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Vector Processing

Topic 6

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Review: Instructon Level Parallelism

High speed execution based on ins t ruc t ionlevel paral lel ism(ilp): potential of shortinstruction sequences to execute in parallel

High-speed microprocessors exploit ILP by:1) pipelined execution: overlap instructions

2) superscalar execution: issue and execute multipleinstructions per clock cycle

3) Out-of-order execution (commit in-order)

Memory accesses for high-speedmicroprocessor?

Data Cache, possibly multiported, multiple levels

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Review

Speculation: Out-of-order execution, In-order commit

(reorder buffer+rename registers)=>precise exceptions

Software Pipelining Symbolic loop unrolling (instructions from different iterations) to

optimize pipeline with little code expansion, little overhead

Superscalar and VLIW: CPI < 1 (IPC > 1) Dynamic issue vs. Static issue

More instructions issue at same time => larger hazard penalty

# independent instructions = # functional units X latency

Branch Prediction Branch History Table: 2 bits for loop accuracy

Recently executed branches correlated with next branch?

Branch Target Buffer: include branch address & prediction

Predicated Execution can reduce number of branches, number ofmispredicted branches

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Problems with conventional approach

Limits to conventional exploitation of ILP:

1) pipel ined clo ck rate: at some point, each increasein clock rate has corresponding CPI increase

(branches, other hazards)2) instruc t ion fetch and decode: at some point, itshard to fetch and decode more instructions perclock cycle

3) cache hit rate: some long-running (scientific)programs have very large data sets accessed withpoor locality;others have continuous data streams (multimedia)and hence poor locality

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Alternative Model:Vector Processing

+

r1 r2

r3

add r3, r1, r2

SCALAR

(1 operation)

v1 v2

v3

+

vector

length

add.vv v3, v1, v2

VECTOR

(N operations)

Vector processors have high-level operations that workon linear arrays of numbers: "vectors"

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Properties of Vector Processors

Each result is independent of previous results (no DH)

long pipeline, compiler ensures no dependencies

high clock rate

Vectorload/store instructions access memory with

known patternhighly interleaved memory

amortize memory latency of over 64 elements

no (data) caches required! (Do use instruction cache)

Reduces branches and branch problems in pipelines(no explicit branch)

Single vector instruction implies lots of work ( i.e.loop)

fewer instruction fetches (reduces Flynns Bottleneck)

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Spec92fp Operations (Millions) Instructions (M)Program RISC Vector R / V RISC Vector R / V

swim256 115 95 1.1x 115 0.8 142x

hydro2d 58 40 1.4x 58 0.8 71xnasa7 69 41 1.7x 69 2.2 31x

su2cor 51 35 1.4x 51 1.8 29x

tomcatv 15 10 1.4x 15 1.3 11xwave5 27 25 1.1x 27 7.2 4x

mdljdp2 32 52 0.6x 32 15.8 2x

Operation & Instruction Count:RISC v. Vector Processor(from F. Quintana, U. Barcelona.)

Vector reduces ops by 1.2X, instructions by 20X

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Basic Vector Processor Architectures

Memory-Memory Vector Processo rs: all vectoroperations are memory to memory (i.e. allvector operands are in memory)

Vecto r-Regis ter p rocessors: all vectoroperations between vector registers (exceptload and store) Vector equivalent of load-store architectures

Includes all vector machines since late 1980s:

Cray, Convex, Fujitsu, Hitachi, NEC We assume vector-register for rest of lectures

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Components of Vector Processor

Vector Register: fixed length bank holding a single vector

has at least 2 read and 1 write ports

typically 8-32 vector registers, each holding 64-128 64-bitelements

Vector Functional Units (FUs): fully pipelined, start new

operation every clock typically 4 to 8 FUs: FP add, FP mult, FP reciprocal (1/X), integer

add, logical, shift; may have multiple of same unit

Vector Load-Store Units (LSUs): fully pipelined unit to loador store a vector; may have multiple LSUs

