Computer Architecture, Advanced Architectures
Part VIIAdvanced Architectures
Computer Architecture, Advanced Architectures
VII Advanced Architectures
Topics in This Part
Chapter 25 Road to Higher Performance
Chapter 26 Vector and Array Processing
Chapter 27 Shared-Memory Multiprocessing
Chapter 28 Distributed Multicomputing
Performance enhancement beyond what we have seen:• What else can we do at the instruction execution level?• Data parallelism: vector and array processing• Control parallelism: parallel and distributed processing
Computer Architecture, Advanced Architectures
25 Road to Higher Performance Review past, current, and future architectural trends:
• General-purpose and special-purpose acceleration• Introduction to data and control parallelism
Topics in This Chapter
25.1 Past and Current Performance Trends
25.2 Performance-Driven ISA Extensions
25.3 Instruction-Level Parallelism
25.4 Speculation and Value Prediction
25.5 Special-Purpose Hardware Accelerators
25.6 Vector, Array, and Parallel Processing
Computer Architecture, Advanced Architectures
25.1 Past and Current Performance Trends
0.06 MIPS (4-bit processor)
Intel 4004: The first p (1971) Intel Pentium 4, circa 2005
10,000 MIPS (32-bit processor)
8008
8080
8084
8-bit
8086
80186
80286
16-bit
8088
80188
80386
Pentium, MMX
Pentium Pro, II32-bit
80486
Pentium III, M
Celeron
Computer Architecture, Advanced Architectures
Architectural Innovations for Improved Performance
Architectural method Improvement factor
1. Pipelining (and superpipelining) 3-8 √ 2. Cache memory, 2-3 levels 2-5 √ 3. RISC and related ideas 2-3 √ 4. Multiple instruction issue (superscalar) 2-3 √ 5. ISA extensions (e.g., for multimedia) 1-3 √ 6. Multithreading (super-, hyper-) 2-5 ? 7. Speculation and value prediction 2-3 ? 8. Hardware acceleration 2-10 ? 9. Vector and array processing 2-10 ?10. Parallel/distributed computing 2-1000s ?
Est
ab
lishe
dm
eth
ods
Ne
we
rm
eth
ods
Pre
vio
usly
dis
cuss
ed
Co
vere
d in
Pa
rt V
II
Available computing power ca. 2000: GFLOPS on desktop TFLOPS in supercomputer center PFLOPS on drawing board
Computer performance grew by a factorof about 10000 between 1980 and 2000 100 due to faster technology 100 due to better architecture
Computer Architecture, Advanced Architectures
Peak Performance of SupercomputersPFLOPS
TFLOPS
GFLOPS1980 20001990 2010
Earth Simulator
ASCI White Pacific
ASCI Red
Cray T3DTMC CM-5
TMC CM-2Cray X-MP
Cray 2
10 / 5 years
Dongarra, J., “Trends in High Performance Computing,”Computer J., Vol. 47, No. 4, pp. 399-403, 2004. [Dong04]
Computer Architecture, Advanced Architectures
Energy Consumption is Getting out of Hand
Figure 25.1 Trend in energy consumption for each MIPS of computational power in general-purpose processors and DSPs.
