pipelined van emde boas tree: algorithms, analysis, and applications hao wang and bill lin...

Pipelined van Emde Boas Tree: Algorithms, Analysis, and Applications

Hao Wang and Bill Lin

University of California, San Diego

IEEE Infocom 2007 2

Introduction

• Priority queues used many network applications• Per-flow weighted fair queueing• Management of per-flow packet buffers using DRAM• Maintenance of exact statistics counters for real-time

network measurements

• Items in priority queue sorted at all times(e.g. smallest key first)

• Challenge: Need to operate at high speeds (e.g. 10+ Gb/s)

IEEE Infocom 2007 3

Introduction

• Binary heap common data structure for priority queues

• O(lg n) time complexity, where n is # items

• e.g. in fine-grained per-flow weighted fair queueing, n can be very large (e.g. 1 million)

• O(lg n) may be too slow for high line rates

• Pipeline heaps [Bhagwan, Lin 2000][Ioannou, Katevenis 2001]

• Reduced time complexity to O(1)• At the expense of O(lg n) pipeline stages

IEEE Infocom 2007 4

This Talk

• Instead of pipelining binary heaps, we present new approach based on pipelining van Emde Boas trees

• van Emde Boas (vEB) trees introduced in 1975• Instead of maintaining a priority queue of sorted

items, maintain a sorted dictionary of keys• In many applications, since keys are represented by

a w-bit integer, possible keys can only be from fixed universe of u = 2w values

• Only O(lg lg u) complexity vs. O(lg n) for heaps

• Main result: pipelined vEB with O(1) operation andO(lg lg u) pipeline stages

IEEE Infocom 2007 5

van Emde Boas (vEB) Trees

• Goal: Maintain a sorted subset S from universe

U = {0, 1, …, u – 1} of size u = 2w, subject to INSERT, DELETE, EXTRACTMIN, SUCCESSOR, PREDECESSOR. e.g.

• INSERT – Inserts new item into the queue

• EXTRACTMIN – Removes item with smallest key

IEEE Infocom 2007 6

vEB Trees

• Size u vs. n depends on application. e.g.

• If u is only polynomial in n, i.e. u = O(nc), then

O(lg lg u) = O(lg lg n), exponential speedup over O(lg n)

Per-flowFair Queues

24 bits 4 × 106

StatisticsCounters

106 counters512 groups

106 flows

Application w u = 2w n

5

3

O(lg lg u)

20

208

O(lg n)

u ≈ n

u « nu ≈ n

compare

5128 bits

IEEE Infocom 2007 7

H and L[0], …,L[ – 1] recursivelydefined as vEB treeswith w/2 bits

H and L[0], …,L[ – 1] recursivelydefined as vEB treeswith w/2 bits

vEB Trees

• Conceptually think of the universe of w-bits,

U = {0, 1, …, 2w – 1}, as a binary tree with height w• Think of top part H of w/2 bits,

and bottom part of sub-trees ofw/2 bits, L[0], L[1], …, L[ – 1]

L[0] L[1] L[ -1]. . .

Hw/2 bits

w/2 bits

sub-trees

IEEE Infocom 2007 8

vEB Trees

• Suppose w = 8 bits, consider e.g. a key x = 31

• Split x into high and low parts:

• Consider INSERT( x, S )

• If xh H, recursively call

• INSERT( xl , L[xh] )

• Else, recursively call

• INSERT( xh , H )


• Can avoid 2nd recursionby storing min[S]

L[0] L[1] L[ -1]. . .

H w/2 bits

w/2 bits

sub-trees

0 0 0 1 1 1 1 1

xh xl

IEEE Infocom 2007 9

Representing vEB Trees

• min[S] : minimum key in S

• n[S] : number of elements in S

• H, L’s : just pointers to corresponding vEB sub-trees

H L[0] L[1] L[ – 1]

min[S] n[S]

IEEE Infocom 2007 10

INSERT Operation

// if S empty set min to x

// if x is smaller swap them

// increment size of S

// recursive call to either L[xh] or H

Only One Recursive CallOnly One Recursive Call


INSERT Operation

INSERT( x, S )





• min( L[xh] ) ← xl

L[0] L[1] L[255]. . .

