CIS 429/529 2007 Parallel Arch. Intro Parallel Computer Architecture Slides adapted from those of...

CIS 429/529 2007 Parallel Arch. Intro

Parallel Computer Architecture

Slides adapted from those of

David Patterson and David Culler, UC Berkeley

Krste Asanovic, MIT

2CIS 429/529 2007 Parallel Arch. Intro

Lecture Roadmap

– Motivation– Flynn’s Taxonomy– History of parallel computers– SIMD - vector architecture (light coverage)– MIMD - shared memory and distributed memory

architectures (in depth with focus on memory coherence)– Performance of parallel computers

1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006

Performance (vs. VAX-11/780)

25%/year

52%/year

??%/year

Uniprocessor Performance (SPECint)

• VAX : 25%/year 1978 to 1986• RISC + x86: 52%/year 1986 to 2002• RISC + x86: ??%/year 2002 to present

From Hennessy and Patterson, Computer Architecture: A Quantitative Approach, 4th edition, 2006

CIS 429/529 2007 Parallel Arch. Intro4

0 1 2 3 4 5 6+0

0 5 10 150

Fraction of total cycles (%)

Number of instructions issued

Speedup

Instructions issued per cycle

Limits of ILP

• Infinite resources and fetch bandwidth, perfect branch prediction and renaming

The Rise of Multiprocessors• Advantage of leveraging design investment by

replication – Rather than new unique designs

• Major need in the scientific computing arena

• Growth in data-intensive applications– Data bases, file servers, …

• Growing interest in servers, server perf.

• Increasing desktop perf. less important – Outside of graphics

• Improved understanding in how to use multiprocessors effectively

– Especially server where significant natural TLP

Definition: Parallel Computer

•Definition: “A parallel computer is a collection of processing elements that cooperate and communicate to solve large problems fast.”

Almasi and Gottlieb, Highly Parallel Computing ,1989

•Parallel Architecture = Computer Architecture + Communication Architecture

Parallel Architecture Design Issues

–How large a collection of processors?–How powerful are processing elements?–How do they cooperate and communicate?–How are data transmitted between processors?–Where to put the memory and I/O? –What type of interconnection?–What are HW and SW primitives for programmer?–Does it translate into performance?

Flynn’s Taxonomy

• Flynn classified by data and control streams in 1966

• SIMD Data Level Parallelism

• MIMD Thread Level Parallelism

• MIMD popular because – Flexible: N pgms or 1 multithreaded pgm

– Cost-effective: same in desktop & MIMD

Single Instruction Single Data (SISD)

(Uniprocessor)

Single Instruction Multiple Data SIMD

(Vector, CM-2)

Multiple Instruction Single Data (MISD)

(????)

Multiple Instruction Multiple Data MIMD

(supercomputers, clusters, SMP servers)

M.J. Flynn, "Very High-Speed Computers", Proc. of the IEEE, V 54, 1900-1909, Dec. 1966.

Scientific Supercomputing

• Proving ground and driver for innovative architecture and techniques

– Market smaller relative to commercial as MPs become mainstream

– Dominated by vector machines starting in 70s

– Microprocessors have made huge gains in floating-point performance

» high clock rates

» pipelined floating point units (e.g., multiply-add every cycle)

» instruction-level parallelism

» effective use of caches (e.g., automatic blocking)

• Large-scale multiprocessors eventually dominate over vector supercomputers

Scientific Computing Demand

Engineering Computing Demand

• Large parallel machines a mainstay in many industries

– Petroleum (reservoir analysis)– Automotive (crash simulation, drag analysis, combustion

efficiency), – Aeronautics (airflow analysis, engine efficiency, structural

mechanics, electromagnetism), – Computer-aided design– Pharmaceuticals (molecular modeling)– Visualization

» in all of the above» entertainment (films like Toy Story)» architecture (walk-throughs and rendering)

– Financial modeling (yield and derivative analysis)– etc.