1990 1980 2000 2010 kIPS
MIPS
GIPS
TIPS
Pe
rfo
rma
nce
Calendar year
Absolute processor
performance
GP processor performance
per watt
DSP performance per watt
Computer Architecture, Advanced Architectures
25.2 Performance-Driven ISA Extensions
Adding instructions that do more work per cycle
Shift-add: replace two instructions with one (e.g., multiply by 5)
Multiply-add: replace two instructions with one (x := c + a b) Multiply-accumulate: reduce round-off error (s := s + a b) Conditional copy: to avoid some branches (e.g., in if-then-else)
Subword parallelism (for multimedia applications)
Intel MMX: multimedia extension 64-bit registers can hold multiple integer operands
Intel SSE: Streaming SIMD extension 128-bit registers can hold several floating-point operands
Computer Architecture, Advanced Architectures
Intel MMXISA
Exten-sion
Table25.1
Class Instruction Vector Op type Function or results
Copy
Register copy 32 bits Integer register MMX registerParallel pack 4, 2 Saturate Convert to narrower elementsParallel unpack low 8, 4, 2 Merge lower halves of 2 vectorsParallel unpack high 8, 4, 2 Merge upper halves of 2 vectors
Arith-metic
Parallel add 8, 4, 2 Wrap/Saturate# Add; inhibit carry at boundariesParallel subtract 8, 4, 2 Wrap/Saturate# Subtract with carry inhibitionParallel multiply low 4 Multiply, keep the 4 low halvesParallel multiply high 4 Multiply, keep the 4 high halvesParallel multiply-add 4 Multiply, add adjacent products*
Parallel compare equal 8, 4, 2 All 1s where equal, else all 0s
Parallel compare greater 8, 4, 2 All 1s where greater, else all 0s
ShiftParallel left shift logical 4, 2, 1 Shift left, respect boundariesParallel right shift logical 4, 2, 1 Shift right, respect boundariesParallel right shift arith 4, 2 Arith shift within each (half)word
Logic
Parallel AND 1 Bitwise dest (src1) (src2)Parallel ANDNOT 1 Bitwise dest (src1) (src2)Parallel OR 1 Bitwise dest (src1) (src2)Parallel XOR 1 Bitwise dest (src1) (src2)
Memoryaccess
Parallel load MMX reg 32 or 64 bits Address given in integer registerParallel store MMX reg 32 or 64 bit Address given in integer register
Control Empty FP tag bits Required for compatibility$
Computer Architecture, Advanced Architectures
MMX Multiplication and Multiply-Add
Figure 25.2 Parallel multiplication and multiply-add in MMX.
a
(a) Parallel multiply low (b) Parallel multiply-add
b d e
e f g h
s t u v
e h
d g
b f
a e
z v
y u
x t
w s
a b d e
e f g h
s + t u + v
e h
d g
b f
a e
v
u
t
s
add add
Computer Architecture, Advanced Architectures
MMX Parallel Comparisons
Figure 25.3 Parallel comparisons in MMX.
14
(a) Parallel compare equal (b) Parallel compare greater
3 58 66
79 1 58 65
0 0 0
5 12 3 32
12 3 22
5 12 6 9
12 5 90 17 8 65 535 (all 1s)
0 0 0 0 0
255 (all 1s)
Computer Architecture, Advanced Architectures
25.3 Instruction-Level Parallelism
Figure 25.4 Available instruction-level parallelism and the speedup due to multiple instruction issue in superscalar processors [John91].
1
Fra
ctio
n o
f cyc
les
Issuable instructions per cycle
20%
30%
10%
0% 2 3 4 5 6 7 8 0
Sp
ee
dup
atta
ine
d
Instruction issue width
3
2
1 2 4 6 8 0
(a) (b)
Computer Architecture, Advanced Architectures
Instruction-Level Parallelism
Figure 25.5 A computation with inherent instruction-level parallelism.
Computer Architecture, Advanced Architectures
VLIW and EPIC Architectures
Figure 25.6 Hardware organization for IA-64. General and floating-point registers are 64-bit wide. Predicates are single-bit registers.
VLIW Very long instruction word architectureEPIC Explicitly parallel instruction computing
Memory
General registers (128)
Floating-point registers (128)
Predi- cates (64)
Execution unit
Execution unit
Execution unit
Execution unit
Execution unit
Execution unit
. . .
. . .
Computer Architecture, Advanced Architectures
25.4 Speculation and Value Prediction
Figure 25.7 Examples of software speculation in IA-64.
