Introduction

2

Introduction

What is Parallel Architecture?

Why Parallel Architecture?

Evolution and Convergence of Parallel Architectures

Fundamental Design Issues

3

What is Parallel Architecture?

A parallel computer is a collection of processing elementsthat cooperate to solve large problems fast

Some broad issues:• Resource Allocation:

– how large a collection?– how powerful are the elements?– how much memory?

• Data access, Communication and Synchronization– how do the elements cooperate and communicate?– how are data transmitted between processors?– what are the abstractions and primitives for cooperation?

• Performance and Scalability– how does it all translate into performance?– how does it scale?

4

Why Study Parallel Architecture?

Role of a computer architect:

To design and engineer the various levels of a computer systemto maximize performance and programmability within limits oftechnology and cost.

Parallelism:

• Provides alternative to faster clock for performance

• Applies at all levels of system design

• Is a fascinating perspective from which to view architecture

• Is increasingly central in information processing

5

Why Study it Today?

History: diverse and innovative organizational structures, oftentied to novel programming models

Rapidly maturing under strong technological constraints

• The “killer micro” is ubiquitous

• Laptops and supercomputers are fundamentally similar!

• Technological trends cause diverse approaches to converge

Technological trends make parallel computing inevitable• In the mainstream

Need to understand fundamental principles and design tradeoffs,not just taxonomies

• Naming, Ordering, Replication, Communication performance

6

Inevitability of Parallel ComputingApplication demands: Our insatiable need for computing cycles

• Scientific computing: CFD, Biology, Chemistry, Physics, ...• General-purpose computing: Video, Graphics, CAD, Databases, TP...

Technology Trends• Number of transistors on chip growing rapidly• Clock rates expected to go up only slowly

Architecture Trends• Instruction-level parallelism valuable but limited• Coarser-level parallelism, as in MPs, the most viable approach

Economics

Current trends:• Today’s microprocessors have multiprocessor support• Servers and workstations becoming MP: Sun, SGI, DEC, COMPAQ!...• Tomorrow’s microprocessors are multiprocessors

7

Application Trends

Demand for cycles fuels advances in hardware, and vice-versa• Cycle drives exponential increase in microprocessor performance

• Drives parallel architecture harder: most demanding applications

Range of performance demands• Need range of system performance with progressively increasing cost

• Platform pyramid

Goal of applications in using parallel machines: Speedup

Speedup (p processors) =

For a fixed problem size (input data set), performance = 1/time

Speedup fixed problem (p processors) =

Performance (p processors)

Performance (1 processor)

Time (1 processor)

Time (p processors)

8

Scientific Computing Demand

9

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.

10

Applications: Speech and Image Processing

1980 1985 1990 1995

1 MIPS

10 MIPS

100 MIPS

1 GIPS

Sub-BandSpeech Coding

200 WordsIsolated SpeechRecognition

SpeakerVeri¼cation

CELPSpeech Coding

ISDN-CD StereoReceiver

5,000 WordsContinuousSpeechRecognition

HDTV Receiver

CIF Video

1,000 WordsContinuousSpeechRecognitionTelephone

NumberRecognition

10 GIPS

• Also CAD, Databases, . . .

• 100 processors gets you 10 years, 1000 gets you 20 !

11

Learning Curve for Parallel Applications

• AMBER molecular dynamics simulation program• Starting point was vector code for Cray-1• 145 MFLOP on Cray90, 406 for final version on 128-processor Paragon,

891 on 128-processor Cray T3D

12

Commercial Computing

Also relies on parallelism for high end• Scale not so large, but use much more wide-spread

• Computational power determines scale of business that can be handled

Databases, online-transaction processing, decision support, datamining, data warehousing ...

TPC benchmarks (TPC-C order entry, TPC-D decision support)• Explicit scaling criteria provided

• Size of enterprise scales with size of system

• Problem size no longer fixed as p increases, so throughput is used as aperformance measure (transactions per minute or tpm)

13

TPC-C Results for March 1996

• Parallelism is pervasive• Small to moderate scale parallelism very important• Difficult to obtain snapshot to compare across vendor platforms

Thr

ough

put (

tpm

C)

Number of processors

◆

◆

◆

◆

◆

◆

◆◆◆◆◆◆ ◆

◆◆◆◆◆◆

◆◆

◆

◆

◆

◆◆

◆

◆◆

◆

◆

◆

◆

◆

✖

■■■

■■■

■

■

■■

■

●●

●●

●

●●

❍❍❍❍●

✽

✖

✖

✖

✖

▲

▲

▲

▲

▲

0

5,000

10,000

15,000

20,000

25,000

0 20 40 60 80 100 120

▲ Tandem Himalaya✖ DEC Alpha✽ SGI PowerChallenge● HP PA■ IBM PowerPC◆ Other

14

Summary of Application Trends

Transition to parallel computing has occurred for scientific andengineering 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 likeparallel programs

Demand for improving throughput on sequential workloads• Greatest use of small-scale multiprocessors

Solid application demand exists and will increase

15

Technology Trends

The natural building block for multiprocessors is now also about the fastest!

