Computer Architecture
Fall 2008
Lecture 25: Intro. to Multiprocessors
Adapted from Mary Jane Irwin ( www.cse.psu.edu/~mji )
[Adapted from Computer Organization and Design, Patterson & Hennessy, © 2005]
Plus, slides from Chapter 18 Parallel Processing by Stallings
Lecture 25
Fall 2008
The Big Picture: Where are We Now?
Processor
Processor
Output
Control
Datapath
Output
Memory
Memory
Input
Input
Control
Datapath

Multiprocessor – multiple processors with a single shared
address space

Cluster – multiple computers (each with their own
address space) connected over a local area network
(LAN) functioning as a single system
Lecture 25
Fall 2008
Applications Needing “Supercomputing”

Energy (plasma physics (simulating fusion reactions),
geophysical (petroleum) exploration)

DoE stockpile stewardship (to ensure the safety and
reliability of the nation’s stockpile of nuclear weapons)

Earth and climate (climate and weather prediction,
earthquake, tsunami prediction and mitigation of risks)

Transportation (improving vehicles’ airflow dynamics, fuel
consumption, crashworthiness, noise reduction)

Bioinformatics and computational biology (genomics,
protein folding, designer drugs)

Societal health and safety (pollution reduction, disaster
planning, terrorist action detection)
http://www.nap.edu/books/0309095026/html/
Lecture 25
Fall 2008
Encountering Amdahl’s Law

Speedup due to enhancement E is
Exec time w/o E
Speedup w/ E = ---------------------Exec time w/ E

Suppose that enhancement E accelerates a fraction F
(F <1) of the task by a factor S (S>1) and the remainder
of the task is unaffected
ExTime w/ E = ExTime w/o E  ((1-F) + F/S)
Speedup w/ E = 1 / ((1-F) + F/S)
Lecture 25
Fall 2008
Examples: Amdahl’s Law
Speedup w/ E = 1 / ((1-F) + F/S)

Consider an enhancement which runs 20 times faster
but which is only usable 25% of the time.
Speedup w/ E = 1/(.75 + .25/20) = 1.31

What if its usable only 15% of the time?
Speedup w/ E = 1/(.85 + .15/20) = 1.17

Amdahl’s Law tells us that to achieve linear speedup
with 100 processors, none of the original computation
can be scalar!

To get a speedup of 99 from 100 processors, the
percentage of the original program that could be scalar
would have to be 0.01% or less
Lecture 25
Fall 2008
Supercomputer Style Migration (Top500)
http://www.top500.org/lists/2005/11/
Nov data
500
Clusters
Constellations
SIMDs
MPPs
SMPs
Uniproc's
400
300
Cluster – whole computers
interconnected using their
I/O bus
200
100

9
19 4
95
19
96
19
97
19
98
19
99
20
00
20
01
20
0
20 2
03
20
04
20
05
19
19
93
0
Constellation – a cluster
that uses an SMP
multiprocessor as the
building block
In the last 8 years uniprocessor and SIMDs disappeared
while Clusters and Constellations grew from 3% to 80%
Lecture 25
Fall 2008
Multiprocessor/Clusters Key Questions

Q1 – How do they share data?

Q2 – How do they coordinate?

Q3 – How scalable is the architecture? How many
processors can be supported?
Lecture 25
Fall 2008
Flynn’s Classification Scheme

SISD – single instruction, single data stream


SIMD – single instruction, multiple data streams


no such machine (although some people put vector machines in
this category)
MIMD – multiple instructions, multiple data streams


single control unit broadcasting operations to multiple datapaths
MISD – multiple instruction, single data


aka uniprocessor - what we have been talking about all semester
aka multiprocessors (SMPs, MPPs, clusters, NOWs)
Now obsolete except for . . .
Lecture 25
Fall 2008
Taxonomy of Parallel Processor Architectures
Lecture 25
Fall 2008
SIMD Processors
PE
PE
PE
PE
PE
PE
PE
PE
PE
PE
PE
PE
PE
PE
PE
PE
Control

Single control unit

Multiple datapaths (processing elements – PEs) running
in parallel

Q1 – PEs are interconnected (usually via a mesh or torus) and
exchange/share data as directed by the control unit

