Komputasi Kinerja Tinggi

Komputasi Kinerja Tinggi

Lembaga Ilmu Pengetahuan Indonesia
Pusat Penelitian Informatika
Tahun 1965 : Lembaga
Elektroteknika Nasional
(LEN)

Keppres No. 1 Tahun
1986 : Puslitbang
Telkoma, Inkom, Telimek
dan UPT Pusat LEN

Tahun 1990 :
UPT LEN diserahkan ke
BPIS (spin-off)

SK Ka. LIPI No.
1151/M/2001 : Pusat
Penelitian Informatika

Profil SDM
Fungsional Peneliti&Kandidat, Non Peneliti dan
fungsional Umum

Wan
ita
30%

Peneliti: 54 Orang

Pria
70%

27%

Fungsional Non
Peneliti: 3 Orang
59%

11%

RANGE USIA

Fungsional Umum adm:
24 Orang

3%

20
15
10
5
0
Series1

Penata Teknis: 10
Orang

26-30

31-35

36-40

41-45

46-50

51-55

56-60

61-65

11

15

19

19

11

8

5

3

Riset @P2Informatika
• Computational Science

Beberapa Hasil Penelitian
PEMANFAATAN TEKNOLOGI BERBASIS VISUAL UNTUK MENILAI KUALITAS PRODUK

Beberapa Hasil Penelitian

Text Data model for Weather Data

DNA QR codes of (a). JX426135; (B) JN245997; (c)
JN245994; (d) JN632605

Data Hiding
Scheme for
Digital Image

Simulasi Curah Hujan di Indonesia

Adaptasi model iklim wilayah Indonesia
menggunakan REGCM 4.0
Pemanfaatan RegCM4 (Regional Climate Model) untuk simulasi iklim
spesifik untuk wilayah Indonesia.

Perambatan Energi Gelombang
Bertujuan untuk melacak perambatan gelombang dengan potensi energi
yang cukup besar. Penelitian berfokus pada perambatan energi
gelombang permukaan air.

Density plot tinggi dan energi gelombang

Simulasi Dinamika Populasi Nyamuk dengan
Cellular
Model dan simulasi dinamika populasi nyamuk merupakan studi
bidang komputasi biologi untuk memahami perubahan ukuran
populasi suatu spesies. Hasil simulasi menunjukkan model yang
diusulkan mampu mensimulasikan populasi nyamuk secara temporal
dan spasial.
Simulasi Dispersal Nyamuk

Pengembangan Algoritma Sistem
Uji Berbasis Visual
Sistem Uji Berbagai Parameter Kualitas

2015

Pengujian Cip Sensor
Implementasi Pengujian pada Produksi Massal

CERN, Swiss

Produksi massal

Cip
terseleksi

2016

Cip hasil
produksi

Pengujian Visual

Riset @P2Informatika
• Big Data & IoT

Jenis Layanan

 Layanan Komputasi untuk Publik
 Layanan Diseminasi Teknologi Komputasi Berkinerja Tinggi

HPC LIPI @P2Informatika

Fasilitas
Cibinong

Gedung Pusat Inovasi
Jl. Raya Jakarta-Bogor
KM 47
Cibinong, Jawa Barat

Bandung

Gedung 10 Kompleks
LIPI
Jl. Cisitu No. 21
Bandung, Jawa Barat

Fasilitas HPC

Master Node (4 Node)
 Prosesor: 2 x 8 core
Intel Xeon E5 Family
 Memori: 128 GB
 Storage: 24 TB

GPU Node (20 Node)
- Prosesor: 2 x 4 core
Intel Xeon E5 Family
- Memori: 8 - 16 GB
- Storage: 500 GB
- GPU Tesla M2075
(488 core)

Basic Node (114 Node)
 Prosesor: 2 x 4 core
Intel Xeon E5 Family
 Memori: 8 - 16 GB
 Storage: 500 GB

High Memory Node (8
Node)
- Prosesor: 2 x 8 core
Intel Xeon E5 Family
- Memori: 256 GB
- Storage: 2 x 300 GB

HPC LIPI @P2Informatika
• Cibinong
− 928 Core
− 3072 GB RAM
− 103 TB Space
−

• Bandung
− 336 Core
− 560 GB RAM
− 67 TB Space
HPC Cibinong

HPC LIPI @P2Informatika

Apa itu Komputasi paralel?
Komputasi Serial:
Komputer desktop
konvensional memiliki Central
Processing Unit tunggal (CPU)
dan komputasi dilakukan
dengan memecah problem
menjadi serangkaian perintah
diskrit.
Perintah di eksekusi oleh
komputer satu persatu, karena
hanya satu perintah yang
dapat dijalankan dalam satu
waktu.

