grid computing 7700 fall 2005 lecture 4: scientific computing and hardware gabrielle allen...
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Grid Computing 7700Fall 2005
Lecture 4: Scientific Computing and Hardware
Gabrielle Allenallen@bit.csc.lsu.edu
http://www.cct.lsu.edu/~gallen
Basic Elements
CPU CPU
CPU CPU
DISK
Campus Network (LAN)
Machine Network
CPU CPU
CPU CPU
DISK
Campus Network (LAN)
Machine Network
Wide Area Network
Basic Elements
Distributed systems built from– Computing elements (processors)– Communication elements (networks)– Storage elements (disk, attached or
networked) New elements
– Visualization/interactive devices– Experimental and operational devices
Distributed Resources
Local workstations CCT Resources Campus/OCS Resources State/LONI Resources National Centers International Colleagues
Laws
Moores Law– Number of transistors on an integrated circuit will double every 18 months– http://en.wikipedia.org/wiki/Moores_law
“Kryders Law”– Hard disk capacity grows quicker than transistors– http://www.sciam.com/article.cfm?
chanID=sa006&colID=30&articleID=000B0C22-0805-12D8-BDFD83414B7F0000
Gilders Law– Total bandwidth of communication systems doubles every six months
Metcalfe’s Law– Value of a network is proportional to the square of the number of
nodes Amdahl’s Law
– Law of diminishing returns, maximum speedup restricted by slowest parts
– http://en.wikipedia.org/wiki/Amdahls_law
Question: So what about applications?
Compute Elements
Moore’s Law: #transistors on a chip (and clock speed) increase exponentially (double every 18 months)– Transistors = 20*2^[(year-1965)/1.5]– 1975 Intel 8080 has 4500 transistors, 100K
intructions/sec– 2003 Pentium IV has 221,000,000, 8 billion
instructions/sec Corollary: Price of a given level of
supercomputing power halves every 18 months Price decrease means that supercomputers
now usually built from “commodity” processors– IA32, PowerPC, “emotion engine”
Compute Elements
Clock speed Cache hierarchy Floating point registers Main memory Internal bandwidths Etc, etc Need powerful operating systems,
compilers, applications to leverage all this
Communication Elements
Links, routers, switches, name servers, protocols Infrastructure evolves slowly (politics, large scale
changes, money) Gilder's Law: total bandwidth of communication systems
doubles every six months Change in LAN to desktops
– 100 mbps shared– 100 mbps switched– 1 gbps – 10 gbps
Clusters: GigE (TCP/IP and MPICH/LAM) standard, Myricom/Quadrics (own MPI drivers) better performance, infiniband/fibrechannel different architecture
Network Speeds
Analog modem: 57 kbps GPRS: 114 kbps Bluetooth: 723 kbps T-1: 1.5 Mbps Eth 10Base-X: 10Mbps 802.11b (WiFi) 11 Mbps T-3: 45 Mbps OC-1: 52 Mbps Fast Eth 100Base-X: 100
Mbps
OC-12: 622 Mbps GigEth 1000Base-X: 1
Gbps OC-24: 1.2 Gbps OC-48: 2.5 Gbps OC-192: 10 Gbps 10 GigEth: 10 Gbps OC-3072: 160 Gbps
My Cox Cable– Upload: 35 KB/s– Download 250 KB/s
CCT “is” to supermike– Up/down: 5000 KB/s
Communication Elements
Interconnect Type
Short Message Latency (microsec)
Peak Bandwidth (mbps)
Bidirectional Bandwidth (mbps)
Approximate cost per port
Gigabit Ethernet
100 ~65 ~130 $100
Myrinet 9 280 500 $1000
Quadrics 5 300 500 $3000
Storage Elements
Magnetic tape/Magnetic disk Magnetic disk
– Properties: density/rotation/cost– 1970-1988 density improvements 29% per year– 1988-now density improvements 60% per year– Standard in PCs: 500mb (1995), 2gb(1997), 100gb