---- ---- ---- ---- load ---- ----
spec load ---- ---- ---- ---- check load ---- ----
(a) Control speculation
---- ---- store ---- load ---- ----
spec load ---- ---- store ---- check load ---- ----
(b) Data speculation
Computer Architecture, Advanced Architectures
Value Prediction
Figure 25.8 Value prediction for multiplication or division via a memo table.
Mult/ Div
Memo table
Control
Mux
Inputs
Inputs ready
Output
Output ready
0
1
Miss
Done
Computer Architecture, Advanced Architectures
25.5 Special-Purpose Hardware Accelerators
Figure 25.9 General structure of a processor with configurable hardware accelerators.
CPU Configuration
memory
Accel. 1
Accel. 2
Accel. 3
Data and program memory
FPGA-like unit on which accelerators can be formed via loading of configuration registers
Unused resources
Computer Architecture, Advanced Architectures
Graphic Processors, Network Processors, etc.
Figure 25.10 Simplified block diagram of Toaster2, Cisco Systems’ network processor.
Input buffer
PE 0
PE 1
PE 2
PE 3
PE 4
PE 5 PE
6 PE 7
PE 8
PE 9
PE 10
PE 11
PE 12
PE 13
PE 14
PE 15
Output buffer
Column memory
Column memory
Column memory
Column memory
Feedback path
PE5
Computer Architecture, Advanced Architectures
25.6 Vector, Array, and Parallel Processing
Figure 25.11 The Flynn-Johnson classification of computer systems.
SISD
SIMD
MISD
MIMD
GMSV
GMMP
DMSV
DMMP
Single data stream
Mult iple data streams
Sin
gle
inst
r st
ream
M
ultip
le in
str
stre
ams
Flynn’s categories
Joh
nso
n’s
ex
pan
sio
n
Shared variables
Message passing
Glo
bal
me
mor
y D
istr
ibut
ed
me
mor
y
Uniprocessors
Rarely used
Array or vector processors
Mult iproc’s or mult icomputers
Shared-memory mult iprocessors
Rarely used
Distributed shared memory
Distrib-memory mult icomputers
Computer Architecture, Advanced Architectures
SIMD Architectures
Data parallelism: executing one operation on multiple data streams
Concurrency in time – vector processing Concurrency in space – array processing
Example to provide context
Multiplying a coefficient vector by a data vector (e.g., in filtering) y[i] := c[i] x[i], 0 i < n
Sources of performance improvement in vector processing (details in the first half of Chapter 26)
One instruction is fetched and decoded for the entire operation The multiplications are known to be independent (no checking) Pipelining/concurrency in memory access as well as in arithmetic
Array processing is similar (details in the second half of Chapter 26)
Computer Architecture, Advanced Architectures
MISD Architecture Example
Figure 25.12 Multiple instruction streams operating on a single data stream (MISD).
I n s t r u c t i o n s t r e a m s 1-5
Data in
Data out
Computer Architecture, Advanced Architectures
MIMD Architectures
Control parallelism: executing several instruction streams in parallel
GMSV: Shared global memory – symmetric multiprocessors DMSV: Shared distributed memory – asymmetric multiprocessors DMMP: Message passing – multicomputers
Figure 27.1 Centralized shared memory. Figure 28.1 Distributed memory.
0 0
1 1
m 1
Processor-to-
memory network
Processor-to-
processor network
Processors Memory modules
Parallel I/O
. . .
.
.
.
.
.
.
p 1
0
1
Inter- connection
network
Memories and processors
Par
alle
l inp
ut/o
utp
ut
p 1
.
.
.
Routers
A computing node
. . .