Apa itu Komputasi paralel?
Komputasi Paralel:
Sedangkan Hardware High
Performance Computing terdiri dari
beberapa CPU dan dikonfigurasi untuk
menjalankan perhitungan paralel.
Setiap problem harus dipecah menjadi
bagian-bagian diskrit yang dapat
dikomputasi secara konkuren
Setiap bagian kemudian dipecah
menjadi serangkaian perintah.
Perintah tersebut dikomputasi secara
simultan di CPU yang berbeda.

Apa itu Komputasi paralel?
•Masalah komputasi yang akan dijalankan di HPC harus
dapat:

•Dipisah-pisahkan menjadi potongan-potongan diskrit
pekerjaan yang dapat diselesaikan secara bersamaan;
•Mengeksekusi beberapa instruksi program pada setiap saat
dalam waktu;
•Diselesaikan dalam waktu kurang dengan beberapa sumber
komputasi daripada dengan sumber daya komputasi tunggal.

•sumber daya komputasi biasanya:

•komputer dengan prosesor / core banyak
•Sejumlah komputer yang terhubung dengan jaringan

Apa itu Komputasi paralel?
Jika anda memiliki aplikasi komputasi favorit



Satu prosesor akan memberi hasil dalam N jam.
Mengapa tidak menggunakan N prosesor
-- dan mendapat hasil hanya dalam 1 jam?
Konsepnya :
Parallelism = menggunakan beberapa prosesor pada sebuah problem

► Dua komponen parallel programming

 Komputasi
 Komunikasi

A Computer Cluster
Regular PC

A computer cluster

Front-end node

1 CPU
1 or 2 Hard disks
Some memory
512MB,.. 1GB,..
Compute-0-0

Compute-0-1

Compute-0-2

Parallel computing is computing by committee
komputasi paralel: penggunaan beberapa komputer atau
prosesor yang bekerja bersama-sama dalam tugas bersama.
Setiap prosesor bekerja pada bagiannya masing-masing dari
problem
Prosesor diperbolehkan untuk bertukar informasi dengan prosesor
lainnya
Grid of Problem to be solved
exchange

CPU #2 works on this area
of the problem

exchange

CPU #3 works on this area
of the problem

exchange

exchange

y

CPU #1 works on this area
of the problem

CPU #4 works on this area
of the problem

x

Mengapa menggunakan HPC?

Data + Simulation = Innovation
“Calculation will increasingly
replace experimentation in design
of useful materials, catalysts, and
drugs, leading to much greater
efficiency and new opportunities
for creativity”
-- Frank Wilczek, Physics in 100 Years

Data + Simulation = Innovation

Mengapa
menggunakan
HPC?

Mengapa menggunakan HPC?
 Dunia nyata parallel secara masiv:
 Di dunia nyata, banyak peristiwa yang kompleks dan saling terkait yang terjadi
pada saat yang sama, namun dalam urutan temporal.
 Dibandingkan dengan komputasi serial, komputasi paralel jauh lebih cocok untuk
pemodelan, simulasi dan pemahaman fenomena dunia nyata yang kompleks.

 Misalnya, bayangkan melakukan pemodelan hal-2 berikut secara
serial:

Mengapa menggunakan HPC?
Misalnya, bayangkan melakukan pemodelan hal-2 berikut
secara serial:

Menghemat waktu dan/atau uang
• Secara teori, menggunakan lebih banyak sumber daya pada
sebuah pekerjaan akan mempersingkat waktu penyelesaian,
dengan potensi penghematan biaya.
• Komputer paralel dapat dibangun dari komponen komoditas
yang murah.