Scalar registers: single element for FP scalar or address

Cross-bar to connect FUs , LSUs, registers

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Typical Vector Instructions (e.g. DLXV)

Instr. Operands Operation Comment

ADDV V1,V2,V3 V1=V2+V3 vector + vector

ADDSV V1,F0,V2 V1=F0+V2 scalar + vector

MULTVV1,V2,V3 V1=V2xV3 vector x vector

MULSV V1,F0,V2 V1=F0xV2 scalar x vector LV V1,R1 V1=M[R1..R1+63] load v, stride=1

SV R1,V1 M[R1..R1+63]=V1 store v, stride=1

LVWS V1,R1,R2 V1=M[R1..R1+63*R2] load v, stride=R2

LVI V1,R1,V2 V1=M[R1+V2i,i=0..63] indir.("gather") CeqV VM,V1,V2 VMASKi = (V1i=V2i)? comp. setmask

MOV VLR,R1 Vec. Len. Reg. = R1 set vector length

MOV VM,R1 Vec. Mask = R1 set vector mask

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Memory operations

Load/store operations move groups of databetween registers and memory

Three types of addressing

Unit stride Fastest

Non-unit (constant) stride

Indexed (gather-scatter)

Vector equivalent of register indirect Good for sparse arrays of data

Increases number of programs that can bevectorized

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DAXPY (Y = a *X + Y)

LD F0,aADDI R4,Rx,#512 ;last address to load

loop: LD F2, 0(Rx) ;load X(i)

MULTD F2,F0,F2 ;a*X(i)

LD F4, 0(Ry) ;load Y(i)

ADDD F4,F2, F4 ;a*X(i) + Y(i)SD F4,0(Ry) ;store into Y(i)

ADDI Rx,Rx,#8 ;increment index to X

ADDI Ry,Ry,#8 ;increment index to Y

SUB R20,R4,Rx ;compute bound

BNZ R20,loop ;check if done

LD F0,a ;load scalar a

LV V1,Rx ;load vector X

MULTS V2,F0,V1 ;vector-scalar mult.

LV V3,Ry ;load vector Y

ADDV V4,V2,V3 ;add

SV Ry,V4 ;store the result

Assuming vectors X, Y

are length 64Scalar vs. Vector

578 (2+9*64) vs.321 (1+5*64) ops (1.8X)

578 (2+9*64) vs.6 instructions (96X)

64 operation vectors +no loop overhead

also 64X fewer pipelinehazards

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Example Vector Machines Machine Year Clock Regs Elements FUs LSUs

Cray 1 1976 80 MHz 8 64 6 1 Cray XMP 1983 120 MHz 8 64 8 2 L, 1 S

Cray YMP 1988 166 MHz 8 64 8 2 L, 1 S

Cray C-90 1991 240 MHz 8 128 8 4

Cray T-90 1996 455 MHz 8 128 8 4

Conv. C-1 1984 10 MHz 8 128 4 1

Conv. C-4 1994 133 MHz 16 128 3 1

Fuj. VP200 1982 133 MHz 8-256 32-1024 3 2 Fuj. VP300 1996 100 MHz 8-256 32-1024 3 2

NEC SX/2 1984 160 MHz 8+8K 256+var 16 8

NEC SX/3 1995 400 MHz 8+8K 256+var 16 8

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Vector Linpack Performance(MFLOPS)

Machine Year Clock 100x100 1kx1k Peak(Procs) Cray 1 1976 80 MHz 12 110 160(1)

Cray XMP 1983 120 MHz 121 218 940(4)

Cray YMP 1988 166 MHz 150 307 2,667(8)

Cray C-90 1991 240 MHz 387 902 15,238(16)

Cray T-90 1996 455 MHz 705 1603 57,600(32)

Conv. C-1 1984 10 MHz 3 -- 20(1)

Conv. C-4 1994 135 MHz 160 2531 3240(4) Fuj. VP200 1982 133 MHz 18 422 533(1)

NEC SX/2 1984 166 MHz 43 885 1300(1)

