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Technical computing: Observations on an ever changing, occasionally repetitious, environment. Los Alamos National Laboratory 17 May 2002. A brief, simplified history of HPC. Sequential & data parallelism using shared memory, Cray’s Fortran computers 60-02 (US:90)

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technical computing observations on an ever changing occasionally repetitious environment

Technical computing: Observations on an ever changing, occasionally repetitious, environment

Los Alamos National Laboratory

17 May 2002

a brief simplified history of hpc
A brief, simplified history of HPC
  • Sequential & data parallelism using shared memory, Cray’s Fortran computers 60-02 (US:90)
  • 1978: VAXen threaten general purpose centers…
  • NSF response: form many centers 1988 - present
  • SCI: Search for parallelism to exploit micros 85-95
  • Scalability: “bet the farm” on clusters.Users “adapt” to clusters aka multi-computers with LCD program model, MPI. >95
  • Beowulf Clusters adopt standardized hardware and Linus’s software to create a standard! >1995
  • “Do-it-yourself” Beowulfs impede new structures and threaten g.p. centers >2000
  • 1997-2002: Let’s tell NEC they aren’t “in step”.
  • High speed networking enables peer2peer computing and the Grid. Will this really work?
outline
Outline
  • Retracing scientific computing evolution: Cray, SCI & “killer micros”, ASCI, & Clusters kick in.
  • Current taxonomy: clusters flavors
  • deja’vu rise of commodity computng: Beowulfs are a replay of VAXen c1978
  • Centers: 2+1/2 at NSF; BRC on CyberInfrastructure urges 650M/year
  • Role of Grid and Peer-to-peer
  • Will commodities drive out or enable new ideas?
darpa sci c1985 1995 prelude to doe s asci
DARPA SCI: c1985-1995;prelude to DOE’s ASCI
  • Motivated by Japanese 5th Generation … note the creation of MCC
  • Realization that “killer micros” were
  • Custom VLSI and its potential
  • Lots of ideas to build various high performance computers
  • Threat and potential sale to military
processor architectures
Processor Architectures?

VECTORS

VECTORS

OR

CS View

MISC >> CISC >> Language directed

RISC >> Super-scalar >>Extra-Long Instruction Word

Caches: mostly alleviate need for memory B/W

SC Designers View

RISC >> VCISC (vectors)>>

Massively parallel (SIMD) (multiple pipelines)

Memory B/W = perf.

the bell hillis bet c1991 massive 1000 parallelism in 1995
The Bell-Hillis Bet c1991Massive (>1000) Parallelism in 1995

TMC

World-wide

Supers

TMC

World-wide

Supers

TMC

World-wide

Supers

Applications

Petaflops / mo.

Revenue

results from darpa s sci c1983
Results from DARPA’s SCI c1983
  • Many research and construction efforts … virtually all new hardware efforts failed except Intel and Cray.
  • DARPA directed purchases… screwed up the market, including the many VC funded efforts.
  • No Software funding!
  • Users responded to the massive power potential with LCD software.
  • Clusters, clusters, clusters using MPI.
  • It’s not scalar vs vector, its memory bandwidth!
    • 6-10 scalar processors = 1 vector unit
    • 16-64 scalars = a 2 – 6 processor SMP
dead supercomputer society
ACRI

Alliant

American Supercomputer

Ametek

Applied Dynamics

Astronautics

BBN

CDC

Convex

Cray Computer

Cray Research

Culler-Harris

Culler Scientific

Cydrome

Dana/Ardent/Stellar/Stardent

Denelcor

Elexsi

ETA Systems

Evans and Sutherland Computer

Floating Point Systems

Galaxy YH-1

Goodyear Aerospace MPP

Gould NPL

Guiltech

Intel Scientific Computers

International Parallel Machines

Kendall Square Research

Key Computer Laboratories

MasPar

Meiko

Multiflow

Myrias

Numerix

Prisma

Tera

Thinking Machines

Saxpy

Scientific Computer Systems (SCS)

Soviet Supercomputers

Supertek

Supercomputer Systems

Suprenum

Vitesse Electronics

Dead Supercomputer Society
what a difference 25 years and spending 10x makes
What a difference 25 years AND spending >10x makes!

ESRDC:40 Tflops. 640 nodes (8 - 8GFl P.vec/node)

LLNL 150 Mflops machine room c1978

computer types
Computer types

-------- Connectivity--------

WAN/LAN SAN DSM SM

Netwrked

Supers…

VPPuni

NEC mP

NEC super

Cray X…T

(all mPv)

Old

World

Clusters

GRID& P2P

micros vector

Legion

Condor

Beowulf

NT clusters

T3E

SP2(mP)

NOW

SGI DSM

clusters &

SGI DSM

Mainframes

Multis

WSs PCs

top500 taxonomy everything is a cluster aka multicomputer
Top500 taxonomy… everything is a cluster aka multicomputer
  • Clusters are the ONLY scalable structure
    • Cluster: n, inter-connected computer nodes operating as one system. Nodes: uni- or SMP. Processor types: scalar or vector.
  • MPP= miscellaneous, not massive (>1000), SIMD or something we couldn’t name
  • Cluster types. Implied message passing.
    • Constellations = clusters of >=16 P, SMP
    • Commodity clusters of uni or <=4 Ps, SMP
    • DSM: NUMA (and COMA) SMPs and constellations
    • DMA clusters (direct memory access) vs msg. pass
    • Uni- and SMPvector clusters:Vector Clusters and Vector Constellations
linux a web phenomenon
Linux - a web phenomenon
  • Linus Tovald - writes news reader for his PC
  • Puts it on the internet for others to play
  • Others add to it contributing to open source software
  • Beowulf adopts early Linux
  • Beowulf adds Ethernet drivers for essentially all NICs
  • Beowulf adds channel bonding to kernel
  • Red Hat distributes Linux with Beowulf software
  • Low level Beowulf cluster management tools added
the challenge leading to beowulf
The Challenge leading to Beowulf
  • NASA HPCC Program begun in 1992
  • Comprised Computational Aero-Science and Earth and Space Science (ESS)
  • Driven by need for post processing data manipulation and visualization of large data sets
  • Conventional techniques imposed long user response time and shared resource contention
  • Cost low enough for dedicated single-user platform
  • Requirement:
    • 1 Gflops peak, 10 Gbyte, < $50K
  • Commercial systems: $1000/Mflops or 1M/Gflops
the virtuous economic cycle drives the pc industry beowulf
The Virtuous Economic Cycle drives the PC industry… & Beowulf

