ISMM 2004

Mostly Concurrent Compaction for Mark-Sweep GC

Yoav Ossia, Ori Ben-Yitzhak, Marc SegalIBM Haifa Research Lab. Israel

IBM Labs in Haifa

ISMM 2004

Prologue: Commercial Multi-tier Applications

Clients (or load injectors) Sending requests to Server

Web Server – using application Application and database

Transaction – a request cycle

Performance requirements Restricted resource utilization on the server

(e.g., CPU utilization below 50%)Throughput – Transactions per secondAverage transaction response time

ClientClient Client

Server

Application

DB

IBM Labs in Haifa

ISMM 2004

Prologue: Commercial Multi-tier Applications

Clients (or load injectors) Sending requests to Server

Web Server – using application Application and database

Transaction – a (set of) request cycle(s)

Performance requirements Restricted resource utilization on the server

(e.g., CPU utilization below 50%)Throughput – Transactions per secondAverage transaction response time

ClientClient Client

Server

Application

DB

IBM Labs in Haifa

ISMM 2004

Prologue: Commercial Multi-tier Applications

Clients (or load injectors) Sending requests to Server

Web Server – using application Application and database

Transaction – a (set of) request cycle(s)

Performance requirements Throughput – Transactions per second Average Transaction Response Time At restricted CPU utilization

(e.g., below 50%)

ClientClient Client

Server

Application

DB

IBM Labs in Haifa

ISMM 2004

Prologue: Observations

GC share is negligible (every 20 sec.) in all examples

1. Long compaction occurs

2. Switch from 500 ms Stop-The-World (STW) GC, to 250 ms mostly concurrent GC

1

2

IBM Labs in Haifa

ISMM 2004

Prologue: Observations

GC share is negligible (every 20 sec.) in all examples

1. Long compaction occurs

2. Switch from 500 ms Stop-The-World (STW) GC, to 250 ms mostly concurrent GC

1

2

Ave

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e R

es

po

ns

e t

ime

Time

1.0

2.0

Ave

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ime

Time

1.0

2.0

IBM Labs in Haifa

ISMM 2004

Prologue: Insights

Average response time overreacts To shorter GC pause time To occasional compaction

Why? Longer GC pause times create a queue

of transactions Queue persist long after the GC

Transaction timeout creates additional work

Conclusion: “some” pause time is acceptable but extras should be avoided

IBM Labs in Haifa

ISMM 2004

Prologue: The Clinic Analogy

Receptionist handles the incoming patient in 5 minutes, the physician in 10 minutes. Appointments are scheduled every 10 minutes

An appointmentlasts 15 minutes :=)

But if the receptionist takes a long break…

When he returns,appointmentslast ~50 minutes :=(

Only after a while, with hard work (of both receptionist and physician), QoS may be restored

IBM Labs in Haifa

ISMM 2004

Prologue: The Physician Analogy

Receptionist handles the incoming patient in 5 minutes, the physician in 10 minutes. Appointments are scheduled every 10 minutes

An appointmentlasts 15 minutes :=)

But if the receptionist takes a long break…

When he returns,appointmentslast ~50 minutes :=(

Only after a while, with hard work (of both receptionist and physician), QoS may be restored

IBM Labs in Haifa

ISMM 2004

Prologue: The Physician Analogy

Receptionist handles the incoming patient in 5 minutes, the physician in 10 minutes. Appointments are scheduled every 10 minutes

An appointmentlasts 15 minutes :=)

But if the receptionist takes a long break…

When he returns,appointmentslast ~50 minutes :=(

Only after a while, with hard work (of both receptionist and physician), QoS may be restored

IBM Labs in Haifa

ISMM 2004

Prologue: The Physician Analogy

Receptionist handles the incoming patient in 5 minutes, the physician in 10 minutes. Appointments are scheduled every 10 minutes

An appointmentlasts 15 minutes :=)

But if the receptionist takes a long break…

When he returns,appointmentslast ~50 minutes :=(

Only after a while, with hard work (of both receptionist and physician), QoS may be restored

IBM Labs in Haifa

ISMM 2004

Prologue: The Physician Analogy

Receptionist handles the incoming patient in 5 minutes, the physician in 10 minutes. Appointments are scheduled every 10 minutes

