Improving Bloom Filter Configuration for Lazy Transactional Memory

Improving Bloom Filter Configuration for Lazy Transactional Memory

Mark Jeffrey and J. Gregory SteffanECE, University of Toronto

November 10, 2011

Mark Jeffrey, Improving Bloom Filter Configuration for Lazy TM 2

Parallel Programming is Hard

T1

Rd(a)

Rd(b)

Wr(a)

T2

Rd(a)

Wr(c)

Rd(a)

T3

Rd(x)

Rd(a)

Tools offload some burden of managing data accesses:– Memory Race Replay– Atomicity Violation Survival– Transactional Memory– Speculative Optimizations

Many tools are using Bloom filters


Bloom Filter

• Bit-vector-based data structure [1970]– offers fast set operations– in exchange for some imprecision

• Recently used to compare memory accesses• With unconventional practices: Intersection

&

We show new practices are inefficient!(in theory and empirically)


Bloom Filters in Concurrency ToolsSystem Year ApplicationBulk 2006 Hardware TMBulkSC 2007 Memory ConsistencyHARD 2007 Race DetectionDeLorean 2008 Deterministic Race ReplaySoftSig 2008 Code Analysis/Optimization/DebugRingSTM 2008 Software TMSigRace 2009 Race DetectionColorSafe 2010 Atomicity ViolationInvalSTM 2010 Software TMAdapSig 2010 Software TMSvS 2011 Auto-protection of shared state

Our propositions will improve parallelism!


Tracking Address-Set Conflicts


Address-Sets

T1

Rd(a)

Rd(b)

Wr(a)

T2

Rd(a)

Wr(c)

Rd(a)

T3

Rd(x)

Rd(a)

Read Set:• memory locations read• RT1 = {a,b}

Write Set:• memory locations written• WT1 = {a}


Burden: Address-Set Conflicts

T1

Rd(a)

Rd(b)

Wr(a)

T2

Rd(a)

Wr(c)

Rd(a)

T3

Rd(x)

Rd(a)

Conflicts– address accesses are dependent– independence -> parallelism!– address conflicts -> no parallelism

Conflict Detection requires – read and write set comparison


Test address-sets for null-intersections

Detect conflicts at the end of a transaction

Lazy Conflict Detection

R1={a,c}W1={b}

T1 T2

Wr(b)--Rd(a)Rd(a)-

Rd(c)- -Rd(b)

?021 RW


Bloom Filters (BF)


Bloom Filter Background

• Bloom filter is a compact set representation– bit vector - much smaller than address space

x

h()

xS )BF(


Bloom Filter Background

y h()?)BF(Sy

{Yes, No}

Query for an address, y


Bloom Filter False Positives (FPs)

• Encode a large address space into a bit-vector – response to query is actually No or Maybe

• False Positives – when “maybe” is wrong

is y in ?

x y


Partitioned Bloom Filter

Insert an address, x:– k hash functions encode k bit indices to set

x

h1() h2() hk()…

…

xS )BF(


Probability of False Positives is well understood

Query for an address, y:

Partitioned Bloom Filter

y

h1() h2() hk()…

…

{Maybe, No}

?)BF(Sy


UnconventionalBloom Filter Null-Intersection Tests


Two existing approaches:1. build a Queue of Queries (QoQ)

2. combine queries into distinct Bloom filter– replace many queries with 1 intersection!

Bloom Filter Null-Intersection Tests

a2a3a4a5 a1 ?

?


Do two sets share any elements?

Partitioned BF Intersection

…

?021 SS

…& …

{Disjoint, Maybe Overlap}


Any asserted bits indicate set overlap

Unpartitioned BF Intersection

…

?021 SS

…& …



Imprecision in BF Intersection

• Bloom filter was intended for fast Querying

• Recent systems use filter for Intersection– Imprecision can produce False Set-Overlaps (FSO)– We are the first to study Bloom filter FSOs– Our goal is to

Understand and improve Bloom filter intersection


Important Questions

When using BFs for testing null-intersection1. How do BF Intersection and QoQ compare?– theoretical study [SPAA ‘11]

2. Can we compromise? – new Bloom filter design

3. Does theory work in practice? – empirical study


1. How do BF Intersection and QoQ compare?

Bloom Filters for Null-Intersection Tests


Definitions

sets access addressdisjoint ,BA

bits m


Definitions

h1() h2() hk()……

partitions k

sets access addressdisjoint ,BA

bits m


• Unpartitioned BF Intersection

• Partitioned BF Intersection

• Queue of BF Queries

BAkmUnpartp

2111

Probability of FSO [SPAA ‘11]h1 h2 hk…

h1 h2 hk…

kBA

mk

Partp 11

BkA

mk

QoQp 1111b2b3b4b5 b1 ϵ?


For any length m, and k > 1 hash functions,

nedUnpartitiodPartitioneQoQ ppp

Queue of Queries gives the fewest false conflictsPartitioned intersection improves on Unpartitioned

Comparing FSOs [SPAA ’11]

b2b3b4 b1 ϵ?

h1 hk… h1 hk…


2. Can we compromise? A new Bloom filter design

Bloom Filters for Null-Intersection Tests


Batch-of-Bloom-filters (BoB)

…

x hpre

x

…

h1 hk…

…

…h1 hk

xS )BoB(

…

…h1 hk

bSSSS 21

)BF( 1S )BF( 2S )BF( bS)BF(S



BoB Intersection

&…

…

……

…

…

…

?021 SS

BoB: compromise between QoQ and Intersect


3. Does theory work in practice?Bloom Filters for Null-Intersection Tests


Methodology

• Augment RingSTM with alternate BF configs[Spear et al. SPAA ’08]– unpartitioned Bloom filter intersection

• Stress BF configurations using STAMP bench

• 8-core Intel Xeon with SSE2 ISA– 32-bit Linux 2.6.32-5-686


QoQ, BoB, part. intersect outperform baseline

Performance Results: LabyrinthExecution Time Aborts

21% Speedup

Better


Querying overhead counteracts reduced aborts

Performance Results: Kmeans-low

Better

>25% slowdown

Execution Time Aborts


Conclusion


Conclusion

Conflict detection often applies Bloom filters– for fast set operations: y ϵ S and S1∩S2

– unconventionally using BFs for null-intersection

Our recommendations (from theory & practice)1. strongly consider querying before intersection2. in hardware, consider intersecting BoBs3. build adaptive systems for application behaviors

Improving Bloom Filter Configuration for Lazy Transactional Memory

Thank [email protected]

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Improving Bloom Filter Configuration for Lazy Transactional Memory