The document discusses energy efficient computing and computational services. It covers using profiling tools like EMPPACK to analyze the energy footprint of applications and optimize software. EMPPACK allows profiling code, applications, and whole systems to compare performance vs energy behavior. The document also discusses using historical energy consumption data and analytics to schedule systems management and identify usage patterns. Overall it aims to achieve the best balance of performance and energy efficiency.

1. 1
Energy efficient computing & computational services
David Wallom

2. Energy Efficiency in Computing
• Basic rule: An application being faster does not imply being energy efficient
Runtime/Energy Performance of Gromacs(MPI)

3. 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

4. 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

5. 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

6. EMPPACK: A Preview

7. EMPPACK: A Preview

8. Uses of EMPPACK
• Data processing – ground segments
• Drive on-board software design and improvements

9. Energy Efficiency in SKA

10. Impact on other operations on Energy consumption

11. 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

12. 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

14. Computational Services/Integrated Applications

15.  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

16. 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

17. 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

18. 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

19. 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

20. • With thanks to;
– J. Thiyagalingam.
– W. Armour
– Anne E Trefethen