1980 1985 1990 1995

1 MIPS

10 MIPS

100 MIPS

1 GIPS

Sub-BandSpeech Coding

200 WordsIsolated SpeechRecognition

SpeakerVerification

CELPSpeech Coding

ISDN-CD StereoReceiver

5,000 WordsContinuousSpeechRecognition

HDTV Receiver

CIF Video

1,000 WordsContinuousSpeechRecognitionTelephone

NumberRecognition

10 GIPS

• Also CAD, Databases, …

Applications: Speech and Image Processing

Commercial Computing

• Relies on parallelism for high end– Computational power determines scale of business that can

be handled

• Databases, online-transaction processing, decision support, data mining, data warehousing

Summary of Application Trends

• Transition to parallel computing has occurred for scientific and engineering computing

• In rapid progress in commercial computing– Database and transactions as well as financial

– Usually smaller-scale, but large-scale systems also used

• Desktop also uses multithreaded programs, which are a lot like parallel programs

• Demand for improving throughput on sequential workloads

• Solid application demand exists and will increase

CIS 429/529 2007 Parallel Arch. Intro15

Economics

• Commodity microprocessors not only fast but CHEAP– Development costs tens of millions of dollars

– BUT, many more are sold compared to supercomputers

– Crucial to take advantage of the investment, and use the commodity building block

• Multiprocessors being pushed by software vendors (e.g. database) as well as hardware vendors

• Standardization makes small, bus-based SMPs commodity

• Desktop: few smaller processors versus one larger one

• Multiprocessor on a chip -> multicore

Supercomputer Applications

Typical application areas• Military research (nuclear weapons, cryptography)• Scientific research• Weather forecasting• Oil exploration• Industrial design (car crash simulation)

All involve huge computations on large data sets

In 70s-80s, Supercomputer Vector Machine

Multiprocessor Trends

CRAY CS6400

SGI Challenge

Sequent B2100

Sequent B8000

Symmetry81

Symmetry21

SS690MP 140 SS690MP 120

AS8400

HP K400AS2100SS20

SS1000E

SS1000

P-ProSGI PowerSeries

Sun E6000

SC2000ESun SC2000SGI PowerChallenge/XL

SunE10000

1984 1986 1988 1990 1992 1994 1996 1998

Number of processors

LINPACK (GFLOPS)

n CRAY peakl MPP peak

Xmp/416(4)

Ymp/832(8) nCUBE/2(1024)iPSC/860

CM-2CM-200

Paragon XP/S

C90(16)

ASCI Red

T932(32)

Paragon XP/S MP(1024)

Paragon XP/S MP(6768)

10,000

1985 1987 1989 1991 1993 1995 1996

Raw Parallel Performance: LINPACK

Whither Parallel Machines?

• 1997, 500 fastest machines in the world: 319 MPPs, 73 bus-based shared memory (SMP), 106 parallel vector processors (PVP)

• 2000, 381 of 500 fastest: 144 IBM SP (~cluster), 121 Sun (bus SMP), 62 SGI (NUMA SMP), 54 Cray (NUMA SMP)

Vector Supercomputers

Epitomized by Cray-1, 1976:

Scalar Unit + Vector Extensions• Load/Store Architecture

• Vector Registers

• Vector Instructions

• Hardwired Control

• Highly Pipelined Functional Units

• Interleaved Memory System

• No Data Caches

• No Virtual Memory

Older Vector Machines

Machine Year Clock Regs Elements FUs LSUsCray 1 1976 80 MHz 8 64 6 1Cray XMP 1983 120 MHz 8 64 8 2 L, 1 SCray YMP 1988 166 MHz 8 64 8 2 L, 1 SCray C-90 1991 240 MHz 8 128 8 4Cray T-90 1996 455 MHz 8 128 8 4Conv. C-1 1984 10 MHz 8 128 4 1Conv. C-4 1994 133 MHz 16 128 3 1Fuj. VP200 1982 133 MHz 8-256 32-1024 3 2Fuj. VP300 1996 100 MHz 8-256 32-1024 3 2NEC SX/2 1984 160 MHz 8+8K 256+var 16 8NEC SX/3 1995 400 MHz 8+8K 256+var 16 8

Cray-1 (1976)

Single PortMemory

16 banks of 64-bit words

+ 8-bit SECDED

80MW/sec data load/store

320MW/sec instructionbuffer refill

4 Instruction Buffers

64-bitx16 NIP

( (Ah) + j k m )

64T Regs

( (Ah) + j k m )

64 B Regs

S0S1S2S3S4S5S6S7

A0A1A2A3A4A5A6A7

FP Add

FP Mul

FP Recip

Int Add

Int Logic

Int Shift

Pop Cnt

Addr Add

Addr Mul

memory bank cycle 50 ns processor cycle 12.5 ns (80MHz)