Per

form

ance

0.1

1

10

100

1965 1970 1975 1980 1985 1990 1995

Supercomputers

Minicomputers

Mainframes

Microprocessors

16

General Technology Trends

• Microprocessor performance increases 50% - 100% per year• Transistor count doubles every 3 years• DRAM size quadruples every 3 years• Huge investment per generation is carried by huge commodity market

• Not that single-processor performance is plateauing, but thatparallelism is a natural way to improve it.

0

20

40

60

80

100

120

140

160

180

1987 1988 1989 1990 1991 1992

Integer FP

Sun 4260

MIPSM/120

IBMRS6000

540MIPS

M2000

HP 9000750

DECalpha

17

Technology: A Closer Look

Basic advance is decreasing feature size ( λ )• Circuits become either faster or lower in power

Die size is growing too• Clock rate improves roughly proportional to improvement in λ• Number of transistors improves like λ2 (or faster)

Performance > 100x per decade; clock rate 10x, rest transistor count

How to use more transistors?• Parallelism in processing

– multiple operations per cycle reduces CPI

• Locality in data access– avoids latency and reduces CPI– also improves processor utilization

• Both need resources, so tradeoff

Fundamental issue is resource distribution, as in uniprocessors

Proc $

Interconnect

18

Clock Frequency Growth Rate

• 30% per year

◆◆◆◆◆◆◆

◆◆◆

◆◆

◆◆◆

◆

◆◆◆

◆

◆◆

◆◆

◆

◆

◆◆◆

◆◆

◆

◆

◆

◆◆

◆◆◆

◆

◆◆

◆

◆◆◆◆

◆

◆◆◆

◆◆

◆◆

◆◆

◆◆◆◆

◆◆◆

◆◆◆

◆◆◆◆◆

◆

◆◆◆

◆◆

0.1

1

10

100

1,000

19701975

19801985

19901995

20002005

Clo

ck r

ate

(MH

z)

i4004i8008

i8080

i8086 i80286i80386

Pentium100

R10000

19

Transistor Count Growth Rate

• 100 million transistors on chip by early 2000’s A.D.• Transistor count grows much faster than clock rate

- 40% per year, order of magnitude more contribution in 2 decades

Tran

sist

ors

◆◆◆◆◆◆◆◆◆

◆

◆◆

◆◆◆◆◆

◆

◆◆

◆

◆◆

◆◆◆◆

◆◆

◆

◆

◆ ◆

◆◆

◆

◆

◆◆◆

◆◆◆◆◆

◆◆◆◆

◆◆◆◆◆

◆

◆◆◆◆

◆◆◆

◆◆◆◆◆

1,000

10,000

100,000

1,000,000

10,000,000

100,000,000

19701975

19801985

19901995

20002005

i4004i8008

i8080

i8086

i80286i80386

R2000

Pentium R10000

R3000

20

Similar Story for StorageDivergence between memory capacity and speed more pronounced

• Capacity increased by 1000x from 1980-95, speed only 2x

• Gigabit DRAM by c. 2000, but gap with processor speed much greater

Larger memories are slower, while processors get faster• Need to transfer more data in parallel• Need deeper cache hierarchies• How to organize caches?

Parallelism increases effective size of each level of hierarchy,without increasing access time

Parallelism and locality within memory systems too• New designs fetch many bits within memory chip; follow with fast

pipelined transfer across narrower interface• Buffer caches most recently accessed data

Disks too: Parallel disks plus caching

21

Architectural Trends

Architecture translates technology’s gifts to performance andcapability

Resolves the tradeoff between parallelism and locality• Current microprocessor: 1/3 compute, 1/3 cache, 1/3 off-chip connect

• Tradeoffs may change with scale and technology advances

Understanding microprocessor architectural trends• Helps build intuition about design issues or parallel machines

• Shows fundamental role of parallelism even in “sequential” computers

Four generations of architectural history: tube, transistor, IC, VLSI• Here focus only on VLSI generation

Greatest delineation in VLSI has been in type of parallelism exploited

22

Architectural Trends

Greatest trend in VLSI generation is increase in parallelism• Up to 1985: bit level parallelism: 4-bit -> 8 bit -> 16-bit

– slows after 32 bit

– adoption of 64-bit now under way, 128-bit far (not performance issue)

– great inflection point when 32-bit micro and cache fit on a chip

• Mid 80s to mid 90s: instruction level parallelism– pipelining and simple instruction sets, + compiler advances (RISC)

– on-chip caches and functional units => superscalar execution

– greater sophistication: out of order execution, speculation, prediction• to deal with control transfer and latency problems

• Next step: thread level parallelism

23

Phases in VLSI Generation

• How good is instruction-level parallelism?• Thread-level needed in microprocessors?