Menghemat
waktu
dan/atau
uang

Menghemat
waktu
dan/atau
uang

MEMECAHKAN MASALAH YANG LEBIH BESAR / KOMPLEKS:
 Banyak masalah yang begitu besar dan / atau kompleks yang secara
teknis tidak atau tidak mungkin untuk dipecahkan dengan satu
komputer, terutama mengingat memori komputer yang terbatas.
 Contoh:
 Mesin pencari / database pengolahan jutaan transaksi setiap detik
 “Masalah yang menjadi tantangan besar”
(en.wikipedia.org/wiki/Grand_Challenge) membutuhkan sumber daya komputasi
petaflops dan petabyte.

Grand challenge Problem
Solving grand challenge applications using computer
modeling, simulation and analysis

Life Sciences

CAD/CAM

Aerospace

Digital Biology

E-commerce/anything

Military Applications

Grand challenges Problem

Life Sciene

Life
Sciene

Engine Combustion Research Group

Signal Processing/Quantum Mechanics
Convolution model (stencil)
Matrix computations (eigenvalues…)
Conjugate gradient methods
Normally not very demanding on latency and bandwidth
Some algorithms are embarrassingly parallel
Examples: seismic migration/processing, medical imaging, SETI@Home

Signal Processing Example

Pekerjaan Dilakukan Secara Konkuren
 Sebuah sumber daya komputasi tunggal hanya dapat melakukan satu hal pada
suatu waktu.
 Beberapa sumber daya komputasi dapat melakukan banyak hal secara
bersamaan.
 Contoh: Jaringan Kolaborasi menyediakan tempat global di mana orang-orang dari
seluruh dunia dapat bertemu dan melakukan pekerjaan “secara virtual".

Siapa yang Menggunakan Parallel Computing?
 Science dan Engineering :
 Secara historis, komputasi paralel telah dianggap “komputasi high end”, dan telah
digunakan untuk memodelkan masalah sulit di banyak bidang ilmu pengetahuan dan
teknik:
 Atmosphere, Earth, Environment
 Physics - applied, nuclear, particle, condensed matter, high pressure, fusion, photonics
 Bioscience, Biotechnology, Genetics
 Chemistry, Molecular Sciences
 Geology, Seismology
 Mechanical Engineering - from prosthetics to spacecraft
 Electrical Engineering, Circuit Design, Microelectronics
 Computer Science, Mathematics
 Defense, Weapons

HPC Applications and Major Industries
42

 Finite Element Modeling
 Auto/Aero

 Fluid Dynamics
 Auto/Aero, Consumer Packaged Goods
Mfgs, Process Mfg, Disaster Preparedness
(tsunami)

 Imaging
 Seismic & Medical

 Finance
 Banks, Brokerage Houses (Regression
Analysis, Risk, Options Pricing, What if, …)

 Molecular Modeling
Complex Problems, Large Datasets, Long Runs
 Biotech and Pharmaceuticals

This slide is from Intel presentation “Technologies for Delivering Peak Performance on HPC and Grid Applications”

5 January 2017

43

Divide and Conquer

Says 1 CPU
1,000,000 elements
Numerical processing for 1
element = .1 secs
One computer will take
100,000 secs = 27.7 hrs

Says 100 CPUs
.27 hr ~ 16 mins
5 January 2017

Life Science Problem – an example of Protein
Folding
 Take a computing year (in serial mode) to do molecular
dynamics simulation for a protein folding problem

•Excerpted from IBM David Klepacki’s The future of HPC
•Petaflop = a thousand trillion floating point operations per second

5 January 2017

Disaster Preparedness
Project LEAD
Severe Weather prediction
(Tornado) – OU leads.
HPC & Dynamically
adaptation to weather
forecast

Professor Seidel’s LSU CCT
Hurricane Route Prediction
Emergency Preparedness
Show Movie – HPC-enabled
Simulation

5 January 2017

Cancer Gene-mining
 Unsuccessful on a uni-processor
 Approach
Novel parallel gene-mining algorithms
Input from microarray
Retain accuracy
Significantly speed up (superlinear)
 IBM P5 supercomputer (128 node PPC).
Time to run the algorithm, keeping number of nodes fixed
Mesothelioma

Time taken(in secs)

1200
1000

Breast

80
60

Renal

Leukemia

40

800

20

600

0

Prostate

Lung

400
Pancreas

200
0
13

39

65

Number of processors

5 January 2017

Bladder
100

91

Colorectal
Ovary

Lymphoma
Melanoma

OvaMarker based Selection

GeneSetMine based Selection

46

Did you know that Playstation 3 is a
HPC/Supercomputer?