An appointmentlasts 15 minutes :=)

But if the receptionist takes a long break…

When he returns,appointmentslast ~50 minutes :=(

Only after a while, with hard work (of both receptionist and physician), QoS may be restored

IBM Labs in Haifa

ISMM 2004

Outline

Prologue – Commercial applications Mark Sweep (and Compact) GC Mostly Concurrent Compaction

Overview The generic algorithm Our implementation

Results Related work, conclusions and future directions

IBM Labs in Haifa

ISMM 2004

Mark-Sweep (and Compact) GC

Used by many modern memory management systems Either for the entire heap, or for parts (e.g., the old objects area of

generational GC) Good performance on large server heaps Usually activated by an allocation request, when the heap is full

Mark - tags all objects that are reachable from roots

Sweep – Reclaims unmarked objects into list of free chunks

Result may be unsatisfactory (fragmentation) Compact – packs together all live objects, creating a large free

chunk

IBM Labs in Haifa

ISMM 2004

Characteristics of Compaction Includes two activities

Move of live objects Fix-up of all references (in objects and roots) to new locations

Advantages Eliminates fragmentation and enables (better, faster) allocation Better cache locality

Disadvantages Very expensive. Typically takes much more time than Mark- Sweep Done in Stop-The-World (STW) mode Severe impact on pause time

Avoided as much as possible, but is occasionally inevitable Compaction is the weak point of Mark Sweep GC (pause time)

IBM Labs in Haifa

ISMM 2004

Outline

Prologue – Commercial applications Mark Sweep (and Compact) GC Mostly Concurrent Compaction

Overview The generic algorithm Our implementation

Results Conclusions and future directions

IBM Labs in Haifa

ISMM 2004

Mostly Concurrent Compaction - Overview

Our Goal: restrict the effect of compaction on pause time Typically to less than mark time For average response time QoS, critical code (e.g., heartbeat)

Method – partial Move in STW, concurrent Fix-up Reduce the pause time of the Move phase, by using

incremental compaction Select the compacted part according to sweep results To optimize compaction impact and control pause time effect

Execute the fix-up phase after the move, when application threads are resumed Correctness preserved by page-protecting the unfixed objects

from application threads access

IBM Labs in Haifa

ISMM 2004

Assumptions About the Environment

Memory management module Uses Mark Sweep GC Has a move operation – able to pack objects in the heap Supplies fix-up logic - knows the new location of an object by

the original address Operating system services

Map2 - maps physical memory into two virtual address ranges, or views

ProtN - protects a virtual address range of pages from read and write access.

Unprot - removes the protection from specified page(s) Execute a Trap routine upon page access violation

IBM Labs in Haifa

ISMM 2004

Outline

Prologue – Commercial applications Mark Sweep (and Compact) GC Mostly Concurrent Compaction

Overview The generic algorithm Our implementation

Results Conclusions and future directions

IBM Labs in Haifa

ISMM 2004

The Generic Algorithm - Details

At Application initialization Use Map2 to create the application view and the fix-up view

of the heap Calculating the areas to compact

Motivation: optimal quality at restricted move time Heap is divided into small sections (e.g., 100 sections) Gather object layout information during sweep

Per section: free space, number of small free chunks, etc. Select the optimal set of sections for compaction Using configurable policy/heuristic

IBM Labs in Haifa

ISMM 2004

The Generic Algorithm - Details (cont.)

Move phase Objects are compacted within the selected areas

Fix-up of root references Prepare the heap pages

Page protect all heap pages that contain objects Reset state of all pages (that contain objects) to Unfixed Rest of (“free”) pages are set to Fixed

Resume execution of application threads

IBM Labs in Haifa

ISMM 2004

The Generic Algorithm - Concurrent Fix-up (method)

Constrains All Unfixed pages are fixed, and only once A page starts as Unfixed (and protected) , then Busy, and

finally Fixed (and unprotected) Application threads access only Fixed pages

Fix-up of page (Exclusive Fix) Done only by a thread that managed to change the page’s

state from Unfixed to Busy All the (protected) page’s references are fixed. Page is

accessed through the (unprotected) Fix-up view Protection is lifted Page state is set to Fixed