Tran

sist

ors

◆◆◆◆◆◆◆

◆◆

◆

◆◆

◆

◆

◆ ◆◆

◆

◆

◆

◆

◆◆

◆◆◆ ◆

◆◆

◆

◆

◆ ◆

◆

◆

◆

◆

◆

◆◆

◆ ◆

◆◆◆

◆◆◆ ◆

◆◆ ◆◆ ◆

◆

◆◆◆ ◆

◆◆◆

◆

◆ ◆◆◆

1,000

10,000

100,000

1,000,000

10,000,000

100,000,000

1970 1975 1980 1985 1990 1995 2000 2005

Bit-level parallelism Instruction-level Thread-level (?)

i4004

i8008i8080

i8086

i80286

i80386

R2000

Pentium

R10000

R3000

24

Architectural Trends: ILP• Reported speedups for superscalar processors

• Horst, Harris, and Jardine [1990] ...................... 1.37

• Wang and Wu [1988] .......................................... 1.70

• Smith, Johnson, and Horowitz [1989] .............. 2.30

• Murakami et al. [1989] ........................................ 2.55

• Chang et al. [1991] ............................................. 2.90

• Jouppi and Wall [1989] ...................................... 3.20

• Lee, Kwok, and Briggs [1991] ........................... 3.50

• Wall [1991] .......................................................... 5

• Melvin and Patt [1991] ....................................... 8

• Butler et al. [1991] ............................................. 17+

• Large variance due to difference in– application domain investigated (numerical versus non-numerical)– capabilities of processor modeled

25

ILP Ideal Potential

• Infinite resources and fetch bandwidth, perfect branch prediction and renaming – real caches and non-zero miss latencies

0 1 2 3 4 5 6+0

5

10

15

20

25

30

●

●

●● ●

0 5 10 150

0.5

1

1.5

2

2.5

3

Fra

ctio

n of

tota

l cyc

les

(%)

Number of instructions issued

Spe

edup

Instructions issued per cycle

26

Results of ILP Studies

1x

2x

3x

4x

Jouppi_89 Smith_89 Murakami_89 Chang_91 Butler_91 Melvin_91

1 branch unit/real prediction

perfect branch prediction

• Concentrate on parallelism for 4-issue machines

• Realistic studies show only 2-fold speedup• Recent studies show that more ILP needs to look across threads

27

Architectural Trends: Bus-based MPs

No. of processors in fully configured commercial shared-memory systems

•Micro on a chip makes it natural to connect many to shared memory– dominates server and enterprise market, moving down to desktop

•Faster processors began to saturate bus, then bus technology advanced– today, range of sizes for bus-based systems, desktop to large servers

●

●

●

●

●

●

●

●

●●

● ●

● ●

● ●

●

●

●

●●

●

●

●

●

●

●

0

10

20

30

40

CRAY CS6400

SGI Challenge

Sequent B2100

Sequent B8000

Symmetry81

Symmetry21

Power

SS690MP 140 SS690MP 120

AS8400

HP K400AS2100SS20

SE30

SS1000E

SS10

SE10

SS1000

P-ProSGI PowerSeries

SE60

SE70

Sun E6000

SC2000ESun SC2000SGI PowerChallenge/XL

SunıE10000

50

60

70

1984 1986 1988 1990 1992 1994 1996 1998

Num

ber

of p

roce

ssor

s

28

Bus BandwidthS

hare

d bu

s ba

ndw

idth

(M

B/s

)

● ●

●●

●● ●●●● ●●●●

●●

●

●

●

●

●

●

● ●

●

●

●

10

100

1,000

10,000

100,000

1984 1986 1988 1990 1992 1994 1996 1998

SequentıB8000

SGIıPowerChı

XL

Sequent B2100

Symmetry81/21

SS690MP 120ıSS690MP 140

SS10/ıSE10/ıSE60

SE70/SE30SS1000 SS20

SS1000EAS2100SC2000EHPK400

SGI Challenge

Sun E6000AS8400

P-Pro

Sun E10000

SGI PowerSeries

SC2000

Power

CS6400

29

Economics

Commodity microprocessors not only fast but CHEAP• Development cost is tens of millions of dollars (5-100 typical)

• BUT, many more are sold compared to supercomputers

• Crucial to take advantage of the investment, and use thecommodity building block

• Exotic parallel architectures no more than special-purpose

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

Standardization by Intel makes small, bus-based SMPs commodity

Desktop: few smaller processors versus one larger one?• Multiprocessor on a chip

30

Consider 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)

• Plus economics

Large-scale multiprocessors replace vector supercomputers

• Well under way already

31

Raw Uniprocessor Performance: LINPACK

LIN

PA

CK

(M

FLO

PS

)