 9 cores/CPUs in one chip.
 Future gaming software is no longer graphic or multimedia only


This diagram is from an article from IBM Cell processor & compiler challenge

5 January 2017

Global Climate Modeling Problem
48

Problem is to compute:
f(latitude, longitude, elevation, tim
e) 
temperature, pressure, humidity,
wind velocity
 Approach:
Discretize the domain, e.g., a
measurement point every 10 km
Devise an algorithm to predict
weather at time t+1 given t

• Uses:
- Predict major events, e.g., El Nino
- Use in setting air emissions standards
C Cox

49

Global Climate Modeling Computation

 Computational requirements:

To match real-time, need 5x 1011 flops in 60 seconds = 8
Gflop/s
Weather prediction (7 days in 24 hours)  56 Gflop/s
Climate prediction (50 years in 30 days)  4.8 Tflop/s
To use in policy negotiations (50 years in 12 hours)  288
Tflop/s
 To double the grid resolution, computation is at least 8x
 State of the art models require integration of atmosphere, ocean, sea-ice, land models, plus
possibly carbon cycle, geochemistry and more
 Current models are coarser than this

C Cox

50

Heart Simulation

Problem is to compute blood flow in the heart
Approach:
Modeled as an elastic structure in an incompressible fluid.

The “immersed boundary method” due to Peskin and McQueen.
20 years of development in model
Many applications other than the heart: blood clotting, inner
ear, paper making, embryo growth, and others

Use a regularly spaced mesh (set of points) for evaluating the fluid flow

Uses
Current model can be used to design artificial heart valves
Can help in understand effects of disease (leaky valves)
Related projects look at the behavior of the heart during a heart attack
Ultimately: real-time clinical work
C Cox

51

•
•
•
•

Parallel computing: Web searching

Functional parallelism: crawling, indexing, sorting
Parallelism between queries: multiple users
Finding information amidst junk
Preprocessing of the web data set to help find information

• General themes of sifting through large, unstructured data
sets:
C Cox

52

Parallel Programming: Decomposition Techniques

Functional Decomposition (Functional Parallelism)
 Decomposing the problem into different tasks which can be distributed
to multiple processors for simultaneous execution
 Good to use when there is not static structure or fixed determination of
number of calculations to be performed
Domain Decomposition (Data Parallelism)
 Partitioning the problem's data domain and distributing portions to
multiple processors for simultaneous execution
 Good to use for problems where:
 data is static (e.g. solving large matrix or finite difference or finite element calculations)
 dynamic data structure tied to single entity where entity can be separated
 domain is fixed but computation within various regions of the domain is dynamic (fluid vortices models)
 Combination of functional and domain decomposition

C Cox

Siapa yang Menggunakan Parallel Computing?

Bioscience, Biotechnol
ogy, Genetics

Atmosphere, Earth, En
vironment

Siapa yang Menggunakan Parallel Computing?

Siapa yang Menggunakan Parallel Computing?
 Industrial and Commercial
 Aplikasi-aplikasi berikut memerlukan pengolahan data dalam jumlah besar dengan cara yang
canggih.