NEC SX/3 1995 400 MHz 368 2757 25,600(4)

Matrix Inverse (gaussian elimination)

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Vector Surprise

Use vectors for inner loop parallelism (no surprise) One dimension of array: A[0, 0], A[0, 1], A[0, 2], ...

think of machine as, say, 32 vector regs each with 64 elements

1 instruction updates 64 elements of 1 vector register

and for outer loop parallelism! 1 element from each column: A[0,0], A[1,0], A[2,0], ...

think of machine as 64 virtual processors (VPs)each with 32 scalar registers! ( multithreaded processor)

1 instruction updates 1 scalar register in 64 VPs

Hardware identical, just 2 compiler perspectives

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Vector Implementation Vector register file

Each register is an array of elements

Size of each register determines maximum

vector length Vector length register determines vector length

for a particular operation

Multiple parallel execution units = lanes(sometimes called pipelines or pipes)

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Execution overhead to execute a singlevector instruction

Vector Start-up time (VST) Initial Latency of the vector operation

Depends on the depth of corresponding FU pipeline

Vector Initiation rate (VIR)

Rate at which data can be fed and results can be takenout

Usually 1 (one) clock cycle

Total execution time per instr = VST + n x VIR

VST for Addv 6, Multv 7, Divv 20, Loadv 12

We assume minimum 4 stall cycles betweendependent vector instructions

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Vector length and Stride

DO 10 I = 1, N

10 Y(I) = a * X(I) + Y(I)

N is not 64 and is a dynamic value

Maximum Vector Length (MVL)

For the above example, divide the loop intosmaller sub-loops, such that each sub-loopcan be coded using a single vector

instruction. This is called Strip Mining.

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LOW = 1

VL = N mod MVL

DO 20 J = 0, N/MVL

DO 10 I = LOW, LOW + VL 1

Y(I) = a * X(I) + Y(I) 10 CONTINUE

LOW = LOW + VL

VL = MVL

20 CONTINUE

VLR to hold the length of a vector, initially Nmod MVL, then MVL

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do 10 i = 1, 100

do 10 j = 1, 100

A(i,j) = 0.0

do 10 k = 1, 100

A(i,j)= A(i,j) + B(i,k)*C(k,j) 10 continue

Assume: matrix stored column-order

B: accessed with a stride of 100

C: accessed with a stride of 1

LVWS v1, R1, R2

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Non Unit Stride

It is difficult to ensure memory access

efficiency Suppose in a particular machine, No. of

Memory banks = 16, Stride = 32

In this case, we access all the elements from

the single bank , so no advantage of memoryinterleaving

Suppose, no. of bank is increased to 64, thenwe can access atleast two elements at a time

A bank conflict and hence a stall will occur if

LCM (Stride, # of Banks)/ Stride >= Memoryaccess time

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Vectorization Techniques

1. User has to explicitly specify

2. A Conventional program will be converted toa vector code (try to vectorize only loops,check for dependency between successiveiterations)

Detection of loop dependencies

Do 10 i = 1, 100

A(i+1) = A(i) + B(i) (S1)

B(i+1) = B(i) + A(i+1) ... (S2)

10 continue

S1 uses a value computed by S1 and S2 in theprevious iteration

S2 uses a value computed by S1 in the same iteration

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Loop carried dependencies

Spans across iterations

Compiler must detect loop carried dependencies

GCD-TEST : X(a*i + b) = X (c*i + d) +

a,b,c, d are integer constatnts

i,j are loop indexes which vary from m to n A dependence exists if the following conditions holds

1. There are two iteration indexes i and j, both within therange of the loop

2. a*i+b = c*j + d

If a loop-carried dependency exists, then GCD (c,a) mustdivide (d-b)

-assumptions: (i) iteration index must start from 1 (ii)iteration index must increment by 1

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GCD-Test Examples

Ex1:

Do 10 i=1, 100

10 x (2*i+3)= x(2*i)+5.0

Here, a=2, b=3, c=2, d=0

GCD (c,a) = 2

D-b = -3

Ex2:

do 10 i=1, 50

10 x(2*I +100) = x(i+2)

Ex3:

Do i=2, 50, 2

10 X(3*i-5) =x(i)

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Vector Terminology:4 lanes, 2 vector functional units

(Vector

Functional

Unit)

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Vector Execution Time

Time = f(vector length, data dependencies, struct.hazards)

Init iat ion rate: rate that FU consumes vector elements(= number of lanes; usually 1 or 2 on Cray T-90)

Convoy: set of vector instructions that can beginexecution in same clock (no structural or data hazards)

Chime: approx. time for a vector operation

mconvoys take mchimes; if each vector length is n, thenthey take approx. mx nclock cycles (ignores overhead;good approximization for long vectors)

4 convoys, 1 lane, VL=64=> 4 x 64 = 256 clocks(or 4 clocks per result)

1: LV V1,Rx ;load vector X

2: MULV V2,F0,V1 ;vector-scalar mult.

LV V3,Ry ;load vector Y

3: ADDV V4,V2,V3 ;add

4: SV Ry,V4 ;store the result

DLXV St t Ti

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DLXV Start-up TimeStart-up t ime: pipeline latency time (depth of FU

pipeline); + other sources of overhead

Operation Start-up penalty (from CRAY-1)Vector load/store 12

Vector multiply 7

Vector add 6

Assume convoys don't overlap; vector length = n

Convo y Start 1st result last result

1. LV 0 12 11+n (12+n-1)

2. MULV, LV 12+n 12+n+7 18+2n Mult ip lystar tup

12+n+1 12+n+13 24+2n Load start-up

3. ADDV 25+2n 25+2n+6 30+3n Waitconvoy 2

4. SV 31+3n 31+3n+12 42+4n Wait

convoy 3

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Why startup time for each vector

instruction? Why not overlap startup time of back-to-back

vector instructions?

Cray machines built from many ECL chips

operating at high clock rates; hard to do? Berkeley vector design (T0) didnt know it

wasnt supposed to do overlap, so no startuptimes for functional units (except load)

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Vector Load/Store Units & Memories

Start-up overheads usually longer for LSUs Memory system must sustain (# lanes x word)/clock cycle

Many Vector Processors use banks (versus simpleinterleaving):

1) support multiple loads/stores per cycle=> multiple banks & address banks independently

2) support non-sequential accesses (see soon)

Note: No. memory banks > memory latency to avoid stalls

mbanks => mwords per memory latencylclocks

ifm< l, then gap in memory pipeline:

clock: 0 l l+1 l+2 l+m- 1 l+m 2 l

word: -- 0 1 2 m-1 -- m

may have 1024 banks in SRAM

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Vector Length

What to do when vector length is not exactly 64?

vector- leng th register(VLR) controls the length ofany vector operation, including a vector load or

store. (cannot be > the length of vector registers)do 10 i = 1, n

10 Y(i) = a * X(i) + Y(i)

Don't know n until runtime!

n > Max. Vector Length (MVL)?

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Strip Mining Suppose Vector Length > Max. Vector Length (MVL)?

Strip mining: generation of code such that each vectoroperation is done for a size MVL

1st loop do short piece (n mod MVL), rest VL = MVL

low = 1

VL = (n mod MVL) /*find the odd size piece*/do 1 j = 0,(n / MVL) /*outer loop*/

do 10 i = low,low+VL-1 /*runs for length VL*/Y(i) = a*X(i) + Y(i) /*main operation*/

10 continue

low = low+VL /*start of next vector*/VL = MVL /*reset the length to max*/

1 continue

Loop Overhead!