(2002)– Performance not increasing so fast
• Peak transfer (~100mbs)• Seek times (3-5ms) [bottleneck]
Grids: cost of storage neglibable, high speed networks make large data libraries attractive
The Future (??)Machine Compute Memory Disk Network
2003 PC 8 g-op/s 512 mb 128 gb 1 gb/s
2003 SC 80 t-op/s 50 tb 1280 pb 10 tb/s
2008 PC 64 g-op/s 16 gb 2 tb 10 gb/s
2008 SC 640 t-op/s 160 tb 20 pb 100 tb/s
2013 PC 512 g-op/s 256 gb 32 tb 100 gb/s
2013 SC 5 p-op/s 2.6 pb 320 pb 1 pb/s
1 mega = 10^61 giga = 10^91 tera = 10^121 peta = 10^15
TeraGrid:40 TFlop/s6 TB memory1 Petabytes storage10 Gigabits/s
Earth Simulator:40 TFlop/s10 TB memory2.5 Petabytes storage13 Gigabits/s
DOE BlueGene:367 TFlop/s16 TB memory400 Terabyte storage
Supercomputers Definition of supercomputer
– Machine on top500.org ?• http://www.top500.org/lists/plists.php?Y=2005&M=06
– Machine costing over $1M ?– Basically highest end machines
Top 3 (2005)– DOE BlueGene/L (USA) 66K procs/137 TF– IBM BGW (USA) 41K procs/91 TF– NASA Columbia (USA) 10K procs/52TF
Top 3 (2003)– Earth Simulator (JAPAN) 5K procs/36 TF (6)– ASCI Q (USA) 8K procs/14 TF (12)– G5 Cluster (USA) 2k procs/12 TF (14)
Others– 18 IBM (China)– 147 Supermike (LSU !!!)
www.webopedia.com
The fastest type of computer. Supercomputers are very expensive
and are employed for specializedapplications that require immense amounts of mathematical calculations. For example, weather
forecasting requires a supercomputer. Other uses of supercomputers include
animated graphics, fluid dynamic calculations, nuclear energy research, and petroleum exploration.The chief difference between a supercomputer
and a mainframe is that a supercomputer channels all its power into executing a few programs as fast
as possible, whereas a mainframe uses its power to execute many programs
concurrently.
Architectural Classes
Flynn (1972): classification based on the way system manipulates instruction and data streams:
SISD Single Instruction Single Data– One instruction stream executed serially.– Conventional workstations
SIMD Single Instruction Multiple Data– Large (many thousands) number of processing units– All execute same instruction on different data in lockstep– Vector processors (NEC SX-6i) acting on arrays of data
MISD Multiple Instruction Single Data– No machines built
MIMD Multiple Instruction Multiple Data– Different to SISD because instructions/data are related
More Classification
Shared Memory Systems– Multiple CPUs sharing same address space– One memory accessed by all processors equally– Location of data not important to user– Can be SIMD (single processor vector processor) or MIMD– OpenMP http://www.openmp.org/index.cgi?faq
Distributed Memory Systems– Each CPU has own memory– CPUs are connected by network– Location of data important– Can be SIMD (lock step example before) or MIMD (large
variety of network topologies)– Distributed processing takes DM-MIMD to extreme
Message Passing
Essential for DM machines, but often also used for SM machines for compatibility– MPI Message Passing interface– PVM Parallel Virtual Machine
DM-MIMD
Fast growing section, best performance. Need to balance computation and communication performance in machine design (and upgrades)
User has to distribute data between processors User has to perform data exchange between processors
explicitly Slow compared to SM machines to access data on other
processors Programming models/algorithms important Programming environments can make this easier (e.g.