Big Data, databases, data
mining
Oil exploration
Web search engines, web
based business services
Medical imaging and
diagnosis
Pharmaceutical design

Financial and economic modeling
Management of national and multinational corporations
Advanced graphics and virtual
reality, particularly in the
entertainment industry
Networked video and multi-media
technologies
Collaborative work environments

Top Ten Most Powerful Computers http://www.top500.org)
#
Site
1 National Supercomputing Center
in Wuxi China
2 National Super Computer Center
in Guangzhou China

System
Sunway TaihuLight - Sunway MPP, Sunway SW26010 260C
1.45GHz, Sunway NRCPC
Tianhe-2 (MilkyWay-2) - TH-IVB-FEP Cluster, Intel Xeon E5-2692
12C 2.200GHz, TH Express-2, Intel Xeon Phi 31S1P NUDT

3 DOE/SC/Oak Ridge National
Laboratory US
4 DOE/NNSA/LLNL US

Titan - Cray XK7 , Opteron 6274 16C 2.200GHz, Cray Gemini
interconnect, NVIDIA K20x Cray Inc.
Sequoia - BlueGene/Q, Power BQC 16C 1.60 GHz, Custom IBM

5 DOE/SC/LBNL/NERSC US

Cori - Cray XC40, Intel Xeon Phi 7250 68C 1.4GHz, Aries
interconnect Cray Inc.
Oakforest-PACS - PRIMERGY CX1640 M1, Intel Xeon Phi 7250
68C 1.4GHz, Intel Omni-Path Fujitsu
K computer, SPARC64 VIIIfx 2.0GHz, Tofu interconnect Fujitsu

6 Joint Center for Advanced HPC
Japan
7 RIKEN (AICS) Japan
8 Swiss National Supercomputing
Centre (CSCS) Switzerland
9 DOE/SC/Argonne National
Laboratory US
10 DOE/NNSA/LANL/SNL US

Piz Daint - Cray XC50, Xeon E5-2690v3 12C 2.6GHz, Aries
interconnect , NVIDIA Tesla P100 Cray Inc.
Mira - BlueGene/Q, Power BQC 16C 1.60GHz, Custom IBM
Trinity - Cray XC40, Xeon E5-2698v3 16C 2.3GHz, Aries
interconnect Cray Inc.

Rmax
Cores
(TFlop/s)
10,649,600 93,014.6

Rpeak
Power
(TFlop/s)
(kW)
125,435.9 15,371

3,120,000

33,862.7

54,902.4

17,808

560,640

17,590.0

27,112.5

8,209

1,572,864

17,173.2

20,132.7

7,890

622,336

14,014.7

27,880.7

3,939

556,104

13,554.6

24,913.5

2,719

705,024

10,510.0

11,280.4

12,660

206,720

9,779.0

15,988.0

1,312

786,432

8,586.6

10,066.3

3,945

301,056

8,100.9

11,078.9

4,233

Computer Food Chain

Original Food Chain Picture

1984 Computer Food Chain

Mainframe
Mini Computer
Vector Supercomputer

Workstation

PC

1994 Computer Food Chain

(hitting wall soon)

Mini Computer

Workstation
(future is bleak)

Mainframe

Vector Supercomputer

MPP

PC

Computer Food Chain (Now and Future)

CLUSTERING OF COMPUTERS
FOR COLLECTIVE COMPUTING: TRENDS
?

1960

1990

1995+ 2000

Computing Platforms Evolution

Breaking Adm inistrative Barriers

2 1 0

2 1 0

2 1 0

2 1 0

2 1 0

2 1 0

2 1 0

?

P
E
R
F
O
R
M
A
N
C
E

2 1 0

21 00

Administrative Barriers
Individual
Group
D epart ment
C ampus
Sta te
N ational
Globe
Inte r Plane t
U niverse

Desktop

(Single Proc es sor?)

SMPs or
SuperC om
puters

Local
Cluster

Enterprise
Cluster/Grid

Global
Cluster/Grid

Inter Plan et
Cluster/Grid ??