H 4 bits

4 bits

Suppose w = 8 x = 0 0 0 1 1 1 1 1

xh xl


INSERT Operation

INSERT( x, S )






L[0] L[1] L[255]. . .

H 4 bits

4 bits

Suppose w = 8 x = 0 0 0 1 1 1 1 1

xh xl


INSERT Operation

INSERT( x, S )






L[1] 4 bits

Now w = 4 x = 1 1 1 1

xh xl


INSERT Operation

INSERT( x, S )






L[0] L[1] L[255]. . .

2 bits

2 bits

H

Now w = 4 x = 1 1 1 1

xh xl


INSERT Operation

INSERT( x, S )






L[0] L[1] L[255]. . .

H 2 bits

2 bits

Now w = 4 x = 1 1 1 1

xh xl


INSERT Operation

INSERT( x, S )






L[0] L[1] L[255]. . .

2 bits

2 bits

H

Now w = 4 x = 1 1 1 1

xh xl

Overall O(lg w) or O(lg lg u) timeOverall O(lg w) or O(lg lg u) time


EXTRACTMIN Operation

// if S empty, nothing to return// set return to current min

// decrement size of S

// recursive call to either H or L[mh]

// set new minimum and return value


EXTRACTMIN Operation

// if S empty, nothing to return// set return to current min

// decrement size of S

// recursive call to either H or L[mh]

// set new minimum and return value

Again Only One Recursive CallAgain Only One Recursive Call


Basic Idea of Pipelining

• vEB operations are recursively defined• Each operation only makes one recursive call at each

level of recursion• Each call goes down to a sub-tree with universe

defined by w/2 bits (then w/4 bits,

w/8 bits, etc)

• Therefore, intuitively canunroll into lg lg u steps

L[0] L[1] L[ -1]. . .

H w/2 bits

w/2 bits

sub-trees


Pipeline Structure

stage 1

op1 arg1

stage 2

op2 arg2

stage 3

op3 arg3

op4 arg4

input

: :

M1 = pointers to all subtrees of width w/2

output

min n

H L0 L1…

min n

H L0 L1…

min n

H L0 L1…

min n

H L0 L1…

stage 4





Main Issues

• Want to initiate new operation at every pipeline cycle(e.g. initiate an INSERT or EXTRACTMIN)

• Even if throughput is O(1),• Don’t want to wait lg lg u pipeline cycles to retrieve

data (e.g. want data in same pipeline cycle for EXTRACTMIN)

• But previous INSERT and EXTRACTMIN operation(s) may still be in pipeline

• Two pipeline stages may need to access same data• Need to resolve memory access orders


Basic Idea• Operations proceed

from top to bottom

• min[S] for the vEB tree rooted at that level is updated immediately as operations flow thru pipeline

• e.g. INSERT at Stage 1 will update min[S] immediately so next EXTRACTMIN at Stage 1 will retrieve correct min[S]

stage 1

op1 arg1

stage 2

op2 arg2

stage 3

op3 arg3

op4 arg4

input

: :

output

min n

H L0 L1…

min n

H L0 L1…

min n

H L0 L1…

min n

H L0 L1…

stage 4


Other Operations• In addition to INSERT and EXTRACTMIN, paper describes

• SUCCESSOR( x, S ) – Returns next element in S larger

than x in the universe U

• Can also define in a similar pipeline fashion

• PREDECESSOR( x, S ) – Returns next element in S smaller than x in the universe U

• EXTRACTMAX( S ) – Removes item with largest key

• To facilitate these operations, we also• Store max[S] for corresponding S at each pipeline stage• Provide another pipeline memory structure for tracking

“in-flight” SUCCESSOR and PREDECESSOR operations


Conclusions

• Pipelined van Emde Boas trees can achieve O(1) operations with O(lg lg u) pipelined stages

• Same O(1) time complexity as pipelined heaps, but exponentially fewer pipeline stages than O(lg n) required for pipelined heaps when u is a polynomial of n

• Can simultaneously support EXTRACTMIN and EXTRACTMAX, which is harder to do with heaps

• Can support other operations like SUCCESSOR and PREDECESSOR, which have potential application to various network problem instances

Thank You

pipelined van emde boas tree: algorithms, analysis, and applications hao wang and bill lin...

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