▲

▲

▲

▲

▲

▲

◆

◆

◆

◆◆

◆

◆◆

◆

◆ ◆

1

10

100

1,000

10,000

1975 1980 1985 1990 1995 2000

▲ CRAY ¸ n = 100■ CRAY ¸ n = 1,000

◆ Micro¸ n = 100● Micro¸ n = 1,000

CRAY 1s

Xmp/14se

Xmp/416Ymp

C90

T94

DEC 8200

IBM Power2/990MIPS R4400

HP9000/735DEC Alpha

DEC Alpha AXPHP 9000/750

IBM RS6000/540

MIPS M/2000

MIPS M/120

Sun 4/260

■

■■

■

■

■

●

●

●

● ●

●●●

●●

●

32

Raw Parallel Performance: LINPACK

• Even vector Crays became parallel: X-MP (2-4) Y-MP (8), C-90 (16), T94 (32)• Since 1993, Cray produces MPPs too (T3D, T3E)

LIN

PA

CK

(G

FLO

PS

)

■ CRAY peak● MPP peak

Xmp /416(4)

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

CM-2CM-200

Delta

Paragon XP/S

C90(16)

CM-5

ASCI Red

T932(32)

T3D

Paragon XP/S MPı(1024)

Paragon XP/S MPı(6768)

■

■

■

■

●

●

■●

●

●

●●

●

●●

0.1

1

10

100

1,000

10,000

1985 1987 1989 1991 1993 1995 1996

33

500 Fastest Computers

Num

ber

of s

yste

ms

◆

◆

◆

◆■

■

■ ■

▲

▲

▲

▲

11/93 11/94 11/95 11/960

50

100

150

200

250

300

350

■ PVP◆ MPP

▲ SMP

319

106

284

239

63

187

313

198

110

10673

34

Summary: Why Parallel Architecture?

Increasingly attractive• Economics, technology, architecture, application demand

Increasingly central and mainstream

Parallelism exploited at many levels• Instruction-level parallelism• Multiprocessor servers• Large-scale multiprocessors (“MPPs”)

Focus of this class: multiprocessor level of parallelism

Same story from memory system perspective• Increase bandwidth, reduce average latency with many local memories

Wide range of parallel architectures make sense

• Different cost, performance and scalability

Convergence of Parallel Architectures

36

History

Application Software

System Software SIMD

Message Passing

Shared MemoryDataflow

SystolicArrays Architecture

• Uncertainty of direction paralyzed parallel software development!

Historically, parallel architectures tied to programming models

• Divergent architectures, with no predictable pattern of growth.

37

Today

Extension of “computer architecture” to support communicationand cooperation

• OLD: Instruction Set Architecture

• NEW: Communication Architecture

Defines• Critical abstractions, boundaries, and primitives (interfaces)

• Organizational structures that implement interfaces (hw or sw)

Compilers, libraries and OS are important bridges today

38

Modern Layered Framework

CAD

Multiprogramming Sharedaddress

Messagepassing

Dataparallel

Database Scientific modeling Parallel applications

Programming models

Communication abstractionUser/system boundary

Compilationor library

Operating systems support

Communication hardware

Physical communication medium

Hardware/software boundary

39

Programming Model

What programmer uses in coding applications

Specifies communication and synchronization

Examples:• Multiprogramming: no communication or synch. at program level

• Shared address space: like bulletin board

• Message passing: like letters or phone calls, explicit point to point

• Data parallel: more regimented, global actions on data– Implemented with shared address space or message passing

40

Communication Abstraction

User level communication primitives provided• Realizes the programming model

• Mapping exists between language primitives of programming modeland these primitives

Supported directly by hw, or via OS, or via user sw

Lot of debate about what to support in sw and gap between layers

Today:• Hw/sw interface tends to be flat, i.e. complexity roughly uniform

• Compilers and software play important roles as bridges today

• Technology trends exert strong influence

Result is convergence in organizational structure• Relatively simple, general purpose communication primitives

41

Communication Architecture= User/System Interface + Implementation

User/System Interface:• Comm. primitives exposed to user-level by hw and system-level sw

Implementation:• Organizational structures that implement the primitives: hw or OS• How optimized are they? How integrated into processing node?• Structure of network

Goals:• Performance• Broad applicability• Programmability• Scalability• Low Cost

42

Evolution of Architectural Models

Historically machines tailored to programming models• Prog. model, comm. abstraction, and machine organization lumped

together as the “architecture”

Evolution helps understand convergence• Identify core concepts

• Shared Address Space

• Message Passing

• Data Parallel

Others:• Dataflow

• Systolic Arrays

Examine programming model, motivation, intended applications, andcontributions to convergence

43

Shared Address Space Architectures

Any processor can directly reference any memory location• Communication occurs implicitly as result of loads and stores

Convenient:• Location transparency

• Similar programming model to time-sharing on uniprocessors– Except processes run on different processors– Good throughput on multiprogrammed workloads