Cluster Computer and its
Components

Clustering gained momentum when 3 technologies
converged:

1. Very HP Microprocessors
 workstation performance = yesterday supercomputers

2. High speed communication
 Comm. between cluster nodes >= between processors in an SMP.

3. Standard tools for parallel/ distributed
computing & their growing popularity.

Parallel architectures (1)
 Vector machines
 CPU processes multiple data sets
 shared memory
 advantages: performance, programming difficulties
 issues: scalability, price
 examples: Cray SV, NEC SX, Athlon3/d, Pentium- IV/SSE/SSE2

 Massively parallel processors (MPP)
 large number of CPUs
 distributed memory
 advantages: scalability, price
 issues: performance, programming difficulties
 examples: ConnectionSystemsCM1 i CM2, GAAP (GeometricArrayParallel Processor)

Parallel architectures (2)
 Symmetric Multiple Processing (SMP)
 two or more processors
 shared memory
 advantages: price, performance, programming difficulties
 issues: scalability
 examples: UltraSparcII, Alpha ES, Generic Itanium, Opteron, Xeon, …

 Non Uniform Memory Access (NUMA)
 Solving SMP’sscalability issue
 hybrid memory model
 advantages: scalability
 issues: price, performance, programming difficulties
 examples: SGI Origin/Altix, Alpha GS, HP Superdome

Clusters
 Cluster consists of:
 Nodes
 Network
 OS
 Cluster middleware

 Standard components
 Avoiding expensive proprietary components

Cluster Architecture
Sequential Applications
Sequential Applications
Sequential Applications

Parallel Applications
Parallel Applications
Parallel Applications
Parallel Programming Environment

Cluster Middleware
(Single System Image and Availability Infrastructure)
PC/Workstation

PC/Workstation

PC/Workstation

PC/Workstation

Communications

Communications

Communications

Communications

Software

Software

Software

Software

Network Interface
Hardware

Network Interface
Hardware

Cluster Interconnection Network/Switch

Network Interface
Hardware

Network Interface
Hardware

Cluster Components...1a
Nodes
Multiple High Performance Components:
PCs
Workstations
SMPs (CLUMPS)
Distributed HPC Systems leading to
Metacomputing
They can be based on different
architectures and running difference OS

Cluster Components...1b
Processors
 There are many (CISC/RISC/VLIW/Vector..)
 Intel: Pentiums, Xeon, Merceed….
 Sun: SPARC, ULTRASPARC
 HP PA
 IBM RS6000/PowerPC
 SGI MPIS
 Digital Alphas

 Integrate Memory, processing and networking into a single
chip

IRAM (CPU & Mem):
(http://iram.cs.berkeley.edu)
Alpha 21366 (CPU, Memory Controller, NI)

Cluster Components…2
OS

State of the art OS:
 Linux

(Beowulf)

 Microsoft NT (Illinois HPVM)
 SUN Solaris (Berkeley NOW)
 IBM AIX (IBM SP2)
 HP UX

(Illinois - PANDA)

 Mach (Microkernel based OS) (CMU)
 Cluster Operating Systems (Solaris MC, SCO Unixware, MOSIX
(academic project)
 OS gluing layers:

(Berkeley Glunix)

Cluster Components…3
High Performance Networks
 Ethernet (10Mbps),
 Fast Ethernet (100Mbps),
 Gigabit Ethernet (1Gbps)
 SCI (Dolphin - MPI- 12micro-sec latency)
 ATM
 Myrinet (1.2Gbps)
 Digital Memory Channel
 FDDI

Cluster Components…4
Network Interfaces
Network Interface Card

Myrinet has NIC
User-level access support
Alpha 21364 processor integrates
processing, memory controller, network
interface into a single chip..

Cluster Components…
5 Communication Software
 Traditional OS supported facilities (heavy weight due
to protocol processing)..
Sockets (TCP/IP), Pipes, etc.
 Light weight protocols (User Level)
Active Messages (Berkeley)
Fast Messages (Illinois)
U-net (Cornell)
XTP (Virginia)
 System systems can be built on top of the above
protocols

Cluster Components…6a
Cluster Middleware
Resides Between OS and Applications and
offers in infrastructure for supporting:
Single System Image (SSI)
System Availability (SA)
SSI makes collection appear as single
machine (globalised view of system
resources). Telnet cluster.myinstitute.edu
SA - Check pointing and process migration..

Cluster Components…6b
Middleware Components
Hardware
 DEC Memory Channel, DSM (Alewife, DASH) SMP Techniques

OS / Gluing Layers
 Solaris MC, Unixware, Glunix)

Applications and Subsystems
 System management and electronic forms
 Runtime systems (software DSM, PFS etc.)
 Resource management and scheduling (RMS):
 CODINE, LSF, PBS, NQS, etc.