V0V1V2V3V4V5V6V7

Vi V. Mask

V. Length64 Element Vector Registers

Vector Programming Model

+ + + + + +

[0] [1] [VLR-1]

Vector Arithmetic Instructions

ADDV v3, v1, v2 v3

Scalar Registers

r15Vector Registers

[0] [1] [2] [VLRMAX-1]

VLRVector Length Register

v1Vector Load and

Store Instructions

LV v1, r1, r2

Base, r1 Stride, r2Memory

Vector Register

Vector Code Example

# Scalar Code

LI R4, 64

L.D F0, 0(R1)

L.D F2, 0(R2)

ADD.D F4, F2, F0

S.D F4, 0(R3)

DADDIU R1, 8

DADDIU R2, 8

DADDIU R3, 8

DSUBIU R4, 1

BNEZ R4, loop

# Vector Code

LI VLR, 64

LV V1, R1

LV V2, R2

ADDV.D V3, V1, V2

SV V3, R3

# C code

for (i=0; i<64; i++)

C[i] = A[i] + B[i];

Vector Instruction Set Advantages

• Compact– one short instruction encodes N operations

• Expressive, tells hardware that these N operations:– are independent

– use the same functional unit

– access disjoint registers

– access registers in the same pattern as previous instructions

– access a contiguous block of memory (unit-stride load/store)

– access memory in a known pattern (strided load/store)

• Scalable– can run same object code on more parallel pipelines or lanes

Vector Arithmetic Execution

• Use deep pipeline (=> fast clock) to execute element operations

• Simplifies control of deep pipeline because elements in vector are independent (=> no hazards!)

V3 <- v1 * v2

Six stage multiply pipeline

Vector Memory System

0 1 2 3 4 5 6 7 8 9 A B C D E F

Base StrideVector Registers

Memory Banks

Address Generator

Cray-1, 16 banks, 4 cycle bank busy time, 12 cycle latency• Bank busy time: Cycles between accesses to same bank

Vector Instruction ExecutionADDV C,A,B

A[3] B[3]

A[4] B[4]

A[5] B[5]

A[6] B[6]

Execution using one pipelined functional unit

A[12] B[12]

A[16] B[16]

A[20] B[20]

A[24] B[24]

A[13] B[13]

A[17] B[17]

A[21] B[21]

A[25] B[25]

A[14] B[14]

A[18] B[18]

A[22] B[22]

A[26] B[26]

A[15] B[15]

A[19] B[19]

A[23] B[23]

A[27] B[27]

Execution using four pipelined

functional units

Vectors Are Inexpensive

Scalar• N ops per cycle

2) circuitry• HP PA-8000

• 4-way issue• reorder buffer:

850K transistors• incl. 6,720 5-bit register

number comparators

Vector• N ops per cycle

2) circuitry

• T0 vector micro• 24 ops per cycle• 730K transistors total

• only 23 5-bit register number comparators

• No floating point

Vectors Lower Power

Vector• One inst fetch, decode,

dispatch per vector

• Structured register accesses

• Smaller code for high performance, less power in instruction cache misses

• Bypass cache

• One TLB lookup pergroup of loads or stores

• Move only necessary dataacross chip boundary

Single-issue Scalar• One instruction fetch, decode,

dispatch per operation• Arbitrary register accesses,

adds area and power• Loop unrolling and software

pipelining for high performance increases instruction cache footprint

• All data passes through cache; waste power if no temporal locality

• One TLB lookup per load or store

• Off-chip access in whole cache lines

Superscalar Energy Efficiency Even Worse

Vector• Control logic grows

linearly with issue width• Vector unit switches

off when not in use

• Vector instructions expose parallelism without speculation

• Software control ofspeculation when desired:

– Whether to use vector mask or compress/expand for conditionals

Superscalar• Control logic grows

quadratically with issue width

• Control logic consumes energy regardless of available parallelism

• Speculation to increase visible parallelism wastes energy

Vector Applications

Limited to scientific computing?• Multimedia Processing (compress., graphics, audio synth, image proc.)