Naturally provided on wide range of platforms• History dates at least to precursors of mainframes in early 60s

• Wide range of scale: few to hundreds of processors

Popularly known as shared memory machines or model• Ambiguous: memory may be physically distributed among processors

44

Shared Address Space ModelProcess: virtual address space plus one or more threads of control

Portions of address spaces of processes are shared

•Writes to shared address visible to other threads (in other processestoo)•Natural extension of uniprocessors model: conventional memoryoperations for comm.; special atomic operations for synchronization•OS uses shared memory to coordinate processes

St or e

P1P2

Pn

P0

Load

P0 pr i vat e

P1 pr i vat e

P2 pr i vat e

Pn pr i vat e

Virtual address spaces for acollection of processes communicatingvia shared addresses

Machine physical address space

Shared portionof address space

Private portionof address space

Common physicaladdresses

45

Communication Hardware

Also natural extension of uniprocessor

Already have processor, one or more memory modules and I/Ocontrollers connected by hardware interconnect of some sort

Memory capacity increased by adding modules, I/O by controllers•Add processors for processing! •For higher-throughput multiprogramming, or parallel programs

I/O ctrlMem Mem Mem

Interconnect

Mem I/O ctrl

Processor Processor

Interconnect

I/Odevices

46

History

P

P

C

C

I/O

I/O

M MM M

PP

C

I/O

M MC

I/O

$ $

“Mainframe” approach• Motivated by multiprogramming• Extends crossbar used for mem bw and I/O• Originally processor cost limited to small

– later, cost of crossbar• Bandwidth scales with p• High incremental cost; use multistage instead

“Minicomputer” approach• Almost all microprocessor systems have bus• Motivated by multiprogramming, TP• Used heavily for parallel computing• Called symmetric multiprocessor (SMP)• Latency larger than for uniprocessor• Bus is bandwidth bottleneck

– caching is key: coherence problem

• Low incremental cost

47

Example: Intel Pentium Pro Quad

• All coherence andmultiprocessing glue inprocessor module

• Highly integrated, targeted athigh volume

• Low latency and bandwidth

P-Pro bus (64-bit data, 36-bit address, 66 MHz)

CPU

Bus interface

MIU

P-Promodule

P-Promodule

P-Promodule256-KB

L2 $Interruptcontroller

PCIbridge

PCIbridge

Memorycontroller

1-, 2-, or 4-wayinterleaved

DRAM

PC

I bus

PC

I busPCI

I/Ocards

48

Example: SUN Enterprise

• 16 cards of either type: processors + memory, or I/O• All memory accessed over bus, so symmetric• Higher bandwidth, higher latency bus

Gigaplane bus (256 data, 41 address, 83 MHz)

SB

US

SB

US

SB

US

2 F

iber

Cha

nnel

100b

T, S

CS

I

Bus interface

CPU/memcardsP

$2

$

P

$2

$

Mem ctrl

Bus interface/switch

I/O cards

49

Scaling Up

• Problem is interconnect: cost (crossbar) or bandwidth (bus)

• Dance-hall: bandwidth still scalable, but lower cost than crossbar– latencies to memory uniform, but uniformly large

• Distributed memory or non-uniform memory access (NUMA)– Construct shared address space out of simple message transactions

across a general-purpose network (e.g. read-request, read-response)

• Caching shared (particularly nonlocal) data?

M M M° ° °

° ° ° M ° ° °M M

NetworkNetwork

P

$

P

$

P

$

P

$

P

$

P

$

“Dance hall” Distributed memory

50

Example: Cray T3E

• Scale up to 1024 processors, 480MB/s links• Memory controller generates comm. request for nonlocal references• No hardware mechanism for coherence (SGI Origin etc. provide this)

Switch

P

$

XY

Z

External I/O

Memctrl

and NI

Mem

51

Message Passing Architectures

Complete computer as building block, including I/O• Communication via explicit I/O operations

Programming model: directly access only private address space(local memory), comm. via explicit messages (send/receive)

High-level block diagram similar to distributed-memory SAS• But comm. integrated at IO level, needn’t be into memory system• Like networks of workstations (clusters), but tighter integration• Easier to build than scalable SAS

Programming model more removed from basic hardware operations• Library or OS intervention

52

Message-Passing Abstraction

• Send specifies buffer to be transmitted and receiving process• Recv specifies sending process and application storage to receive into• Memory to memory copy, but need to name processes• Optional tag on send and matching rule on receive• User process names local data and entities in process/tag space too• In simplest form, the send/recv match achieves pairwise synch event

– Other variants too• Many overheads: copying, buffer management, protection

Process P Process Q

Address Y

Address X

Send X, Q, t

Receive Y, P, tMatch

Local processaddress space

Local processaddress space

53

Evolution of Message-Passing Machines

Early machines: FIFO on each link• Hw close to prog. Model; synchronous ops• Replaced by DMA, enabling non-blocking ops