Computer Architecture, Advanced Architectures
Amdahl’s Law Revisited
0
10
20
30
40
50
0 10 20 30 40 50Enhancement factor (p )
Spe
edup
(s
)
f = 0
f = 0.1
f = 0.05
f = 0.02
f = 0.01
Figure 4.4 Amdahl’s law: speedup achieved if a fraction f of a task is unaffected and the remaining 1 – f part runs p times as fast.
s =
min(p, 1/f)
1f + (1 – f)/p
f = sequential fraction
p = speedup of the rest with p processors
Computer Architecture, Advanced Architectures
26 Vector and Array Processing Single instruction stream operating on multiple data streams
• Data parallelism in time = vector processing• Data parallelism in space = array processing
Topics in This Chapter
26.1 Operations on Vectors
26.2 Vector Processor Implementation
26.3 Vector Processor Performance
26.4 Shared-Control Systems
26.5 Array Processor Implementation
26.6 Array Processor Performance
Computer Architecture, Advanced Architectures
26.1 Operations on Vectors
Sequential processor:
for i = 0 to 63 do P[i] := W[i]
D[i]endfor
Vector processor:
load Wload DP := W Dstore P
for i = 0 to 63 do X[i+1] := X[i] + Z[i] Y[i+1] := X[i+1] + Y[i] endfor
Unparallelizable
Computer Architecture, Advanced Architectures
26.2 Vector Processor Implementation
Figure 26.1 Simplified generic structure of a vector processor.
Function unit 1 pipeline
To
and
from
mem
ory
uni
t
From scalar registers
Vector register
file
Function unit 2 pipeline
Function unit 3 pipeline
Forwarding muxes
Load unit A
Load unit B
Store unit
Computer Architecture, Advanced Architectures
Conflict-Free Memory Access
Figure 26.2 Skewed storage of the elements of a 64 64 matrix for conflict-free memory access in a 64-way interleaved memory. Elements of column 0 are highlighted in both diagrams .
0,0
1,0
2,0 . . .
62,0
63,0
0,1
1,1
2,1 . . .
62,1
63,1
0,2
1,2
2,2 . . .
62,2
63,2
0,62
1,62
2,62 . . .
62,62
63,62
0,63
1,63
2,63 . . .
62,63
63,63
...
...
... . . . ...
...
0,0
1,63
2,62 . . .
62,2
63,1
0,1
1,0
2,63 . . .
62,3
63,2
0,2
1,0
2,0 . . .
62,4
63,3
0,62
1,61
2,60 . . .
62,0
63,63
0,63
1,62
2,61 . . .
62,1
63,0
... ... ... . . . ... ...
(a) Conventional row-major order (b) Skewed row-major order
Bank number
0 1 62 63 2 . . . 0 1 62 63 2 . . .
Computer Architecture, Advanced Architectures
Overlapped Memory Access and Computation
Figure 26.3 Vector processing via segmented load/store of vectors in registers in a double-buffering scheme. Solid (dashed) lines show data flow in the current (next) segment.
Vector reg 0
Vector reg 1
Vector reg 5
Vector reg 2
Vector reg 3
Vector reg 4
Load X
Load Y
Store Z
To
an
d fr
om
mem
ory
un
it
Pipelined adder
Computer Architecture, Advanced Architectures
26.3 Vector Processor Performance
Figure 26.4 Total latency of the vector computation S := X Y + Z, without and with pipeline chaining.
Multiplication start-up
Addition start-up
+
+
Without chaining
With pipeline chaining
Time
Computer Architecture, Advanced Architectures
Performance as a Function of Vector Length
Figure 26.5 The per-element execution time in a vector processor as a function of the vector length.
Vector length 100 200 300 400 0
Clo
ck c
ycle
s pe
r ve
ctor
ele
me
nt
5
4
3
2
1
0
Computer Architecture, Advanced Architectures
26.4 Shared-Control Systems
Figure 26.6 From completely shared control to totally separate controls.
(a) Shared-control array processor, SIMD
(b) Multiple shared controls, MSIMD
(c) Separate controls, MIMD
Processing Control
. . .
Processing Control
. . .
Processing Control
. . .
. . .
Computer Architecture, Advanced Architectures
Example Array Processor
Figure 26.7 Array processor with 2D torus interprocessor communication network.