• Standard benchmark kernels (Matrix Multiply, FFT, Convolution, Sort)• Lossy Compression (JPEG, MPEG video and audio)• Lossless Compression (Zero removal, RLE, Differencing, LZW)• Cryptography (RSA, DES/IDEA, SHA/MD5)

• Speech and handwriting recognition• Operating systems/Networking (memcpy, memset, parity, checksum)• Databases (hash/join, data mining, image/video serving)• Language run-time support (stdlib, garbage collection)

• even SPECint95

Newer Vector Computers

• Cray X1– MIPS like ISA + Vector in CMOS

• NEC Earth Simulator– Fastest computer in world for 3 years; 40 TFLOPS

– 640 CMOS vector nodes

Key Architectural Features of X1

New vector instruction set architecture (ISA)

– Much larger register set (32x64 vector, 64+64 scalar)

– 64- and 32-bit memory and IEEE arithmetic

– Based on 25 years of experience compiling with Cray1 ISA

Decoupled Execution– Scalar unit runs ahead of vector unit, doing addressing and control

– Hardware dynamically unrolls loops, and issues multiple loops concurrently– Special sync operations keep pipeline full, even across barriers

Allows the processor to perform well on short nested loops

Scalable, distributed shared memory (DSM) architecture

– Memory hierarchy: caches, local memory, remote memory

– Low latency, load/store access to entire machine (tens of TBs)

– Processors support 1000’s of outstanding refs with flexible addressing– Very high bandwidth network

– Coherence protocol, addressing and synchronization optimized for DM

A Modern Vector Super: NEC SX-6 (2003)

• CMOS Technology– 500 MHz CPU, fits on single chip

– SDRAM main memory (up to 64GB)

• Scalar unit– 4-way superscalar with out-of-order and speculative

execution

– 64KB I-cache and 64KB data cache

• Vector unit– 8 foreground VRegs + 64 background VRegs (256x64-bit

elements/VReg)

– 1 multiply unit, 1 divide unit, 1 add/shift unit, 1 logical unit, 1 mask unit

– 8 lanes (8 GFLOPS peak, 16 FLOPS/cycle)

– 1 load & store unit (32x8 byte accesses/cycle)

– 32 GB/s memory bandwidth per processor

• SMP structure– 8 CPUs connected to memory through crossbar

– 256 GB/s shared memory bandwidth (4096 interleaved banks)

SIMD Vector Summary

• Vector is alternative model for exploiting ILP• If code is vectorizable, then simpler hardware,

more energy efficient, and better real-time model than Out-of-order machines

• Design issues include number of lanes, number of functional units, number of vector registers, length of vector registers, exception handling, conditional operations

• Fundamental design issue is memory bandwidth– With virtual address translation and caching

• Will multimedia popularity revive vector architectures?

MIMD Architectures

• 2 classes of multiprocessors wrt memory:

1. Centralized Memory Multiprocessor • < few dozen processor chips (and < 100 cores) in 2006

• Small enough to share single, centralized memory

2. Physically Distributed-Memory multiprocessor• Larger number chips and cores (greater scalability)

• BW demands Memory distributed among processors

Centralized vs. Distributed Memory

Interconnection network

Mem Mem

Interconnection network

Mem Mem

Centralized Memory Distributed Memory

Centralized Memory Multiprocessor

• Also called symmetric multiprocessors (SMPs) because single main memory has a symmetric relationship to all processors

• Large caches single memory can satisfy memory demands of small number of processors

• Can scale to a few dozen processors by using a switch and by using many memory banks

• Although scaling beyond that is technically conceivable, it becomes less attractive as the number of processors sharing centralized memory increases

Distributed Memory Multiprocessor

• Pro: Cost-effective way to scale memory bandwidth

• If most accesses are to local memory

• Pro: Reduces latency of local memory accesses

• Con: Communicating data between processors more complex

• Con: Must change software to take advantage of increased memory BW

2 Models for Communication and Memory Architecture1. Communication occurs by explicitly passing

messages among the processors: message-passing multiprocessors

2. Communication occurs through a shared address space (via loads and stores): shared memory multiprocessors either• UMA (Uniform Memory Access time) for shared

address, centralized memory MP• NUMA (Non Uniform Memory Access time

multiprocessor) for shared address, distributed memory MP

• In past, confusion whether “sharing” means sharing physical memory (Symmetric MP) or sharing address space

Performance of Parallel Processing

• First challenge is % of program inherently sequential

• Suppose 80X speedup from 100 processors. What fraction of original program can be sequential?