Cluster Components…7a
Programming environments

Threads (PCs, SMPs, NOW..)
POSIX Threads
Java Threads

MPI
Linux, NT, on many Supercomputers

PVM
Software DSMs (Shmem)

Cluster Components…7b
Development Tools ?
Compilers
 C/C++/Java/ ;
 Parallel programming with C++ (MIT Press book)

RAD (rapid application development tools)..
GUI based tools for PP modeling
Debuggers
Performance Analysis Tools
Visualization Tools

Cluster Components…8
Applications
Sequential
Parallel / Distributed (Cluster-aware app.)

Grand Challenging applications
Weather Forecasting
Quantum Chemistry
Molecular Biology Modeling
Engineering Analysis (CAD/CAM)
……………….

PDBs, web servers,data-mining

Classification
of Cluster Computer

Clusters Classification..1
Based on Focus (in Market)

High Performance (HP) Clusters
Grand Challenging Applications

High Availability (HA) Clusters
Mission Critical applications

Clusters Classification..2
Based on Workstation/PC Ownership

Dedicated Clusters
Non-dedicated clusters
Adaptive parallel computing
Also called Communal
multiprocessing

Clusters Classification..3
Based on Node Architecture..

Clusters of PCs (CoPs)
Clusters of Workstations (COWs)
Clusters of SMPs (Symmetric
Multiprocessors)(CLUMPs)

Clusters Classification..4
Based on Node OS Type..

Linux Clusters (Beowulf)
Solaris Clusters (Berkeley NOW)
NT Clusters (HPVM)
AIX Clusters (IBM SP2)
SCO/Compaq Clusters (Unixware)
…….Digital VMS Clusters, HP
clusters, ………………..

Clusters Classification..5
Based on node components architecture &
configuration (Processor Arch, Node Type:
PC/Workstation.. & OS: Linux/NT..):

Homogeneous Clusters
 All nodes will have similar configuration

Heterogeneous Clusters
Nodes based on different processors
and running different OSes.

Clusters Classification..6a

Dimensions of Scalability & Levels of Clustering

(3)
Network
Public
Enterprise

Metacomputing (GRID)

Technology

(1)

Campus
Department
Workgroup
Uniprocessor

SMP

Cluster
MPP

Platform

(2)

Clusters Classification..6b
Levels of Clustering
Group Clusters (#nodes: 2-99)
 (a set of dedicated/non-dedicated computers - mainly connected by SAN like
Myrinet)

 Departmental Clusters (#nodes: 99-999)
 Organizational Clusters (#nodes: many 100s)
 (using ATMs Net)
 Internet-wide Clusters=Global Clusters: (#nodes: 1000s to many millions)

 Metacomputing
 Web-based Computing
 Agent Based Computing
 Java plays a major in web and agent based computing

Major issues in cluster design

Size Scalability (physical &
application)

Enhanced Availability (failure
management)

Single System Image (look-andfeel of one system)

Fast Communication (networks &
protocols)

Load Balancing (CPU, Net,
Memory, Disk)

Security and Encryption (clusters of
clusters)

Distributed Environment (Social
issues)
Programmability (simple API if
required)

Manageability (admin. And
control)
Applicability (cluster-aware and
non-aware app.)

What Next ??
Clusters of Clusters (HyperClusters)
Global Grid
Interplanetary Grid
Universal Grid??

Clusters of Clusters (HyperClusters)
Cluster 1
Scheduler

Master
Daemon

Submit
Graphical
Control

Clients

Cluster 2
Master
Daemon

Clients

Execution
Daemon

Cluster 3
Scheduler

Master
Daemon

Submit
Graphical
Control

Scheduler

Submit
Graphical
Control

LAN/WAN

Clients

Execution
Daemon

Execution
Daemon

Towards Grid Computing….

