energy efficient computing & computational services
TRANSCRIPT
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
Uses of EMPPACK
• Data processing – ground segments
• Drive on-board software design and improvements
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
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