Challenges of Parallel Processing

• Second challenge is long latency to remote memory

• Suppose 32 CPU NUMA:base CPI is 0.5, remote access make up 0.2% of instructions but take 400 CPI.

• What is performance impact?

a. 1.5X

b. 2.0X

c. 2.5X

Speedup

• Speedup (p processors) =

• Speedup fixed problem (p processors) =

Performance (p processors)

Performance (1 processor)

Time (1 processor)

Time (p processors)

Speedup - what’s happening?

• Ideally, linear speedup• In reality, communication overhead reduces• Suprisingly, super-linear speedup is achievable

Amdahl’s Law

• Most fundamental limitation on parallel speedup

• If fraction s of seq execution is inherently serial, speedup <= 1/s

• Example: 2-phase calculation– sweep over n-by-n grid and do some independent computation– sweep again and add each value to global sum

• Time for first phase = n2/p

• Second phase serialized at global variable, so time = n2

• Speedup <= or at most 2

• Trick: divide second phase into two– accumulate into private sum during sweep– add per-process private sum into global sum

• Parallel time is n2/p + n2/p + p, and speedup at best

p + n2

2n2 + p2

Amdahl’s Law

Timen2/p n2/p

Concurrency Profiles

– Area under curve is total work done, or time with 1 processor– Horizontal extent is lower bound on time (infinite processors)

– Speedup is the ratio: , base case:

– Amdahl’s law applies to any overhead, not just limited concurrency

Concurrency

Clock cycle number

s + 1-sp

Communication Performance Metrics: Latency and Bandwidth

1. Bandwidth– Need high bandwidth in communication– Match limits in network, memory, and processor– Challenge is link speed of network interface vs. bisection

bandwidth of network

2. Latency– Affects performance, since processor may have to wait– Affects ease of programming, since requires more thought to

overlap communication and computation– Overhead to communicate is a problem in many machines

3. Latency Hiding– How can a mechanism help hide latency?– Increases programming system burdern– Examples: overlap message send with computation, prefetch

data, switch to other tasks

Networks

• Design Options:

• Topology

• Routing

• Direct vs. Indirect

• Physical implementation

• Evaluation Criteria:

• Latency

• Bisection Bandwidth

• Contention and hot-spot behavior

• Partitionability

• Cost and scalability

• Fault tolerance

• Simple and cost-effective for small-scale multiprocessors

• Not scalable (limited bandwidth; electrical complications)

Crossbars

• Each port has link to every other port

+ Low latency and high throughput

- Cost grows as O(N^2) so not very scalable.

- Difficult to arbitrate and to get all data lines into and out of a centralized crossbar.

• Used in small-scale MPs and as building block for other networks (e.g., Omega).

M M M M

Crossbar

• Cheap: Cost is O(N).

• Point-to-point wires and pipelining can be used to make them very fast.

+ High overall bandwidth

- High latency O(N)

• Examples: KSR machine, Hector

• Cheap: Cost is O(N).

• Latency is O(logN).

• Easy to layout as planar graphs (e.g., H-Trees).

• For random permutations, root can become bottleneck.

• To avoid root being bottleneck, notion of Fat-Trees (used in CM-5)

• channels are wider as you move towards root.

H-Tree

Fat Tree

Hypercubes

• Also called binary n-cubes. # of nodes = N = 2^n.

• Latency is O(logN); Out degree of PE is O(logN)

• Minimizes hops; good bisection BW; but tough to layout in 3-space

• Popular in early message-passing computers (e.g., intel iPSC, NCUBE)

• Used as direct network ==> emphasizes locality

0-D 1-D 2-D 3-D 4-D

Multistage Logarithmic Networks

• Cost is O(NlogN); latency is O(logN); throughput is O(N).

• Generally indirect networks.

• Many variations exist (Omega, Butterfly, Benes, ...).

• Used in many machines: BBN Butterfly, IBM RP3, ...

Omega Network

• All stages are same, so can use recirculating network.

• Single path from source to destination.

• Can add extra stages and pathways to minimize collisions and increase fault tolerance.

• Can support combining. Used in IBM RP3.

010011

110111

010011

110111

Omega Network

Butterfly Network

010011

110111

010011

110111

Butter fly Network

split on MSB

split on LSB

• Equivalent to Omega network. Easy to see routing of messages.

• Also very similar to hypercubes (direct vs. indirect though).