– Buffered by system at destination until recv

Diminishing role of topology• Store&forward routing: topology important• Introduction of pipelined routing made it less so• Cost is in node-network interface• Simplifies programming

000001

010011

100

110

101

111

54

Example: IBM SP-2

• Made out of essentially complete RS6000 workstations• Network interface integrated in I/O bus (bw limited by I/Obus)

Memory bus

MicroChannel bus

I/O

i860 NI

DMA

DR

AM

IBM SP-2 node

L2 $

Power 2CPU

Memorycontroller

4-wayinterleaved

DRAM

General interconnectionnetwork formed from8-port switches

NIC

55

Example Intel Paragon

Memory bus (64-bit, 50 MHz)

i860

L1 $

NI

DMA

i860

L1 $

Driver

Memctrl

4-wayinterleaved

DRAM

IntelParagonnode

8 bits,175 MHz,bidirectional2D grid network

with processing nodeattached to every switch

Sandia’ s Intel Paragon XP/S-based Supercomputer

56

Toward Architectural Convergence

Evolution and role of software have blurred boundary• Send/recv supported on SAS machines via buffers

• Can construct global address space on MP using hashing

• Page-based (or finer-grained) shared virtual memory

Hardware organization converging too• Tighter NI integration even for MP (low-latency, high-bandwidth)

• At lower level, even hardware SAS passes hardware messages

Even clusters of workstations/SMPs are parallel systems• Emergence of fast system area networks (SAN)

Programming models distinct, but organizations converging• Nodes connected by general network and communication assists

• Implementations also converging, at least in high-end machines

57

Data Parallel SystemsProgramming model

• Operations performed in parallel on each element of data structure

• Logically single thread of control, performs sequential or parallel steps

• Conceptually, a processor associated with each data element

Architectural model• Array of many simple, cheap processors with little memory each

– Processors don’t sequence through instructions

• Attached to a control processor that issues instructions

• Specialized and general communication, cheap global synchronization

PE PE PE° ° °

PE PE PE° ° °

PE PE PE° ° °

° ° ° ° ° ° ° ° °

ControlprocessorOriginal motivations

•Matches simple differential equation solvers•Centralize high cost of instructionfetch/sequencing

58

Application of Data Parallelism• Each PE contains an employee record with his/her salary

If salary > 100K then

salary = salary *1.05

else

salary = salary *1.10

• Logically, the whole operation is a single step

• Some processors enabled for arithmetic operation, others disabled

Other examples:• Finite differences, linear algebra, ...• Document searching, graphics, image processing, ...

Some recent machines:• Thinking Machines CM-1, CM-2 (and CM-5)• Maspar MP-1 and MP-2,

59

Evolution and Convergence

Rigid control structure (SIMD in Flynn taxonomy)• SISD = uniprocessor, MIMD = multiprocessor

Popular when cost savings of centralized sequencer high• 60s when CPU was a cabinet• Replaced by vectors in mid-70s

– More flexible w.r.t. memory layout and easier to manage• Revived in mid-80s when 32-bit datapath slices just fit on chip• No longer true with modern microprocessors

Other reasons for demise• Simple, regular applications have good locality, can do well anyway• Loss of applicability due to hardwiring data parallelism

– MIMD machines as effective for data parallelism and more general

Prog. model converges with SPMD (single program multiple data)• Contributes need for fast global synchronization• Structured global address space, implemented with either SAS or MP

60

Dataflow Architectures

Represent computation as a graph of essential dependences• Logical processor at each node, activated by availability of operands• Message (tokens) carrying tag of next instruction sent to next processor• Tag compared with others in matching store; match fires execution

1 b

a

+ − ×

×

×

c e

d

f

Dataflow graph

f = a × d

Network

Tokenıstore

WaitingıMatching

Instructionıfetch

Execute

Token queue

Formıtoken

Network

Network

Programıstore

a = (b +1) × (b − c) ııd = c × e ıı

61

Evolution and ConvergenceKey characteristics

• Ability to name operations, synchronization, dynamic scheduling

Problems• Operations have locality across them, useful to group together• Handling complex data structures like arrays• Complexity of matching store and memory units• Expose too much parallelism (?)

Converged to use conventional processors and memory• Support for large, dynamic set of threads to map to processors• Typically shared address space as well• But separation of progr. model from hardware (like data-parallel)

Lasting contributions:• Integration of communication with thread (handler) generation• Tightly integrated communication and fine-grained synchronization• Remained useful concept for software (compilers etc.)

62

Systolic Architectures

• Replace single processor with array of regular processing elements

• Orchestrate data flow for high throughput with less memory access

Different from pipelining• Nonlinear array structure, multidirection data flow, each PE may

have (small) local instruction and data memory

Different from SIMD: each PE may do something different

Initial motivation: VLSI enables inexpensive special-purpose chips

Represent algorithms directly by chips connected in regular pattern

M

PE

M

PE PE PE

63

Systolic Arrays (contd.)