Control broadcast Parallel
I/O
Processor array Control
Switches
Computer Architecture, Advanced Architectures
26.5 Array Processor Implementation
Figure 26.8 Handling of interprocessor communication via a mechanism similar to data forwarding.
ALU Reg file
CommunDir
CommunEn
PE state FF
Data memory
To array state reg
To reg file and data memory
Commun buffer
N E
W S To NEWS
neighbors
0
1
Computer Architecture, Advanced Architectures
Configuration Switches
Figure 26.9 I/O switch states in the array processor of Figure 26.7.
Control broadcast Parallel
I/O
Processor array Control
Switches
Figure 26.7
(a) Torus operation
In
(b) Clockwise I/O (c) Counterclockwise I/O
Out
In
Out
Computer Architecture, Advanced Architectures
26.6 Array Processor Performance
Array processors perform well for the same class of problems thatare suitable for vector processors
For embarrassingly (pleasantly) parallel problems, array processorscan be faster and more energy-efficient than vector processors
A criticism of array processing:For conditional computations, a significant part of the array remainsidle while the “then” part is performed; subsequently, idle and busyprocessors reverse roles during the “else” part
However:Considering array processors inefficient due to idle processorsis like criticizing mass transportation because many seats are unoccupied most of the time
It’s the total cost of computation that counts, not hardware utilization!
Computer Architecture, Advanced Architectures
27 Shared-Memory Multiprocessing Multiple processors sharing a memory unit seems naïve
• Didn’t we conclude that memory is the bottleneck?• How then does it make sense to share the memory?
Topics in This Chapter
27.1 Centralized Shared Memory
27.2 Multiple Caches and Cache Coherence
27.3 Implementing Symmetric Multiprocessors
27.4 Distributed Shared Memory
27.5 Directories to Guide Data Access
27.6 Implementing Asymmetric Multiprocessors
Computer Architecture, Advanced Architectures
Parallel Processing as a Topic of Study
Graduate course ECE 254B:Adv. Computer Architecture – Parallel Processing
An important area of studythat allows us to overcomefundamental speed limits
Our treatment of the topic isquite brief (Chapters 26-27)
Computer Architecture, Advanced Architectures
27.1 Centralized Shared Memory
Figure 27.1 Structure of a multiprocessor with centralized shared-memory.
0 0
1 1
m 1
Processor-to-
memory network
Processor-to-
processor network
Processors Memory modules
Parallel I/O
. . .
.
.
.
.
.
.
p 1
Computer Architecture, Advanced Architectures
Processor-to-Memory Interconnection Network
Figure 27.2 Butterfly and the related Beneš network as examples of processor-to-memory interconnection network in a multiprocessor.
(a) Butterfly network (b) Beneš network
0
2
4
6
8
10
12
14
Processors Memories
P r o c e s s o r s
M e m o r i e s
1
3
5
7
9
11
13
15
0
2
4
6
8
10
12
14
1
3
5
7
9
11
13
15
0
2
4
6
0
2
4
6
1
3
5
7
1
3
5
7
Row 0
Row 1
Row 2
Row 3
Row 4
Row 5
Row 6
Row 7
Computer Architecture, Advanced Architectures
Processor-to-Memory Interconnection Network
Figure 27.3 Interconnection of eight processors to 256 memory banks in Cray Y-MP, a supercomputer with multiple vector processors.
