1 UNIVERSITY OF MASSACHUSETTS, AMHERST • School of Computer Science

PREDATOR: Predictive False Sharing Detection

Tongping Liu*, Chen Tian, Ziang Hu, Emery Berger*

*University of Massachusetts AmherstHuawei US Research Center

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Parallelism: Expectation is Awesome

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Number of threads

Expectation

Parallel Program

int count[8];int W; void increment(int S) { for(in=S; in<S+W; in++) for(j=0; j<1M; j++) count[in]++;}

int main(int THREADS) { W=8/THREADS; for(i=0; i<8; i+=W) spawn(increment,i);}

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Number of threads

False sharing slows the program by 13X

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Parallel Program Expectation

Reality

Parallelism: Reality is Awful

int count[8];int W; void increment(int S) { for(in=S; in<S+W; in++) for(j=0; j<1M; j++) count[in]++;}

int main(int THREADS) { W=8/THREADS; for(i=0; i<8; i+=W) spawn(increment,i);}

False sharing

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False Sharing in Real Applications

False sharing slows MySQL by 50%

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Cache Line

False Sharing vs. True Sharing

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Task 3Task 1

Task 2 Task 4

False Sharing

Task 1

TrueSharing

Task 2

False Sharing vs. True Sharing

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Resource Contention at Cache Line Level

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Thread 1

Main Memory

Core 1

Thread 2

Core 2

Cache

Cache

Invalidate

Cache line: basic unit of data transfer

False Sharing Causes Performance Problems

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Thread 1 Thread 2

Cache

Cache

Invalidate

Interleaved accesses cause cache invalidations

Main Memory

Core 1 Core 2

False Sharing Causes Performance Problems

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me = 1;you = 1; // globals

me = new Foo;you = new Bar; // heap

class X { int me; int you;}; // fields

array[me] = 12;array[you] = 13; // array indices

False Sharing is Everywhere

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False Sharing is Hard to Diagnose

Multiple experts worked together to diagnose MySQL scalability issue (1.5M LOC)

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Problems of Existing Tools

• No precise information/false positives–WIBA’09, VEE’11, EuroSys’13, SC’13

• Accurate & Precise– OOPSLA’11 ( Cannot detect read-write

FS)Shared problem: only detect observed false sharing

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Task 1

Task 2

Cache

Cache

Invalidate

Main Memory

Core 1 Core 2

False Sharing Causes Performance Problems

Find cache lines with many cache invalidations

Interleaved accesses

Cache invalidations

Performance problems

Detect false sharing causing performance problems

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Find Lines with Many Invalidations

. . . . . . .

……

Track cache invalidations on each cache line

Memory: Global, Heap

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Track Cache Invalidations

• Hardware-based approach– Needs hardware

support– No portability

• Simulation-based approach– Needs hardware info

such as cache hierarchy, cache capacity

– Very slow

• Conservative Assumptions– Each thread runs on a

different core with its private cache.

– Infinite cache capacity.

PREDATOR: based on memory access history of each cache line

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Track Cache Invalidations

r w r ww r w r

T1 T2

0

# of invalidations

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Time

30 0 0 0T2 r T1 rT2 w

Each Entry: { Thread ID, Access Type}

T2 w 0 0T1 wT2 w 0 0T1 r

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PREDATOR Components

Compiler Instrumenta

tion

Runtime System

Instruments every memory read/write

access

Collects memory accesses and reports

false sharing

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Detect Problems Correctly & Precisely

• Correctly: –No false

alarms

Task 3Task 1

Task 2 Task 4

False Sharing

Task 1

TrueSharing

Task 2

Track memory accesses on each word

• Precisely– Global variables–Heap objects: pinpoint the line of memory

allocation

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PREDATOR’s Report

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Why do we need prediction?

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Necessity of False Sharing Prediction

Thread 1 Thread 2

Cache line 1 Cache line 2

Cache line 1 Cache line 2

False Sharin

g

Cache line 1

False Sharin

g

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Properties Affecting False Sharing Occurrence

32-bit platform 64-bit platformDifferent memory allocatorDifferent compiler or optimizationDifferent allocation order by changing the

code, e.g., printf

• Change of memory layout

• Run on hardware with different cache line size

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Example of False Sharing Sensitivity

Offset = 0

Offset = 8

Offset = 56

……

Memory

Colors represent threads

Cache line size = 64 bytes

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Offse

t=0

Offse

t=8

Offse

t=16

Offse

t=24

Offse

t=32

Offse

t=40

Offse

t=48

Offse

t=56

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1

2

3

4

5

6

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PREDATOR predicts false sharing

problems without occurrence

Example of False Sharing Sensitivity

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Prediction Based on Virtual Cache Lines

Thread 1 Thread 2

Cache line 1 Cache line 2

Virtual cache line 1 Virtual cache line 2

False Sharin

g

Virtual cache line 1

False Sharin

g

Real case

Prediction 1

Prediction 2

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d YX

(sz-d)/2 (sz-d)/2

Tracked virtual line

Non-tracked virtual lines

Track Invalidations on Virtual Cache Lines

d < the cache line size - sz(X, Y) from different threads && one of

them is write

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Benchmark Results

Benchmarks Unknown Problem

Without Prediction

With Prediction Improvement

Histogram ✔ ✔ ✔ 46%

Linear_regression ✔ 1207%

Reverse_index ✔ ✔ 0.09%

Word_count ✔ ✔ 0.14%

Streamcluster-1 ✔ ✔ ✔ 4.77%

Streamcluster-2 ✔ ✔ 7.52%

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Real Applications Results

• MySQL– Problem: False sharing occurs when

different threads update the shared bitmap simultaneously.

– Performance improves 180% after fixes.

• Boost library:– Problem: “there will be 16 spinlocks per

cache line”– Performance improves about 100%.

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Performance Overhead of PREDATOR

Phoenix

histogra

m

kmea

ns

linear_

regressi

on

matrix_

multiply pca

revers

e_index

string_m

atch

word_c

ount

PARSEC

blacks

choles

bodytrac

k

dedup

ferret

fluidanimate

strea

mcluste

r

swap

tions x2

64

RealApplica

tionsag

etBoost

Mem

cach

ed

MyS

QL

pbzip2

pfscan

AVERAGE

0

3

6

9

12

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Execution Time Overhead

Original

PREDATOR-NP

PREDATOR

Nor

mal

ized

Runti

me

2326

5.6X

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Compiler Instrumenta

tionRuntime System

Thread 1

Thread 2

Cache

Cache

Invalidate

Main Memory

Core 1 Core 2

Precise report

Thread 1 Thread 2

Cache line 1 Cache line 2

Virtual cache line 1Virtual cache line 2

False Sharin

g

Virtual cache line 1

False Sharin

g

Real case

Prediction 1

Prediction 2

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False Sharing is Hard to Diagnose

Multiple experts worked together to diagnose MySQL scalability issue (1.5M LOC)

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Detailed Prediction Algorithm

1. Find suspected cache lines

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Detailed Prediction Algorithm

1. Find suspected cache lines

2. Track detailed memory accesses

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Detailed Prediction Algorithm

1. Find suspected cache lines

2. Track detailed memory accesses

3. Predict based on hot accesses

YX

d d < sz && (X, Y) from different

threads, potential false sharing

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4: Tracking Cache Invalidations on the Virtual Line

d YX

(sz-d)/2 (sz-d)/2

Tracked virtual line

Non-tracked virtual lines