Hpc, grid and cloud computing - the past, present, and future challenge

HPC, Grid and Cloud Computing
- The Past, Present and Future

Jason Shih
Academia Sinica Grid computing
FBI 極簡主義, Nov 3rd, 2010

Outline

 Trend in HPC
 Grid: eScience Research @ PetaScale
 Cloud Hype and Observation
 Future Exploration Path of Computing
 Summary

Max CERN/T1-ASGC Point2Point

About ASGC
Inbound : 9.3 Gbps!

1. Most Reliable T1: 98.83%!
2. Very Highly Performing and
most Stable Site in CCRC08!

Asia Pacific Regional
Operation Center
A Worldwide Grid
Infrastructure
>280 sites,
>45 countries
>80,000 CPUs,
>20 PetaBytes 100 meters underground
>14,000 users,
>200 VOs 27km of circumstances;
>250,000 jobs/day
locate in Geneva
Best Demo Award of EGEE’07!
Grid Application Platform Avian Flu Drug Discovery

Lightweight Problem Solving Large Hadron Collider (LHC)
Framework!

21

Emerging Trend and Technologies: 2009 -2010

Hype Cycle for Storage Technologies - 2010

Trend in High Performance Computing

Ugly? Performance of HPC Cluster

 272 (52%) of world fastest clusters have efficiency lower
than 80% (Rmax/Rpeak)
 Only 115 (18%) could drive over 90% of theoretical peak
 Sampling from Top500 HPC cluster

Trend of Cluster Efficiency 2005-2009

Performance and Efficiency
 20% of Top-performed clusters contribute 60% of Total
Computing Power (27.98PF)
 5 Clusters Eff. < 30

Impact Factor: Interconnectivity
- Capacity and Cluster Efficiency

 Over 52% of Cluster base on GbE
 With efficiency around 50% only
 InfiniBand adopt by ~36% HPC Clusters

HPC Cluster - Interconnect Using IB
 SDR, DDR and QDR in Top500
 Promising efficiency >= 80%
 Majority of IB ready cluster adopt
DDR (87%) (2009 Nov)
 Contribute 44% of total computing
power
 ~28 Pflops
 Avg efficiency ~78%

Trend in HPC Interconnects: Infiniband Roadmap

Common semantics

 Programmer productivity
 Easy of deployment
 HPC filesystem are more mature, wider feature set:
 High concurrent read and write
 In the comfort zone of programmers (vs cloudFS)
 Wide support, adoption, acceptance possible
 pNFS working to be equivalent
 Reuse standard data management tools
 Backup, disaster recovery and tiering

Some Observations & Looking for Future (I)
 Computing Paradigm
 (Almost) Free FLOPS
 (Almost) Logic Operation
 Data Access (Memory) Is A Major Bottleneck
 Synchronization Is the Most Expensive
 Data Communication Is A Big Factor in Performance
 I/O Still A Major Programming Consideration
 MPI Coding Is the Motherhood of Large Scale Computing
 Computing in Conjunction of Massive Data Management
 Finding Parallelism Is Not A Whole Issue In Programming
 Data Layout
 Data Movement
 Data Reuse
 Frequency of Interconnected Data Communication

Some Observations & Looking for Future (II)
 Emerging New Possibility
 Massive “Small” Computing Elements with On Board Memory
 Computing Node Can Be Caonfigured Dynamically (including Failure
recovery)
 Network Switch (within on site complex) Will Nearly Match Memory
Performance
 Parallel I/O Support for Massive Parallel System
 Asynchronous Computing/Communication Operation
 Sophisticate Data Pre-fetch Scheme (Hardware/Algorithm)
 Automate Dynamic Load Balance Method
 Very High Order Difference Scheme (also Implicit Method)
 Full Coupling of Formerly Split Operators
 Fine Numerical Computational Grid (grid number > 10,000)
 Full Simulation of Protein
 Full Coupling of Computational Model
 Grid Computing for All

Some Observations & Looking for Future (3)

System will get more complicate &
Computing Tool will get more sophisticated:

Vendor Support & User Readiness?