0
1
2
3
4
5
6
7
8 8
8 8
8 8
8 8
4 4
4 4
4 4
4 4
4 4
4 4
4 4
4 4
Sections Subsections Memory banks
0, 4, 8, 12, 16, 20, 24, 28 32, 36, 40, 44, 48, 52, 56, 60
1, 5, 9, 13, 17, 21, 25, 29
2, 6, 10, 14, 18, 22, 26, 30
3, 7, 11, 15, 19, 23, 27, 31
Processors
1 8 switches 224, 228, 232, 236, . . . , 252
225, 229, 233, 237, . . . , 253
226, 230, 234, 238, . . . , 254
227, 231, 235, 239, . . . , 255
8 /
8 /
8 /
8 /
Computer Architecture, Advanced Architectures
Shared-Memory Programming: BroadcastingCopy B[0] into all B[i] so that multiple
processorscan read its value without memory access conflicts
for k = 0 to log2 p – 1 processor j, 0 j < p, do
B[j + 2k] := B[j]endfor
0 1 2 3 4 5 6 7 8 9 10 11
B
Recursivedoubling
Computer Architecture, Advanced Architectures
Shared-Memory Programming: SummationSum reduction of vector X
processor j, 0 j < p, do Z[j] := X[j]
s := 1while s < p processor j, 0 j < p – s,
do Z[j + s] := X[j] + X[j + s] s := 2 sendfor
0 1 2 3 4 5 6 7 8 9
S 0:0 1:1 2:2 3:3 4:4 5:5 6:6 7:7 8:8 9:9
0:0 0:1 1:2 2:3 3:4 4:5 5:6 6:7 7:8 8:9
0:0 0:1 0:2 0:3 1:4 2:5 3:6 4:7 5:8 6:9
0:0 0:1 0:2 0:3 0:4 0:5 0:6 0:7 1:8 2:9
0:0 0:1 0:2 0:3 0:4 0:5 0:6 0:7 0:8 0:9
Recursivedoubling
Computer Architecture, Advanced Architectures
27.2 Multiple Caches and Cache Coherence
Private processor caches reduce memory access traffic through the interconnection network but lead to challenging consistency problems.
0 0
1 1
m 1
Processor-to-
memory network
p 1
Processor-to-
processor network
Processors Caches Memory modules
Parallel I/O
. . .
.
.
.
.
.
.
Computer Architecture, Advanced Architectures
Status of Data Copies
Figure 27.4 Various types of cached data blocks in a parallel processor with centralized main memory and private processor caches.
0
1
Processor-to-
memory network
p–1
Processor-to-
processor network
Processors Caches Memory modules
Parallel I/O
. . .
.
.
.
.
.
.
w x
y
z
w z
w y
x z
Multiple consistent
Single consistent
Single inconsistent
Invalid
m–1
0
1
Computer Architecture, Advanced Architectures
A Snoopy Cache Coherence
Protocol
Figure 27.5 Finite-state control mechanism for a bus-based snoopy cache coherence protocol with write-back caches.
CPU read or write hit
Invalid
Shared (read-only)
Exclusive (writable)
CPU read hit
CPU read miss: signal read miss
on bus
CPU w rite miss: signal write miss
on bus
CPU w rite hit: signal write miss on bus
Bus write miss: write back cache line
Bus write miss
Bus read miss: write back cache line
P
C
P
C
P
C
P
C
BusMemory
Computer Architecture, Advanced Architectures
27.3 Implementing Symmetric Multiprocessors
Figure 27.6 Structure of a generic bus-based symmetric multiprocessor.
Computing nodes (typically, 1-4 CPUs
and caches per node)
Interleaved memory
Bus adapter
I/O modules
Standard interfaces
Bus adapter
Very wide, high-bandwidth bus
Computer Architecture, Advanced Architectures
Bus Bandwidth Limits PerformanceExample 27.1
Consider a shared-memory multiprocessor built around a single bus with a data bandwidth of x GB/s. Instructions and data words are 4 B wide, each instruction requires access to an average of 1.4 memory words (including the instruction itself). The combined hit rate for caches is 98%. Compute an upper bound on the multiprocessor performance in GIPS. Address lines are separate and do not affect the bus data bandwidth.
Solution
Executing an instruction implies a bus transfer of 1.4 0.02 4 = 0.112
B. Thus, an absolute upper bound on performance is x/0.112 = 8.93x GIPS. Assuming a bus width of 32 B, no bus cycle or data going to waste, and a bus clock rate of y GHz, the performance bound becomes 286y GIPS. This bound is highly optimistic. Buses operate in the range 0.1 to 1 GHz. Thus, a performance level approaching 1 TIPS (perhaps even ¼ TIPS) is beyond reach with this type of architecture.