IBM Labs in Haifa

ISMM 2004

The Generic Algorithm - Concurrent fix-up (who/how)

Concurrent Fixing – fix-up that is initiated by the collector All concurrency flavors are possible Concurrent Fixers scan the heap, and try to Exclusively Fix

each page Failure is OK; someone else did (or is doing) the fix-up

Trapped Fixing – forced fix-up Access violating application thread becomes a Trapped Fixer Executes a trap routine that attempts to Exclusively Fix the

accessed page If fails, thread must wait till page becomes Fixed

Completed when Concurrent Fixing exhaust the heap

IBM Labs in Haifa

ISMM 2004

Outline

Prologue – Commercial applications Mark Sweep (and Compact) GC Mostly Concurrent Compaction

Overview The generic algorithm Our implementation

Results Conclusions and future directions

IBM Labs in Haifa

ISMM 2004

Our Implementation

Implemented for Java, on top of the IBM J9 JVM Using Mark Sweep GC on the entire heap Reusing parallel move code and fix-up logic of J9’s compactor

Configurable fix-up unit, bigger than the OS page size Fix-up more than an OS page on each trap Fewer access violations (more “hot” memory fixed each time)

Reduces the relative cost of traps Longer trapped fixing

We found that a significant unit size increase can be tolerated

Concurrent fixing by incremental work of the Java threads For each X KB of allocation, fix-up X*F KB of heap space

IBM Labs in Haifa

ISMM 2004

Outline

Prologue – Commercial applications Mark Sweep (and Compact) GC Mostly Concurrent Compaction

Overview The generic algorithm Our implementation

Results Conclusions and future directions

IBM Labs in Haifa

ISMM 2004

Testing Environment Red Hat Linux OS Pentium 4 Intel uniprocessor and a 4-way, Intel Xeon MP

processors, server Benchmarks: SPECjbb2000, Health (from Java-olden suite)

and SPECjvm98

Compaction triggered every N GCs N=10 for SPECjvm98, 15 for SPECjbb, and 1 for Health

No compact (Base) compared to compact with three area selection heuristics: Dark Matter reduction (DM) Creating Bigger Free chunks (BF) Round-Robin (RR)

IBM Labs in Haifa

ISMM 2004

Results : Throughput and Pause Time (Highlights)

Minor effect on pause time Area selection heuristics matters, and should not be

hard-coded

Benchmark Pause time contribution

relative to mark

Throughput improvement

Best area selection heuristic

SPECjvm up to 30%Improves most benchmarks(2% to 10%)

Varies(round-robin usually inferior)

Health up to 15% Base needs twice the heap to complete

Dark matter reduction

SPECjbb up to 15% No improvement round-robin is inferior

IBM Labs in Haifa

ISMM 2004

Results: Overall Costs of Concurrent Fix-up INCR-C - our Mostly Concurrent incremental compactor to

INCR-STW - same incremental move with STW fix-up FULL-STW - full heap move with STW fix-up

STWinc pause time contribution is 3 times the move timeNo throughput gain over our compactor

STWfull has very large pause time increase Compaction time is up to ten times the mark time Significant throughput gain with Health, some gain with SPECjvm

Concurrent fix-up is better than STW fix-up, for incremental compaction

Partial (but “smart”) compaction may be more effective than full compaction

Collector Pause time additionrelative to mark

Throughput gain over MCincr

INCR-C up to 1/3

INCR-STW up to 3 times No throughput gain

FULL-STW up to 10 times Health: significant, SPECjvm: some

IBM Labs in Haifa

ISMM 2004

Results: Cost of Access Violations

Concern: recently, page protection techniques became relatively inefficient, due to increase in computational speed

SPECjbb costs of Trapped fix-up

Conclusion: For concurrent fix-up, bigger fix-up units (64 KB-256 KB) are acceptable, and justify the use of page protection techniques

32 65 128 256Fix-up unit size (KB)

0

0.04

0.08

0.12

0.16

0.2

Total cost of Trapped fix-up (ms)

32 65 128 256Fix-up unit size (KB)

0

20

40

60

80

100

Relative cost of access violation (%)

