Control

Condition

Monitoring

/Data

Mining

Distribution

System

State

Estimation

SCADA & Distribution Management System

myTrustedCloud: Trusted Computing and Cloud

• Attestation of VMs: only expected programs

with expected configuration files are loaded

inside the VM.

• Attestation of Node Controllers: only the

expected VM with the expected software stack

has been instantiated. The VM the user is

currently connecting to, is genuinely loaded by

the genuine hypervisor.

• Attestation of Storage Controllers: the VM is

binding to the expected virtual storage, and the

state of the virtual storage verified

• Drive down costs of ICT provision within the

energy industry by reducing the need for

multiple types of system to support multiple

parallel policy domains

Creating Actionable Information

• Exploiting data mining techniques:

– Predicting and classifying costs when there is a shift in the type of

tariff, e.g. shifting to a real-time tariff from a fixed price tariff.

– Clustering of domestic load profiles, determining behaviour type

and response by the consumer to tariff changes

• Utilise the EC FP7 Dehams dataset (www.dehams.eu, UK &

Bulgaria) to provide domestic load data

• Utilise well known k-means clustering & the Dirichlet Process

Mixture Model, a Bayesian non-parametric statistical

clustering model

• Other work includes the investigation utilising data from

commercial energy aggregation companies to quantify benefit

per commercial sector of the transition to real time energy

pricing

Clustering Domestic load profiles using Dirichlet Process Mixture Model

Using a Bayesian method allows us to handle uncertainty

within the data set more easily than more traditional data

mining methods

• First Bayesian non-parametric model to

cluster electricity load profiles

• Results are similar than other clustering

algorithms but number of clusters is not

a user input parameter

Potential Areas of Collaboration.

• One of the leading groups in the UK and internationally to work on Energy Efficient Computing

• We do have the most capable energy profiling software (the other one is PowerPack)

• We have better understanding of software w.r.t to their energy consumption

• EMPPACK is portable, supports C/C++/MATLAB and works on clusters with GPUs.

• Provide profiling ability to achieve energy efficient computing in large-scale parallel simulations

• With thanks to;

– J. Thiyagalingam.

– W. Armour

– Anne E Trefethen