Computer Architecture, Advanced Architectures
Implementing Snoopy Caches
Figure 27.7 Main structure for a snoop-based cache coherence algorithm.
Tags
Cache data array
Duplicate tags and state store for snoop side
CPU
Main tags and state store for processor side
=?
=?
Processor side cache control
Snoop side cache control
Addr Addr Cmd Cmd Buffer Buffer Snoop state
System bus
Tag
Addr Cmd
State
Computer Architecture, Advanced Architectures
27.4 Distributed Shared Memory
Figure 27.8 Structure of a distributed shared-memory multiprocessor.
0
1 z : 0
x : 0 y : 1
Inter- connection
network
Processors with memory
Pa
ralle
l inp
ut/o
utp
ut
.
.
.
p 1
y := -1 z := 1
while z=0 do x := x + y endwhile
Routers
Computer Architecture, Advanced Architectures
27.5 Directories to Guide Data Access
Figure 27.9 Distributed shared-memory multiprocessor with a cache, directory, and memory module associated with each processor.
0
1
Inter- connection
network
Processors & caches
Pa
ralle
l inp
ut/o
utp
ut
.
.
.
p 1
Memories
Directories Communication & memory interfaces
Computer Architecture, Advanced Architectures
Directory-Based Cache Coherence
Figure 27.10 States and transitions for a directory entry in a directory-based cache coherence protocol (c is the requesting cache).
Write miss: return value, set sharing set to {c}
Uncached
Shared (read-only)
Exclusive (writable)
Read miss: return value, include c in sharing set
Read miss: return value, set sharing set to {c}
Write miss: invalidate all cached copies, set sharing set to {c}, return value
Data w rite-back: set sharing set to { }
Read miss: fetch data from owner, return value, include c in sharing set
Write miss: fetch data from owner, request invalidation,
return value, set sharing set to {c}
Computer Architecture, Advanced Architectures
27.6 Implementing Asymmetric Multiprocessors
Figure 27.11 Structure of a ring-based distributed-memory multiprocessor.
Computing nodes (typically, 1-4 CPUs and associated memory)
Link
To I/O controllers
Memory
Ring network
Link Link Link
Node 0 Node 1 Node 2 Node 3
Computer Architecture, Advanced Architectures
Scalable Coherent Interface
(SCI)
Figure 27.11 Structure of a ring-based distributed-memory multiprocessor.
0
1
Processors and caches
To
inte
rco
nnec
tion
net
wo
rk
3
Memories
2
Computer Architecture, Advanced Architectures
28 Distributed MulticomputingComputer architects’ dream: connect computers like toy blocks
• Building multicomputers from loosely connected nodes• Internode communication is done via message passing
Topics in This Chapter
28.1 Communication by Message Passing
28.2 Interconnection Networks
28.3 Message Composition and Routing
28.4 Building and Using Multicomputers
28.5 Network-Based Distributed Computing
28.6 Grid Computing and Beyond
Computer Architecture, Advanced Architectures
28.1 Communication by Message Passing
Figure 28.1 Structure of a distributed multicomputer.
0
1
Inter- connection
network
Memories and processors
Pa
ralle
l inp
ut/o
utp
ut
p 1
.
.
.
Routers
A computing node
Computer Architecture, Advanced Architectures
Router Design
Figure 28.2 The structure of a generic router.
Switch
Inp
ut c
hann
els
Routing and arbitration
Input queues
Q
Q
Q
Q
Q
Q
Q
Q
LC
LC
LC
LC
LC
LC
LC
LC Out
put
chan
nels
Output queues
Q Q
LC LC Link controller
Message queue
Injection channel Ejection channel
Computer Architecture, Advanced Architectures
Building Networks from Switches
Straight through Crossed connection Lower broadcast Upper broadcast
Figure 28.3 Example 2 2 switch with point-to-point and broadcast connection capabilities.