IBM Labs in Haifa

ISMM 2004

Results: Java Mutator Utilization

Concern: Trapped fix-up cannot be controlled. If most pages are accessed all the time, the Java application, right

after STW, will practically do nothing but Trapped fix-up We measured the portion of time spent on trapped fix-up in first

450 ms

Acceptable Java utilization Reasonable Java utilization

after 50..100 ms

With 256 KB fix-up unit results are even better

SPECjbb’s Java utilizationin first 100 ms improves from 16% to 48%

1030

5070

90110

130150

170190

250350

450

Time from resuming the application (ms)

0

10

20

30

40

50

60

70

80

90

100

Trap

ped

fix-u

p pa

rt (%

)

jess

mtrt

javac

db

Jack

health

SPECjbb

IBM Labs in Haifa

ISMM 2004

Results: Java Mutator Utilization

Concern: Trapped fix-up cannot be controlled. If most pages are accessed all the time, the Java application, right

after STW, will practically do nothing but fix-up We measured the portion of time spent on trapped fix-up in first

450 ms

Acceptable Java utilization Reasonable Java utilization

after 50..100 ms With 256 KB fix-up unit

results are even betterSPECjbb’s Java utilization

in first 100 ms improves from 16% to 48%

1030

5070

90110

130150

170190

250350

450

Time from resuming the application (ms)

0

10

20

30

40

50

60

70

80

90

100

Trap

ped

fix-u

p pa

rt (

%)

jess

mtrt

javac

db

SPEC256

health

SPECjbb

IBM Labs in Haifa

ISMM 2004

Outline

Prologue – Commercial applications Mark Sweep (and Compact) GC Mostly Concurrent Compaction

Overview The generic algorithm Our implementation

Results Related work, conclusions and future directions

IBM Labs in Haifa

ISMM 2004

Related Work

Compaction techniques Jonkers, Morris - The threaded algorithm. 1978, 1979 Flood et al - Parallel garbage collection for shared memory

multiprocessors. 2001 Sachindran and Moss - Mark Copy: Fast copying GC with less

space overhead. 2003 Abuaiadh et al - An efficient parallel heap compaction algorithm.

2004

Incremental compaction Lang and Dupont - Incremental incrementally compacting garbage

collection. 1987 Ben-Yitzhak et al. - An algorithm for parallel incremental

compaction. 2002

IBM Labs in Haifa

ISMM 2004

Related Work (cont.)

Concurrent Copying collectors Baker - List processing in real-time on a serial computer. 1978 Brooks - Trading data space for reduced time and code space. 1984 Appel et al. - Real-time concurrent collection on stock

multiprocessors. 1988

Fully concurrent compaction Larose and Feeley - A compacting incremental collector and its

performance...1998 Bacon et al. - Controlling fragmentation and space consumption in

the metronome. 2003

Use of page protection Appel et al. - Virtual memory primitives for user programs.

1991

IBM Labs in Haifa

ISMM 2004

Conclusions

A solution is proposed for bounding the pause time effect of compaction

Mostly concurrent compaction: A generic solution suitable for Mark Sweep, and other GCs Method – partial Move in STW, concurrent Fix-up

A Java implementation is presented, on top of IBM J9 JVM Minor pause time hit (less than 1/3 of the mark time) Highly efficient - No significant hit due to concurrent

fix-up Improved performance with most benchmarks

IBM Labs in Haifa

ISMM 2004

Future Directions

Explore adaptive and sophisticated methods for: Triggering of the mostly concurrent compaction Choosing an optimal policy for selecting the parts to

compact Minimize the costs of Trapped Fix-up, by performing

“proactive” concurrent fix-up Fix the predicted next locations of access violations, rather

than performing sequential pass of heap

IBM Labs in Haifa

ISMM 2004

End

IBM Labs in Haifa

ISMM 2004

Java Mutator Utilization – The Mark Perspective

1030

5070

90110

130150

170190

250350

450

Time from resuming the application (ms)

0

10

20

30

40

50

60

70

80

90

100

Trap

ped

fix-u

p pa

rt (

%)

jess

mtrt

javac

db

SPEC256

health

SPECjbb

2000…