Grid: eScience Research @ PetaScale

WLCG Computing Model
- The Tier Structure
 Tier-0 (CERN)
 Data recording
 Initial data reconstruction
 Data distribution
 Tier-1 (11 countries)
 Permanent storage
 Re-processing
 Analysis
 Tier-2 (~130 countries)
 Simulation
 End-user analysis

Enabling Grids for E-sciencE

Archeology
Astronomy
Astrophysics
Civil Protection
Comp. Chemistry
Earth Sciences
Finance
Fusion
Geophysics
High Energy Physics
Life Sciences
Multimedia
Material Sciences
…

EGEE-II INFSO-RI-031688 EGEE07, Budapest, 1-5 October 2007 4

Objectives

 Building sustainable research and collaboration
infrastructure
 Support research by e-Science, on data intensive
sciences and applications require cross disciplinary
distributed collaboration

ASGC Milestone
 Operational from the deployment of LCG0 since 2002
 ASGC CA establish on 2005 (IGTF in same year)
 Tier-1 Center responsibility start from 2005
 Federated Taiwan Tier-2 center (Taiwan Analysis Facility, TAF)
is also collocated in ASGC
 Rep. of EGEE e-Science Asia Federation while joining EGEE
from 2004
 Providing Asia Pacific Regional Operation Center (APROC)
services to regional-wide WLCG/EGEE production infrastructure
from 2005
 Initiate Avian Flu Drug Discovery Project and collaborate with
EGEE in 2006
 Start of EUAsiaGrid Project from April 2008

LHC First Beam – Computing at the Petascale

 General Purpose, pp, heavy ions

LHCb: B-physics, CP Violation ALICE: Heavy ions, pp

CMS: General Purpose, pp, heavy ions

ATLAS: General Purpose, pp, heavy ions

Size of LHC Detector

ATLAS
Bld. 40

7,000 Tons ATLAS Detector
CMS
25 Meters in Height
45 Meters in Length

Standard Cosmology

Good model from 0.01 sec
after Big Bang

Energy, Density, Temperature
Supported by considerable
observational evidence

Time
Elementary Particle Physics

From the Standard Model into the
unknown: towards energies of
1 TeV and beyond: the Terascale

Towards Quantum Gravity

From the unknown into the
unknown...
http://www.damtp.cam.ac.uk/user/gr/public/bb_history.html
UNESCO Information 25
Preservation debate, April 2007 -
Jamie.Shiers@cern.ch

WLCG Timeline

 First Beam on LHC, Sep.
10, 2008
 Severe Incident after 3w
operation (3.5TeV)

Petabyte Scale Data Challenges

 Why Petabyte?
 Experiment Computing Model
 Comparing with conventional data management
 Challenges
 Performance: LAN and WAN activities
 Sufficient B/W between CPU Farm
 Eliminate Uplink Bottleneck (Switch Tires)
 Fast responding of Critical Events
 Fabric Infrastructure & Service Level Agreement
 Scalability and Manageability
 Robust DB engine (Oracle RAC)
 KB and Adequate Administration (Training)

Tier Model and Data Management Components

Disk Pool Configuration
- T1 MSS (CASTOR)

Distribution of Free Capacity
- Per Disk Servers vs. per Pool

Storage Server Generation
- Drive vs. Net Capacity (Raid6)

TB
TB 21TB/DS
31TB/DS

TB TB
40TB/DS 15TB/DS

IDC Collocation
 Facility install complete at Mar 27th
 Tape system delay after Apr 9th
 Realignment
 RMA for faulty parts

Storage Farm
 ~ 110 raid subsystem deployed since 2003.
 Supporting both Tier1 and 2 storage fabric
 DAS connection to front-end blade server
 Flexible switching front end server upon
performance requirement
 4-8G fiber channel connectivity

Computing/Storage System Infrastructure

Throughput of WLCG Experiments
 Throughput defined as Job Eff. x # Jobs running
 Characteristic of 4 LHC Exp. depicting in-efficiency
is due to poor coding.