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Common Vector Metrics

R: MFLOPS rate on an infinite-length vector vector speed of light

Real problems do not have unlimited vector lengths, and the start-uppenalties encountered in real problems will be larger

(Rn is the MFLOPS rate for a vector of length n)

N1/2: The vector length needed to reach one-half of R a good measure of the impact of start-up

NV

: The vector length needed to make vector modefaster than scalar mode

measures both start-up and speed of scalars relative to vectors, qualityof connection of scalar unit to vector unit

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Vector Stride

Suppose adjacent elements not sequential in memory

do 10 i = 1,100do 10 j = 1,100

A(i,j) = 0.0

do 10 k = 1,100

10 A(i,j) = A(i,j)+B(i,k)*C(k,j)

Either B or C accesses not adjacent (800 bytes between)

str ide: distance separating elements that are to bemerged into a single vector (caches do unit stride)=> LVWS (load vector with stride) instruction

Strides => can cause bank conflicts(e.g., stride = 32 and 16 banks)

Think of address per vector element

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Compiler Vectorization on Cray XMP

Benchmark %FP %FP in vector

ADM 23% 68%

DYFESM 26% 95%

FLO52 41% 100%

MDG 28% 27% MG3D 31% 86%

OCEAN 28% 58%

QCD 14% 1%

SPICE 16% 7% (1% overall)

TRACK 9% 23%

TRFD 22% 10%

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Vector Opt #1: Chaining

Suppose:

MULV V1,V2,V3ADDV V4,V1,V5 ; separate convoy?

chaining: vector register (V1) is not as a single entity butas a group of individual registers, then pipeline

forwarding can work on individual elements of a vector Flexib le chaining: allow vector to chain to any other

active vector operation => more read/write ports

As long as enough HW, increases convoy size

7 664 64

7 64

6 64

Unchained

Chained

multv addv

multv

addv

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Example Execution of Vector CodeVector

Memory Pipeline

Vector

Multiply Pipeline

Vector

Adder Pipeline

8 lanes, vector length 32,

chaining

Scalar

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Vector Opt #2: Conditional Execution

Suppose:do 100 i = 1, 64

if (A(i) .ne. 0) then

A(i) = A(i) B(i)

endif100 continue

vector-mask con troltakes a Boolean vector: whenvector -mask registeris loaded from vector test, vector

instructions operate only on vector elements whosecorresponding entries in the vector-mask register are 1.

Still requires clock even if result not stored; if stillperforms operation, what about divide by 0?

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Vector Opt #3: Sparse Matrices

Suppose:

do 100 i = 1,n

100 A(K(i)) = A(K(i)) + C(M(i))

gather(LVI) operation takes an index vectorand fetches

the vector whose elements are at the addresses given byadding a base address to the offsets given in the indexvector => a nonsparse vector in a vector register

After these elements are operated on in dense form, thesparse vector can be stored in expanded form by a

scatterstore (SVI), using the same index vector Can't be done by compiler since can't know Ki elements

distinct, no dependencies; by compiler directive

Use CVI to create index 0, 1xm, 2xm, ..., 63xm

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Sparse Matrix Example

Cache (1993) vs. Vector (1988)

IBM RS6000 Cray YMP

Clock 72 MHz 167 MHz

Cache 256 KB 0.25 KB

Linpack 140 MFLOPS 160 (1.1)

Sparse Matrix 17 MFLOPS 125 (7.3)(Cholesky Blocked )

Cache: 1 address per cache block (32B to 64B)

Vector: 1 address per element (4B)

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Challenges:Vector Example with dependency

/* Multiply a[m][k] * b[k][n] to get c[m][n] */for (i=1; i

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Optimized Vector Example

/* Multiply a[m][k] * b[k][n] to get c[m][n] */for (i=1; i

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Vector for Multimedia?