Q2 – Each PE performs the same operation on its own local data
Lecture 25
Fall 2008
Example SIMD Machines
Maker
Year
# PEs # b/
Max
PE
PE memory clock
(MB)
(MHz)
System
BW
(MB/s)
Illiac IV
UIUC
1972 64
64
1
13
2,560
DAP
ICL
1980 4,096
1
2
5
2,560
MPP
Goodyear 1982 16,384 1
2
10
20,480
CM-2
Thinking
Machines
1987 65,536 1
512
7
16,384
1989 16,384 4
1024
25
23,000
MP-1216 MasPar
Lecture 25
Fall 2008
Multiprocessor Basic Organizations

Processors connected by a single bus

Processors connected by a network
# of Proc
Communication Message passing 8 to 2048
model
Shared NUMA 8 to 256
address UMA
2 to 64
Physical
connection
Lecture 25
Network
8 to 256
Bus
2 to 36
Fall 2008
Shared Address (Shared Memory) Multi’s

Q1 – Single address space shared by all the processors

Q2 – Processors coordinate/communicate through shared
variables in memory (via loads and stores)


UMAs (uniform memory access) – aka SMP (symmetric
multiprocessors)


Use of shared data must be coordinated via synchronization
primitives (locks)
all accesses to main memory take the same amount of time no
matter which processor makes the request or which location is
requested
NUMAs (nonuniform memory access)

some main memory accesses are faster than others depending on
the processor making the request and which location is requested

can scale to larger sizes than UMAs so are potentially higher
performance
Lecture 25
Fall 2008
Message Passing Multiprocessors

Each processor has its own private address space

Q1 – Processors share data by explicitly sending and
receiving information (messages)

Q2 – Coordination is built into message passing
primitives (send and receive)
Lecture 25
Fall 2008
N/UMA Remote Memory Access Times (RMAT)
Year
Type
Max
Proc
64
Interconnection RMAT
Network
(ns)
Sun Starfire
1996 SMP
Cray 3TE
1996 NUMA 2048
2-way 3D torus
300
HP V
1998 SMP
8 x 8 crossbar
1000
SGI Origin 3000 1999 NUMA 512
Fat tree
500
Compaq
1999 SMP
AlphaServer GS
32
Switched bus
400
Sun V880
2002 SMP
8
Switched bus
240
HP Superdome
9000
2003 SMP
64
Switched bus
275
Fat tree
???
32
NASA Columbia 2004 NUMA 10240
Lecture 25
Address buses, 500
data switch
Fall 2008
Multiprocessor Basics

Q1 – How do they share data?

Q2 – How do they coordinate?

Q3 – How scalable is the architecture? How many
processors?
# of Proc
Communication Message passing 8 to 2048
model
Shared NUMA 8 to 256
address UMA
2 to 64
Physical
connection
Lecture 25
Network
8 to 256
Bus
2 to 36
Fall 2008
Single Bus (Shared Address UMA) Multi’s
Proc1
Proc2
Caches
Caches
Proc3
Proc4
Caches
Caches
Single Bus
Memory

I/O
Caches are used to reduce latency and to lower bus traffic

Write-back caches used to keep bus traffic at a minimum

Must provide hardware to ensure that caches and memory
are consistent (cache coherency)

Must provide a hardware mechanism to support process
synchronization
Lecture 25
Fall 2008
Multiprocessor Cache Coherency

Cache coherency protocols

Bus snooping – cache controllers monitor shared bus traffic with
duplicate address tag hardware (so they don’t interfere with
processor’s access to the cache)
Proc1
Snoop DCache
Proc2
Snoop DCache
ProcN
Snoop DCache
Single Bus
Memory
Lecture 25
I/O
Fall 2008
Bus Snooping Protocols

Multiple copies are not a problem when reading

Processor must have exclusive access to write a word


What happens if two processors try to write to the same shared
data word in the same clock cycle? The bus arbiter decides
which processor gets the bus first (and this will be the
processor with the first exclusive access). Then the second
processor will get exclusive access. Thus, bus arbitration
forces sequential behavior.