What is Grid ?
 An infrastructure that couples

Computers (PCs, workstations, clusters, traditional
supercomputers, and even laptops, notebooks, mobile
computers, PDA, and so on)
Software ? (e.g., renting expensive special purpose
applications on demand)
Databases (e.g., transparent access to human genome
database)
Special Instruments (e.g., radio telescope--SETI@Home
Searching for Life in galaxy, Austrophysics@Swinburne for
pulsars)
People (may be even animals who knows ?)
 across the local/wide-area networks (enterprise, organisations, or Internet)
and presents them as an unified integrated (single) resource.

Conceptual view of the Grid

Leading to Portal (Super)Computing

http://www.sun.com/hpc/

Grid Application-Drivers
Old and New applications getting enabled due to
coupling of computers, databases, instruments, people,
etc:
(distributed) Supercomputing
Collaborative engineering
high-throughput computing
large scale simulation & parameter studies

Remote software access / Renting Software
Data-intensive computing
On-demand computing

Grid Components
Applications and Portals

Scientific

Engineering

Collaboration

…

Prob. Solving Env.

Development Environments and Tools

Languages

Libraries

Debuggers

Monitoring

Resource Brokers

Web enabled Apps

…

Distributed Resources Coupling Services

Comm.

Sign on & Security

Information

Process

Data Access

Web tools

…

QoS

Grid Apps.

Grid Tools

Grid Middleware

Local Resource Managers

Operating Systems

Computers

Queuing Systems

Clusters

Libraries & App Kernels

Networked Resources across
Organisations

Storage Systems

Data Sources

…

…

TCP/IP & UDP

Scientific Instruments

Grid Fabric

Many GRID Projects and Initiatives
Europe
USA

Globus
Legion
JAVELIN
AppLes
NASA IPG
Condor
Harness
NetSolve
NCSA Workbench
WebFlow
EveryWhere
and many more...

UNICORE
MOL
METODIS
Globe
Poznan
Metacomputing
CERN Data Grid
MetaMPI
DAS
JaWS
and many more...

Australia

Nimrod/G
EcoGrid and GRACE
DISCWorld

 PUBLIC FORUMS
 Computing Portals
 Grid Forum
 European Grid Forum
 IEEE TFCC!
 GRID’2000 and more.

 Public Grid Initiatives
 Distributed.net
 SETI@Home
 Compute Power Grid

 Japan
 Ninf
 Bricks
 and many more...

Literature on Cluster
Computing

Reading Resources..1
Internet & WWW
Computer Architecture:
http://www.cs.wisc.edu/~arch/www/

Linux Parallel Procesing
 http://yara.ecn.purdue.edu/~pplinux/Sites/

Solaris-MC
http://www.sunlabs.com/research/solaris-mc

Microprocessors: Recent Advances
 http://www.microprocessor.sscc.ru

Beowulf:
 http://www.beowulf.org

Metacomputing
 http://www.sis.port.ac.uk/~mab/Metacomputing/

Reading Resources..2
Books
In Search of Cluster
 by G.Pfister, Prentice Hall (2ed), 98

High Performance Cluster Computing
Volume1: Architectures and Systems
Volume2: Programming and Applications
 Edited by Rajkumar Buyya, Prentice Hall, NJ, USA.

Scalable Parallel Computing
 by K Hwang & Zhu, McGraw Hill,98

Cluster Computing Forum
IEEE Task Force on Cluster Computing
(TFCC)

http://www.ieeetfcc.org

TFCC Activities...
 Network Technologies
 OS Technologies
 Parallel I/O
 Programming Environments
 Java Technologies
 Algorithms and Applications
 >Analysis and Profiling
 Storage Technologies
 High Throughput Computing

TFCC Activities...
 High Availability
 Single System Image
 Performance Evaluation
 Software Engineering
 Education
 Newsletter
 Industrial Wing
 TFCC Regional Activities
 All the above have there own pages, see pointers from:
 http://www.ieeetfcc.org

TFCC Activities...
 Mailing list, Workshops, Conferences, Tutorials, Web-resources etc.
 Resources for introducing subject in senior undergraduate and
graduate levels.
 Tutorials/Workshops at IEEE Chapters..
 ….. and so on.
 FREE MEMBERSHIP, please join!
 Visit TFCC Page for more details:
 http://www.ieeetfcc.org (updated daily!).

TERIMA KASIH