Intel MMX: 57 new 80x86 instructions (1st since 386) similar to Intel 860, Mot. 88110, HP PA-71000LC, UltraSPARC

3 data types: 8 8-bit, 4 16-bit, 2 32-bit in 64bits reuse 8 FP registers (FP and MMX cannot mix)

short vector: load, add, store 8 8-bit operands

Claim: overall speedup 1.5 to 2X for 2D/3D graphics,audio, video, speech, comm., ...

use in drivers or added to library routines; no compiler

+

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MMX Instructions

Move 32b, 64b

Add, Subtract in parallel: 8 8b, 4 16b, 2 32b opt. signed/unsigned saturate (set to max) if overflow

Shifts (sll,srl, sra), And, And Not, Or, Xorin parallel: 8 8b, 4 16b, 2 32b

Multiply, Multiply-Add in parallel: 4 16b

Compare = , > in parallel: 8 8b, 4 16b, 2 32b sets field to 0s (false) or 1s (true); removes branches

Pack/Unpack Convert 32b 16b, 16b 8b

Pack saturates (set to max) if number is too large

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Vectors and Variable Data Width

Programmer thinks in terms of vectors of dataof some width (8, 16, 32, or 64 bits)

Good for multimedia; More elegant thanMMX-style extensions

Dont have to worry about how data stored inhardware

No need for explicit pack/unpack operations

Just think of more virtual processors operating

on narrow data Expand Maximum Vector Length with

decreasing data width:64 x 64bit, 128 x 32 bit, 256 x 16 bit, 512 x 8 bit

M di i

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Mediaprocesing:Vectorizable? Vector Lengths?

Kernel Vector length

Matrix transpose/multiply # vertices at once

DCT (video, communication) image width

FFT (audio) 256-1024

Motion estimation (video) image width, iw/16

Gamma correction (video) image width

Haar transform (media mining) image width

Median filter (image processing) image width

Separable convolution (img. proc.) image width

(from Pradeep Dubey - IBM,http://www.research.ibm.com/people/p/pradeep/tutor.html)

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Vector Pitfalls

Pitfall: Concentrating on peak performance and ignoringstart-up overhead:

e.g. NV (length faster than scalar) > 100 (CDC-star)

Pitfall: Increasing vector performance, without

comparable increases in scalar performance(Amdahl's Law)

failure of Cray competitor from his former company

Pitfall: Good processor vector performance withoutproviding good memory bandwidth

MMX?

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Vector Advantages Easy to get high performance; N operations:

are independent

use same functional unit

access disjoint registers

access registers in same order as previous instructions

access contiguous memory words or known pattern

can exploit large memory bandwidth hide memory latency (and any other latency)

Scalable (get higher performance as more HW resources available)

Compact: Describe N operations with 1 short instruction (v. VLIW)

Predictable (real-time) performance vs. statistical performance

(cache) Multimedia ready: choose N * 64b, 2N * 32b, 4N * 16b, 8N * 8b

Mature, developed compiler technology

Vector Disadvantage: Out of Fashion

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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 registernumber 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

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MIPS R10000 vs. T0

*See http://www.icsi.berkeley.edu/real/spert/t0-intro.html

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Vectors Lower Power

Vector One instruction

fetch,decode, dispatch pervector

Structured registeraccesses

Smaller code for highperformance, less power ininstruction 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

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Superscalar Energy Efficiency Even Worse

Vector

Control logic growslinearly with issue width

Vector unit switches

off when not in use

Vector instructions exposeparallelism withoutspeculation

Software control ofspeculation when desired:

Whether to use vector mask orcompress/expand forconditionals

Superscalar Control logic grows

quadratically with issue

width

Control logic consumes

energy regardless of

available parallelism

Speculation to increase

visible parallelism

wastes energy

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VLIW/Out-of-OrderversusModestScalar+Vector

0

100

Applications sorted by Instruction Level Parallelism

Performa

nce

VLIW/OOO

Modest Scalar

Vector

Very Sequential Very Parallel

(Where are important

applications on this axis?)

(Where are crossover

points on these curves?)

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Vector Summary

Alternate model accomodates long memory latency,doesnt rely on caches as does Out-Of-Order,superscalar/VLIW designs

Much easier for hardware: more powerful instructions,

more predictable memory accesses, fewer harzards,fewer branches, fewer mispredicted branches, ...

What % of computation is vectorizable?

Is vector a good match to new apps such as

multidemia, DSP?

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