• Practical realizations (e.g. iWARP) use quite general processors– Enable variety of algorithms on same hardware

• But dedicated interconnect channels– Data transfer directly from register to register across channel

• Specialized, and same problems as SIMD– General purpose systems work well for same algorithms (locality etc.)

y(i) = w1 × x(i) + w2 × x(i + 1) + w3 × x(i + 2) + w4 × x(i + 3)

x8

y3 y2 y1

x7x6

x5x4

x3

w4

x2

x

w

x1

w3 w2 w1

xin

yin

xout

yout

xout = x

yout = yin + w × xinx = xin

Example: Systolic array for 1-D convolution

64

Convergence: Generic Parallel Architecture

A generic modern multiprocessor

Node: processor(s), memory system, plus communication assist• Network interface and communication controller

• Scalable network

• Convergence allows lots of innovation, now within framework• Integration of assist with node, what operations, how efficiently...

Mem

° ° °

Network

P

$

Communicationassist (CA)

Fundamental Design Issues

66

Understanding Parallel Architecture

Traditional taxonomies not very useful

Programming models not enough, nor hardware structures• Same one can be supported by radically different architectures

Architectural distinctions that affect software

• Compilers, libraries, programs

Design of user/system and hardware/software interface• Constrained from above by progr. models and below by technology

Guiding principles provided by layers• What primitives are provided at communication abstraction

• How programming models map to these

• How they are mapped to hardware

67

Fundamental Design Issues

At any layer, interface (contract) aspect and performance aspects

• Naming: How are logically shared data and/or processes referenced?

• Operations: What operations are provided on these data

• Ordering: How are accesses to data ordered and coordinated?

• Replication: How are data replicated to reduce communication?

• Communication Cost: Latency, bandwidth, overhead, occupancy

Understand at programming model first, since that sets requirements

Other issues• Node Granularity: How to split between processors and memory?

• ...

68

Sequential Programming Model

Contract• Naming: Can name any variable in virtual address space

– Hardware (and perhaps compilers) does translation to physical addresses

• Operations: Loads and Stores

• Ordering: Sequential program order

Performance• Rely on dependences on single location (mostly): dependence order

• Compilers and hardware violate other orders without getting caught

• Compiler: reordering and register allocation

• Hardware: out of order, pipeline bypassing, write buffers

• Transparent replication in caches

69

SAS Programming Model

Naming: Any process can name any variable in shared space

Operations: loads and stores, plus those needed for ordering

Simplest Ordering Model:• Within a process/thread: sequential program order

• Across threads: some interleaving (as in time-sharing)

• Additional orders through synchronization

• Again, compilers/hardware can violate orders without getting caught– Different, more subtle ordering models also possible (discussed later)

70

Synchronization

Mutual exclusion (locks)• Ensure certain operations on certain data can be performed by

only one process at a time

• Room that only one person can enter at a time

• No ordering guarantees

Event synchronization• Ordering of events to preserve dependences

– e.g. producer —> consumer of data

• 3 main types:– point-to-point– global– group

71

Message Passing Programming Model

Naming: Processes can name private data directly.• No shared address space

Operations: Explicit communication through send and receive• Send transfers data from private address space to another process• Receive copies data from process to private address space• Must be able to name processes

Ordering:• Program order within a process• Send and receive can provide pt to pt synch between processes• Mutual exclusion inherent

Can construct global address space:• Process number + address within process address space• But no direct operations on these names

72

Design Issues Apply at All Layers

Prog. model’s position provides constraints/goals for system

In fact, each interface between layers supports or takes a position on:• Naming model• Set of operations on names• Ordering model• Replication• Communication performance

Any set of positions can be mapped to any other by software

Let’s see issues across layers• How lower layers can support contracts of programming models• Performance issues

73

Naming and Operations

Naming and operations in programming model can be directlysupported by lower levels, or translated by compiler, libraries or OS

Example: Shared virtual address space in programming model

Hardware interface supportsshared physical address space

• Direct support by hardware through v-to-p mappings, no software layers

Hardware supports independent physical address spaces• Can provide SAS through OS, so in system/user interface

– v-to-p mappings only for data that are local– remote data accesses incur page faults; brought in via page fault handlers– same programming model, different hardware requirements and cost model

• Or through compilers or runtime, so above sys/user interface– shared objects, instrumentation of shared accesses, compiler support

74

Naming and Operations (contd)

Example: Implementing Message Passing

Direct support at hardware interface• But match and buffering benefit from more flexibility

Support at sys/user interface or above in software (almost always)• Hardware interface provides basic data transport (well suited)

• Send/receive built in sw for flexibility (protection, buffering)

• Choices at user/system interface:– OS each time: expensive– OS sets up once/infrequently, then little sw involvement each time