Attracts suppliers

Greater availability

@ lower cost

Competition

Volume

Standards

DOJ

Utility/value

Innovation

Creates apps, tools, training,

Attracts users

lessons from beowulf
Lessons from Beowulf
  • An experiment in parallel computing systems
  • Established vision- low cost high end computing
  • Demonstrated effectiveness of PC clusters for some (not all) classes of applications
  • Provided networking software
  • Provided cluster management tools
  • Conveyed findings to broad community
  • Tutorials and the book
  • Provided design standard to rally community!
  • Standards beget: books, trained people, software … virtuous cycle that allowed apps to form
  • Industry begins to form beyond a research project

Courtesy, Thomas Sterling, Caltech.

clusters next steps
Clusters: Next Steps
  • Scalability…
  • They can exist at all levels: personal, group, … centers
  • Clusters challenge centers… given that smaller users get small clusters
disk evolution

Kilo

Mega

Giga

Tera

Peta

Exa

Zetta

Yotta

Disk Evolution
  • Capacity:100x in 10 years 1 TB 3.5” in 2005 20 TB? in 2012?!
  • System on a chip
  • High-speed SAN
  • Disk replacing tape
  • Disk is super computer!
intermediate step shared logic
Intermediate Step: Shared Logic

Snap

~1TB 12x80GB NAS

  • Brick with 8-12 disk drives
  • 200 mips/arm (or more)
  • 2xGbpsEthernet
  • General purpose OS
  • 10k$/TB to 100k$/TB
  • Shared
    • Sheet metal
    • Power
    • Support/Config
    • Security
    • Network ports
  • These bricks could run applications e.g. SQL, Mail…

NetApp

~.5TB

8x70GB NAS

Maxstor

~2TB 12x160GB NAS

IBM TotalStorage

~360GB

10x36GB NAS

rlx cluster in a cabinet
RLX “cluster” in a cabinet
  • 366 servers per 44U cabinet
    • Single processor
    • 2 - 30 GB/computer (24 TBytes)
    • 2 - 100 Mbps Ethernets
  • ~10x perf*, power, disk, I/O per cabinet
  • ~3x price/perf
  • Network services… Linux based

*42, 2 processors, 84 Ethernet, 3 TBytes

computing in small spaces @ lanl rlx cluster in building with no a c
Computing in small spaces @ LANL(RLX cluster in building with NO A/C)

240 processors @2/3 GFlops

Fill the 4 racks -- gives a Teraflops

slide27

“The networks becomes the system.”- Bell 2/10/82 Ethernet announcement with Noyce (Intel), and Liddle (Xerox)“The network become the computer.” SUN Slogan >1982“The network becomes the system.” GRID mantra c1999

computing snap built entirely from pcs

Legacy

mainframes &

minicomputers

servers & terms

Portables

Legacy

mainframe &

minicomputer

servers & terminals

ComputingSNAPbuilt entirelyfrom PCs

Wide-area

global

network

Mobile

Nets

Wide & Local

Area Networks

for: terminal,

PC, workstation,

& servers

Person

servers

(PCs)

scalable computers

built from PCs

A space, time (bandwidth), & generation scalable environment

Person

servers

(PCs)

Centralized

& departmental

uni- & mP servers

(UNIX & NT)

Centralized

& departmental

servers buit from

PCs

???

TC=TV+PC

home ...

(CATV or ATM

or satellite)

the virtuous cycle of bandwidth supply and demand

Increased Demand

Increase Capacity(circuits & bw)

Create new

service

Lower response time

WWW

Audio

Video

Voice!

The virtuous cycle of bandwidth supply and demand

Incompence ?

Standards

Telnet & FTP

EMAIL

internet ii concerns given 0 5b cost
Internet II concerns given $0.5B cost
  • Very high cost
    • $(1 + 1) / GByte to send on the net; Fedex and 160 GByte shipments are cheaper
    • DSL at home is $0.15 - $0.30
  • Disks cost $1/GByte to purchase!
  • Low availability of fast links (last mile problem)
    • Labs & universities have DS3 links at most, and they are very expensive
    • Traffic: Instant messaging, music stealing
  • Performance at desktop is poor
    • 1- 10 Mbps; very poor communication links
scalable computing the effects
Scalable computing: the effects
  • They come in all sizes; incremental growth 10 or 100 to 10,000 (100X for most users)debug vs run; problem growth
  • Allows compatibility heretofore impossible1978: VAX chose Cray Fortran1987: The NSF centers went to UNIX
  • Users chose sensible environment
    • Acquisition and operational costs & environments
    • Cost to use as measured by user’s time
  • The role of gp centers e.g. NSF, statex is unclear. Necessity for support?
    • Scientific Data for a given community…
    • Community programs and data
    • Manage GRIDdiscipline
  • Are clusters ≈ Gresham’s Law? Drive out alts.