• Clearly see that bisection of network is (N / 2) channels.

• Can use higher-degree switches to reduce depth. Used in BBN machines.

k-ary n-cubes

• Generalization of hypercubes (k-nodes in a string)

• Total # of nodes = N = k^n.

• k > 2 reduces # of channels at bisection, thus allowing for wider channels but more hops.

4-ary 3-cube

Routing Strategies and Latency

• Store-and-Forward routing:

• Tsf = Tc • ( D • L / W)

• L = msg length, D = # of hops,

W = width, Tc = hop delay

• Wormhole routing:

• Twh = Tc • (D + L / W)

• # of hops is an additive rather than multiplicative factor

• Virtual Cut-Through routing:

• Older and similar to wormhole. When blockage occurs, however, message is removed from network and buffered.

• Deadlock are avoided through use of virtual channels and by using a routing strategy that does not allow channel-dependency cycles.

Advantages of Low-Dimensional Nets

• What can be built in VLSI is often wire-limited• LDNs are easier to layout:

– more uniform wiring density (easier to embed in 2-D or 3-D space)– mostly local connections (e.g., grids)

• Compared with HDNs (e.g., hypercubes), LDNs have:– shorter wires (reduces hop latency)– fewer wires (increases bandwidth given constant bisection width)

» increased channel width is the major reason why LDNs win!

• Factors that limit end-to-end latency:– LDNs: number of hops– HDNs: length of message going across very narrow channels

• LDNs have better hot-spot throughput– more pins per node than HDNs

CIS 429/529 2007 Parallel Arch. Intro

Cluster InterconnectTechnology (off-the-shelf)

Dolphin

• Implementation of SCI (scalable coherent interconnect)

• Torus topology– (Usually) no switches

» Dolphin has small (8-ported) switches

Dolphin

• PCI Card– $1300 - $2500 (USD)

• MPI performance:– Latency: 4 microseconds

– Bandwidth: 300 MB/s

• Supported Software: Linux and Solaris

• Supported CPUs: x86, SPARC, Alpha

Giganet

• cLAN– VI interface

– 1.25 Gb/s switched interconnect

• MPI Performance:– Latency: 12 microseconds

• Supported Software: Linux and Windows

• Supported CPUs: x86

Myrinet

• 2 Gb/s switched interconnect

• MPI performance:– Latency: 10 microseconds

• PCI Card– USD$1200 - $1800

Myrinet

• Switch– 128 ports in one chassis

– Cost: ~USD$50,000

• Supported Software: Linux, Windows, Solaris, Tru64, Irix, VxWorks

• Supported CPUs: x86, Itanium, Alpha, PowerPC, SPARC, O200

Quadrics

• Switched Interconnect

• Interconnect on the PSC Terascale machine

• MPI Performance:– Latency: 5 microseconds

• Supported Software: Tru64 and Linux

• Supported CPUs: Alpha, IA-32

Ethernet

• 1 Gb/s switched interconnect

• PCI Card– “Free” (integrated into motherboard), or

– USD$90

• MPI Performance– Latency: 70 microseconds

• High volume

• 128-port GbE switch» USD$160,000

• Hardware and software support: ubiquitous

Ethernet

• 10 GbE on the horizon– 10 GbE is equivalent to OC-192

Infiniband

• 2.5 Gb/s switched interconnect– Multiple link widths: 1x, 4x, 12x (250 MB/s, 1 GB/s, 3 GB/s)

• Engineered with many good networking ideas– E.g., OS bypass (registered memory), selectable transfer

units, remote DMA

• Management and form factors specified up front– Focus on interoperability

» Learning from Fibre Channel’s mistakes!

SummaryNetwork MPI Latency

(μsec)

(MB/s)

NIC Cost

($USD)

Switch Cost

$(USD)

CPU Support Software Support

Dolphin 4 300 1300-2500 NA X86, SPARC, Alpha

Linux, Solaris

GigaNet 12 107 ??? ??? X86 Linux, Windows

Myrinet 10 240 1200-1800 50,000 X86, IA-64, Alpha, PowerPC, SPARC,

Linux, Windows, Solaris,

Tru64, Irix, VxWorks

Quadrics 5 300 ??? ??? X86, Alpha Tru64, Linux

Ethernet 70 50 0-90 160,000 ubiq ubiq

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