• Or lower interfaces provide SAS, and send/receive built on topwith buffers and loads/stores

Need to examine the issues and tradeoffs at every layer• Frequencies and types of operations, costs

75

Ordering

Message passing: no assumptions on orders across processes exceptthose imposed by send/receive pairs

SAS: How processes see the order of other processes’ referencesdefines semantics of SAS

• Ordering very important and subtle

• Uniprocessors play tricks with orders to gain parallelism or locality

• These are more important in multiprocessors

• Need to understand which old tricks are valid, and learn new ones

• How programs behave, what they rely on, and hardware implications

76

Replication

Very important for reducing data transfer/communication

Again, depends on naming model

Uniprocessor: caches do it automatically• Reduce communication with memory

Message Passing naming model at an interface• A receive replicates, giving a new name; subsequently use new name

• Replication is explicit in software above that interface

SAS naming model at an interface• A load brings in data transparently, so can replicate transparently

• Hardware caches do this, e.g. in shared physical address space

• OS can do it at page level in shared virtual address space, or objects

• No explicit renaming, many copies for same name: coherence problem– in uniprocessors, “coherence” of copies is natural in memory hierarchy

77

Communication Performance

Performance characteristics determine usage of operations at a layer• Programmer, compilers etc make choices based on this

Fundamentally, three characteristics:• Latency: time taken for an operation

• Bandwidth: rate of performing operations

• Cost: impact on execution time of program

If processor does one thing at a time: bandwidth ∝ 1/latency• But actually more complex in modern systems

Characteristics apply to overall operations, as well as individualcomponents of a system, however small

We’ll focus on communication or data transfer across nodes

78

Simple Example

Component performs an operation in 100ns

Simple bandwidth: 10 Mops

Internally pipeline depth 10 => bandwidth 100 Mops• Rate determined by slowest stage of pipeline, not overall latency

Delivered bandwidth on application depends on initiation frequency

Suppose application performs 100 M operations. What is cost?• op count * op latency gives 10 sec (upper bound)

• op count / peak op rate gives 1 sec (lower bound)– assumes full overlap of latency with useful work, so just issue cost

• if application can do 50 ns of useful work before depending on result ofop, cost to application is the other 50ns of latency

79

Linear Model of Data Transfer Latency

Transfer time (n) = T0 + n/B

• useful for message passing, memory access, vector ops etc

As n increases, bandwidth approaches asymptotic rate B

How quickly it approaches depends on T0Size needed for half bandwidth (half-power point):

n1/2 = T0 / B

But linear model not enough

• When can next transfer be initiated? Can cost be overlapped?

• Need to know how transfer is performed

80

Communication Cost Model

Comm Time per message= Overhead + Assist Occupancy +Network Delay + Size/Bandwidth + Contention

= ov + oc + l + n/B + Tc

Overhead and assist occupancy may be f(n) or not

Each component along the way has occupancy and delay

• Overall delay is sum of delays

• Overall occupancy (1/bandwidth) is biggest of occupancies

Comm Cost = frequency * (Comm time - overlap)

General model for data transfer: applies to cache misses too

81

Summary of Design Issues

Functional and performance issues apply at all layers

Functional: Naming, operations and ordering

Performance: Organization, latency, bandwidth, overhead, occupancy

Replication and communication are deeply related• Management depends on naming model

Goal of architects: design against frequency and type of operationsthat occur at communication abstraction, constrained by tradeoffsfrom above or below

• Hardware/software tradeoffs

82

RecapParallel architecture is important thread in evolution of architecture

• At all levels

• Multiple processor level now in mainstream of computing

Exotic designs have contributed much, but given way to convergence• Push of technology, cost and application performance

• Basic processor-memory architecture is the same

• Key architectural issue is in communication architecture– How communication is integrated into memory and I/O system on node

Fundamental design issues• Functional: naming, operations, ordering

• Performance: organization, replication, performance characteristics

Design decisions driven by workload-driven evaluation• Integral part of the engineering focus

83

Outline for Rest of ClassUnderstanding parallel programs as workloads

– Much more variation, less consensus and greater impact than in sequential

• What they look like in major programming models (Ch. 2)

• Programming for performance: interactions with architecture (Ch. 3)

• Methodologies for workload-driven architectural evaluation (Ch. 4)

Cache-coherent multiprocessors with centralized shared memory• Basic logical design, tradeoffs, implications for software (Ch 5)

• Physical design, deeper logical design issues, case studies (Ch 6)

Scalable systems• Design for scalability and realizing programming models (Ch 7)

• Hardware cache coherence with distributed memory (Ch 8)

• Hardware-software tradeoffs for scalable coherent SAS (Ch 9)

84

Outline (contd.)

Interconnection networks (Ch 10)

Latency tolerance (Ch 11)

Future directions (Ch 12)

Overall: conceptual foundations and engineering issues across broadrange of scales of design, all of which are important