This sequential consistency is the most conservative of the
memory consistency models. With it, the result of any
execution is the same as if the accesses of each processor
were kept in order and the accesses among different
processors were interleaved.
All other processors sharing that data must be informed
of writes
Lecture 25
Fall 2008
Handling Writes
Ensuring that all other processors sharing data are
informed of writes can be handled two ways:
1.
2.
Write-update (write-broadcast) – writing processor
broadcasts new data over the bus, all copies are
updated

All writes go to the bus  higher bus traffic

Since new values appear in caches sooner, can reduce latency
Write-invalidate – writing processor issues invalidation
signal on bus, cache snoops check to see if they have a
copy of the data, if so they invalidate their cache block
containing the word (this allows multiple readers but
only one writer)

Lecture 25
Uses the bus only on the first write  lower bus traffic, so better
use of bus bandwidth
Fall 2008
A Write-Invalidate CC Protocol
read (hit or
miss)
read (miss)
receives invalidate
(write by another processor
to this block)
Shared
(clean)
write (miss)
send invalidate
write-back due to
read (miss) by
another processor to
this block
Invalid
Modified
(dirty)
read (hit) or write (hit)
Lecture 25
write-back caching
protocol in black
signals from the processor
coherence additions in red
signals from the bus
coherence additions in
blue
Fall 2008
Write-Invalidate CC Examples

I = invalid (many), S = shared (many), M = modified (only one)
3. snoop sees
read request
Proc 1 for
A & lets MM
supply A
A S
1. read miss for A
Proc 2
4. gets A from MM
& changes its state
A I to S
2. read request for A
Main Mem
A
3. snoop sees read 1. read miss for A
request Proc
for A,1writes- Proc 2
4. gets A from MM
back A to MM
& changes its state
5. change A
A M
A I to M
state to I
5. P2 sends invalidate for A
2. read request for A
Main Mem
A
Lecture 25
1. write miss for A
Proc 1
4. change A
A IS
state to
Proc 2
2. writes A &
changes its state
A I to M
3. P2 sends invalidate for A
Main Mem
A
1. write miss for A
Proc 1
4. change A
A IM
state to
Proc 2
2. writes A &
changes its state
A I to M
3. P2 sends invalidate for A
Main Mem
A
Fall 2008
Other Coherence Protocols

There are many variations on cache coherence protocols

Another write-invalidate protocol used in the Pentium 4
(and many other micro’s) is MESI with four states:

Modified – (same) only modified cache copy is up-to-date;
memory copy and all other cache copies are out-of-date

Exclusive – only one copy of the shared data is allowed to be
cached; memory has an up-to-date copy
- Since there is only one copy of the block, write hits don’t need to
send invalidate signal
Lecture 25

Shared – multiple copies of the shared data may be cached (i.e.,
data permitted to be cached with more than one processor);
memory has an up-to-date copy

Invalid – same
Fall 2008
MESI Cache Coherency Protocol
Processor
shared read
Invalid
Another
(not valid
processor
block)
has read/write
miss for this
block
Invalidate for this block
Processor
shared read
miss
Processor
write
miss
Modified
(dirty)
Processor
write
[Write back block]
Processor write
or read hit
Lecture 25
Shared
(clean)
Processor
shared
read
Processor
Processor
exclusive
read miss
exclusive
read
Exclusive
(clean)
Processor exclusive
read miss
Processor
exclusive
read
Fall 2008
Process Synchronization

Need to be able to coordinate processes working on a
common task

Lock variables (semaphores) are used to coordinate or
synchronize processes

Need an architecture-supported arbitration mechanism to
decide which processor gets access to the lock variable


Single bus provides arbitration mechanism, since the bus is the
only path to memory – the processor that gets the bus wins
Need an architecture-supported operation that locks the
variable

Lecture 25
Locking can be done via an atomic swap operation (processor
can both read a location and set it to the locked state – test-andset – in the same bus operation)
Fall 2008
Spin Lock Synchronization
Read lock
variable
Spin
No
Unlocked?
(=0?)
Yes
Try to lock variable using swap: atomic
operation
read lock variable and set it
to locked value (1)
No
unlock variable:
set lock variable
to 0
Succeed?
(=0?)
Yes
Finish update of
shared data
.
.
.
Begin update of
shared data
The single winning processor will read a 0 all others processors will read the 1 set by
the winning processor
Lecture 25
Fall 2008
Summing 100,000 Numbers on 10 Processors

Processors start by running a loop that sums their subset of
vector A numbers (vectors A and sum are shared variables,
Pn is the processor’s number, i is a private variable)
sum[Pn] = 0;
for (i = 10000*Pn; i< 10000*(Pn+1); i = i + 1)
sum[Pn] = sum[Pn] + A[i];