(a) Butterfly network (b) Beneš network
0
2
4
6
8
10
12
14
Processors Memories
P r o c e s s o r s
M e m o r i e s
1
3
5
7
9
11
13
15
0
2
4
6
8
10
12
14
1
3
5
7
9
11
13
15
0
2
4
6
0
2
4
6
1
3
5
7
1
3
5
7
Row 0
Row 1
Row 2
Row 3
Row 4
Row 5
Row 6
Row 7
Figure 27.2Butterfly and Beneš networks
Computer Architecture, Advanced Architectures
Interprocess Communication via Messages
Figure 28.4 Use of send and receive message-passing primitives to synchronize two processes.
Process A Process B
...
...
...
...
...
... send x ... ... ... ... ... ... ...
...
... receive x ... ... ... Time
Communication latency
Process B is suspended
Process B is awakened
Computer Architecture, Advanced Architectures
28.2 Interconnection Networks
Figure 28.5 Examples of direct and indirect interconnection networks.
(a) Direct network (b) Indirect network
Routers Nodes Nodes
Computer Architecture, Advanced Architectures
Direct Interconnection
Networks
Figure 28.6 A sampling of common direct interconnection networks. Only routers are shown; a computing node is implicit for each router.
(a) 2D torus (b) 4D hypercube
(c) Chordal ring (d) Ring of rings
Computer Architecture, Advanced Architectures
Indirect Interconnection Networks
Figure 28.7 Two commonly used indirect interconnection networks.
(a) Hierarchical buses (b) Omega network
Level-1 bus
Level-2 bus
Level-3 bus
Computer Architecture, Advanced Architectures
28.3 Message Composition and Routing
Figure 28.8 Messages and their parts for message passing.
Message Padding
Packet data
Last packet Header Trailer
A transmitted packet
Flow control digits (flits)
Data or payload First packet
Computer Architecture, Advanced Architectures
Wormhole Switching
Figure 28.9 Concepts of wormhole switching.
Worm 1: moving
(a) Two worms en route to their respective destinations
Source 2
Source 1
Destination 1
Destination 2
Worm 2: blocked
(b) Deadlock due to circular waiting of four blocked worms
Each worm is blocked at the point of attempted right turn
Computer Architecture, Advanced Architectures
28.4 Building and Using Multicomputers
Figure 28.10 A task system and schedules on 1, 2, and 3 computers.
(a) Static task graph (b) Schedules on 1-3 computers
Inputs
Outputs
t = 1
t = 1
t = 2
t = 2 t = 2
t = 3
B
A C
D
E
F
G
H
t = 1
t = 2
B A C D E F G H
B A C
D E
H F G
B A C
D
E F G H
0 5 10 15
Time
Computer Architecture, Advanced Architectures
Building Multicomputers from Commodity Nodes
Figure 28.11 Growing clusters using modular nodes.
(a) Current racks of modules (b) Futuristic toy-block construction
Expansion slots
One module: CPU,
memory, disks
One module: CPU(s), memory,
disks
Wireless connection surfaces
Computer Architecture, Advanced Architectures
28.5 Network-Based Distributed Computing
Figure 28.12 Network of workstations.
System or I/O bus PC
Fast network interface with large memory
NIC
Network built of high-speed
wormhole switches
Computer Architecture, Advanced Architectures
28.6 Grid Computing and Beyond
Computational grid is analogous to the power grid
Decouples the “production” and “consumption” of computational power
Homes don’t have an electricity generator; why should they have a computer?
Advantages of computational grid:
Near continuous availability of computational and related resources
Resource requirements based on sum of averages, rather than sum of peaks
Paying for services based on actual usage rather than peak demand
Distributed data storage for higher reliability, availability, and security
Universal access to specialized and one-of-a-kind computing resources
Still to be worked out: How to charge for computation usage