Reliability From Different View Perspective

Storage Fabric Management
– The Challenges: Events Management

Open Cloud Consortium

Cloud Hype and Observation

Hpc, grid and cloud computing - the past, present, and future challenge

Cloud Hype
 Metacomputing (~1987, L. Smarr)
 Grid Computing (~1997, I. Foster, K. Kesselman)
 Cloud Computing (~2007, E. Schmidt?)

Type of Infrastructure

 roprietary solutions by public providers
P
 Turnkey solutions developed internally as they own
the software and hardware solution/tech.
 loud specific support
C
 Developers of specific hardware and/or software
solutions that are utilized by service providers or used
internally when building private cloud
 raditional providers
T
 Leverage or tweak their existing

Grid and Cloud:
Comparison
 Cost & Performance
 Scale & Usability
 Service Mapping
 Interoperability
 Application Scenarios

Cloud Computing:
“X” as a Service
 ype of Cloud
T
 ayered Service Model
L
 eference Model
R

Virtualization is not Cloud computing

 Performance Overhead
 FV vs. PV
 Disk I/O and network throughput (VM scalability)

Ref: Linux-based virtualization for HPC clusters.

Cloud Infrastructure
Best practical & Real world performance
 tart Up: 60 ~ 44s
S
 estart : 30 ~ 27s
R
 eletion: 60 ~ <5s
D
 igrate
M
 30 VM ~ 26.8s
 60 VM ~ 40s
 20 VM ~ 89s
1
 top
S
 30VM ~ 27.4s
 60VM ~ 26s
 20VM ~ 57s
1

Cloud Infrastructure
Best practical
Real World Performance
 tart Up: 60 ~ 44s
S
 estart : 30 ~ 27s
R
 eletion: 60 ~ <5s
D
 igrate
M
 30 VM ~ 26.8s
 60 VM ~ 40s
 20 VM ~ 89s
1
 top
S
 30VM ~ 27.4s
 60VM ~ 26s
 20VM ~ 57s
1

Virtualization: HEP Best Practical

Grid over Cloud or
Cloud over Grid?

Conclusion: My Opinion

 Future of Computing: Technology-Push & Demand-
Pull
 Emerging of new science paradigm
 Virtualization: Promising Technology but being
overemphasized
 Green: Cloud Service Transparency & Common
Platform
 More Computing Power ~ Power Consumption
Challenge
 Private Clouds Will be predominant way
 Commercial Cloud (Public) expect not evolving fast

Acknowledgment

 Thanks valuable discussion/inputs from TCloud
(Cloud OS: Elaster)
 Professional Technical Support from Silvershine
Tech. at beginning of the collaboration.

The interesting thing about Cloud Computing is that we’ve
defined Cloud Computing to include everything that we
already do….. I don’t understand what we would do
differently in the light of Cloud Computing other than
change the wording of some of our ads.
Larry Ellison, quote in the Wall Street Journal, Sep 26, 2008

Issues

 Scalability?
 Infrastructure operation vs. performance
 Assessment
 Application aware – Cloud service
 Cost analysis
 Data center power usage – PUE
 Cloud Myth
 Top 10 Cloud Computing Trend
 http://www.focus.com/articles/hosting-bandwidth/
top-10-cloud-computing-trends/
 Use Cases & Best Practical

Issues (II)

 Volunteer computing (boinc)?
 Total capacity & performance
 successful stories & research Despines
 What’s hindering cloud adoption? Try human.
 http://gigaom.com/cloud/whats-hindering-cloud-
adoption-how-about-humans/
 Future projection?
 service readiness? Service level? Technical barriers?

Hpc, grid and cloud computing - the past, present, and future challenge

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