Cactus Framework http://www.cactuscode.org handles data distribution, communications, IO, …)
Same programming models need to be extended to Grid computing
ccNUMA
Cache Coherent Non Uniform Memory Access Build systems from SMPs (symmetric
multiprocessing nodes) SMPs consist of up to ~16 processors
connected by a crossbar which share same memory
Each node is a SM-MIMD, but with different memory access times for different processors (memory is physically distributed)
Nodes then connecting in a different way Computational scientists like these machines
DM-MIMD
Processor topology and interconnects very important– Hypercube (with 2^d nodes number of steps
between two nodes at most d, possible to simulate other topologies)
– Fat tree (simple tree structure with more connections at higher levels to ease conjestion)
– 2D/3D mesh structure (many apps map well to this, avoids expense)
– Crossbars (connecting up to around 64 processors, can be hierarchical)
Details should be hidden from application programmers, but for performance need to be aware
Virtual Shared Memory
Kendall Square Research Systems tried to implement at hardware level
High Performance Fortran– HPF Specification 1993– Simulates a virtual shared memory at a software
level– Programming directives distribute data across
processors– Looks like shared memory machine to user
Some vendors have propriety virtual shared memory programming models by providing global address space
Network Eras
Past (1969-1988)– ARPANET/NSFNET
Current (1988-2005) Future (2005-)
Historical network maps– http://www.cybergeography.org/atlas/historical.html
Network Infrastructure
Chapter 30 (The Grid 2) Network infrastructure is the foundation
on which Grids are built Composition of local and wide area
services, transport protocols and services, routing protocols and network services, link protocols and physical media
One example of network infrastructure in the Internet (core protocols TCP/IP)
Protocol Agreed-upon format for transmitting data between two
devices which determines:– The type of error checking to be used– Any data compression method– How sending device indicates it has finished sending a message– How receiving device indicates it has received a message
Various standard protocols: differ in simplicity, reliability, performance.
Computer/device must support the right ones to communicate with other computers.
Implemented either in hardware or in software http://www.protocols.com/protocols.htm
Slow to Change
Internet has not changed much since 1983 (when TCP/IP deployed), which does make is stable, but still don’t really have envisaged services:– Multicast (one-to-many communication)– Network Reservation– Quality of Service
New protocols peer-to-peer file sharing and instant messaging
New technology coupled to applications drive change: e-mail, web/file-sharing, video streaming
Past: 1969-1988 ARPANET (1969) 56-kbps lines
– Experiment to investigate resource sharing and remote access
– Added interface message processor (IMP) at each end of network (our routers), provided flexibility for lower levels and higher level applications
– Success from: freely available documentation and source code; software bundled with new machines; use for teaching; community development vs. proprietary
NSFNET (1985) 45-mpbs lines – Connect academic HPC centers
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ARPANET: 1971
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ARPANET: 1980
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NSFNET: 1991
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Past: 1969-1988
Driving application: e-mail, remote file access, remote job control (drove basic protocols)
Network technology: WAN links lines leased from telephone companies. Xerox Palo Alto Research Center (PARC) created Ethernet (3 mbps) (alternatives token ring (IBM), …). Workstations appear bundled with network protocols. PCs on the network as interface costs dropped and processors became more powerful.
Past: 1969-1988
Protocols and Services– telnet, file transfer protocol, e-mail– Underlying transport protocol TCP (stream
of bytes which can be opened or closed, data can be sent or received)
– Machine location: Domain Name System (DNS) (replaced list of named files)
• Hierarchical, distributed, redundant
Past: 1969-1988
System Integration– ARPANET: assumed central network operations center– NSFNET: introduced hierarchical system, toplevel
backbone network connecting to regional networks connecting to campuses
Packet switching strategy was important (using computing power to optimize communication)
Single communication model was important because it allowed so many people to be connected driving future development.
Present: 1988-2005
Internet today: complex structure of backbone networks and regional networks
Increased role of private sector (e.g. AT&T, BellSouth), who basically control our network now.
LSU Campus
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LANet
Louisiana statewide network: Office of Telecommunications Management, state agencies, higher education: 6Mbps -> $2450 a month
http://www.state.la.us/otm/lanet/
Quest
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Bell South
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Baton Rouge: 4 DS3 to New Orleans, 1 DS3 to Houston
Abeline (Internet2)
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http://abilene.internet2.edu/maps-lists/Traffic: http://loadrunner.uits.iu.edu/weathermaps/abilene/
National Lambda Rail
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http://www.nationallambdarail.org/architecture.html
National Lambda Rail
Global Terabit Research Network
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Required Reading
Overview of Recent Supercomputers– http://www.euroben.nl/reports/overview05a.pdf
Concentrate on pages 1 to 32, you do not need to learn this, just get an appreciation of the concepts.
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