The processors then coordinate in adding together the
partial sums (half is a private variable initialized to 10
(the number of processors))
repeat
synch();
/*synchronize first
if (half%2 != 0 && Pn == 0)
sum[0] = sum[0] + sum[half-1];
half = half/2
if (Pn<half) sum[Pn] = sum[Pn] + sum[Pn+half]
until (half == 1);
/*final sum in sum[0]
Lecture 25
Fall 2008
An Example with 10 Processors

synch(): Processors must synchronize before the
“consumer” processor tries to read the results from the
memory location written by the “producer” processor

Barrier synchronization – a synchronization scheme where
processors wait at the barrier, not proceeding until every processor
has reached it
P0
P1
P2
P3
P4
P5
P6
P7
P8
P9
sum[P0] sum[P1] sum[P2] sum[P3]sum[P4]sum[P5]sum[P6] sum[P7] sum[P8] sum[P9]
P0
Lecture 25
P1
P2
P3
P4
Fall 2008
An Example with 10 Processors
sum[P0]sum[P1]sum[P2] sum[P3]sum[P4]sum[P5]sum[P6] sum[P7]sum[P8] sum[P9]
P0
P1
P2
P3
P4
P0
P1
P2
P3
P4
P0
P1
P0
Lecture 25
P5
P6
P7
P8
P9
half = 10
half = 5
half = 2
half = 1
Fall 2008
Barrier Implemented with Spin-Locks

n is a shared variable initialized to the number of
processors,count is a shared variable initialized to 0,
arrive and depart are shared spin-lock variables where
arrive is initially unlocked and depart is initially locked
procedure synch()
lock(arrive);
count := count + 1;
/* count the processors as
if count < n
/* they arrive at barrier
then unlock(arrive)
else unlock(depart);
lock(depart);
count := count - 1;
/* count the processors as
if count > 0
/* they leave barrier
then unlock(depart)
else unlock(arrive);
Lecture 25
Fall 2008
Spin-Locks on Bus Connected ccUMAs

With bus based cache coherency, spin-locks allow
processors to wait on a local copy of the lock in their
caches


Reduces bus traffic – once the processor with the lock releases
the lock (writes a 0) all other caches see that write and invalidate
their old copy of the lock variable. Unlocking restarts the race to
get the lock. The winner gets the bus and writes the lock back to
1. The other caches then invalidate their copy of the lock and on
the next lock read fetch the new lock value (1) from memory.
This scheme has problems scaling up to many
processors because of the communication traffic when
the lock is released and contested
Lecture 25
Fall 2008
Cache Coherence Bus Traffic
Proc P0
Proc P1
Proc P2
Bus activity
1
Has lock
Spins
Spins
None
2
Releases
lock (0)
Spins
Spins
Bus services
P0’s invalidate
Cache miss
Cache miss
Bus services
P2’s cache miss
Waits
Reads lock
(0)
Response to
P2’s cache miss
5
Reads lock
(0)
Swaps lock
Bus services
P1’s cache miss
6
Swaps lock
Swap
succeeds
Response to
P1’s cache miss
7
Swap fails
Has lock
Bus services
P2’s invalidate
8
Spins
Has lock
Bus services
P1’s cache miss
3
4
Sends lock
(0)
Lecture 25
Memory
Update memory
from P0
Sends lock
variable to P1
Fall 2008
Commercial Single Backplane Multiprocessors
Processor
# proc.
MHz
BW/
system
Compaq PL
Pentium Pro
4
200
540
IBM R40
PowerPC
8
112
1800
AlphaServer
Alpha 21164
12
440
2150
SGI Pow Chal MIPS R10000
36
195
1200
Sun 6000
30
167
2600
Lecture 25
UltraSPARC
Fall 2008
Summary


Flynn’s classification of processors – SISD, SIMD, MIMD

Q1 – How do processors share data?

Q2 – How do processors coordinate their activity?

Q3 – How scalable is the architecture (what is the maximum
number of processors)?
Bus connected (shared address UMA’s(SMP’s)) multi’s

Cache coherency hardware to ensure data consistency

Synchronization primitives for synchronization

Scalability of bus connected UMAs limited (< ~ 36 processors)
because the three desirable bus characteristics
- high bandwidth
- low latency
- long length
are incompatible

Network connected NUMAs are more scalable
Lecture 25
Fall 2008