Financial Portfolio Management with Java on Steroids - JAX Finance 2016

Marcus Gründler | aixigo AG

Financial Portfolio Management on Steroids

About meMarcus Gründler@marcusgruendler

Head of Portfolio Management SystemsArchitect at aixigo AG, Germany

www.aixigo.de

JAX Finance 2016 - London

Agenda

• Financial portfolio management

• Hardware

• Java memory

• Programming patterns

• Scaling

What exactly is

Financial PortfolioManagement?

Portfolio Management

• Transactions, prices, securities

• Financial algorithms

• Historical analysis

• Time series

Portfolio Management

• No extreme low latency

• High data throughput (1 mio rec/sec)

• Low response times (100ms/10,000 rec)

• Very large datasets

Ishigh performancecomputingpossible withJava?

Yes ...

... read the fine print!

Matrix Sum23 101 2 34 88 120 4

44 12 234 211 112 189 11

33 1 86 201 3 11 22

65 32 62 22 34 15 67

43 178 105 138 192 38 41

11 58 35 25 27 16 21

Row major access

Matrix Sum

23 101 2 34 88 120 4

44 12 234 211 112 189 11

33 1 86 201 3 11 22

65 32 62 22 34 15 67

43 178 105 138 192 38 41

11 58 35 25 27 16 21

Column major access

Matrix Sum(10,000 x 10,000 elements)

ops/sec

ops/sec

ops/sec

Matrix Sum

Row major access

Column major access

Matrix Sum

Row major access

Column major access

Matrix Sum

Row major access

Column major access

Matrix Sum

Row major access

Column major access

Matrix Sum

Row major access

Column major access

Matrix Sum

Row major access

Column major access

Matrix Sum

Row major access

Column major access

Matrix Sum

Row major access

Column major access

Tool Support - JMH

• OpenJDK JMH (Java Microbenchmark Harness)

• Eliminates measurement (in)accuracy

• Statistically robust measurements

• Maven and Jenkins support

http://openjdk.java.net/projects/code-tools/jmh/

Memory Access

CPU

RAM

Cached Memory Access

CPU

RAM

Cache

Multi Level Caches

CPU

RAM

L1 L2 L3

Latency NumbersCPU cycles Time Size

L1 latency ~ 4-5 cycles 1.5 ns 32 KBL2 latency ~ 12 cycles 3.5 ns 256 KBL3 latency ~ 36 cycles 10.6 ns 8-40 MBRAM latency ~ 230 cycles 67.6 ns 256 GB2KB over 1GBit 20,000 ns1 MB from RAM 250,000 nsDisk seek 10,000,000 nsRoundtrip US-EU-US 150,000,000 nsIntel i7-4770 (Haswell), 3.4 GHz Sources:http://www.7-cpu.com/cpu/Haswell.html

http://norvig.com/21-days.html#answers

Latency NumbersCPU cycles Time Size

L1 latency ~ 4-5 cycles 1.5 ns 32 KBL2 latency ~ 12 cycles 3.5 ns 256 KBL3 latency ~ 36 cycles 10.6 ns 8-40 MBRAM latency ~ 230 cycles 67.6 ns 256 GB2KB over 1GBit 20,000 ns1 MB from RAM 250,000 nsDisk seek 10,000,000 nsRoundtrip US-EU-US 150,000,000 nsIntel i7-4770 (Haswell), 3.4 GHz Sources:http://www.7-cpu.com/cpu/Haswell.html

http://norvig.com/21-days.html#answers

Cache-oblivious Algorithms

• Optimized for minimal memory transfer

• All computation with L1 cache

• Cache-“oblivious“: no knowledge about

cache hierarchy

• Keeps CPU permanently „under pressure“

• Empowers cache prefetching

Plain Matrix Transpose

1 2 34 5 6

...

1 4...2 5

637

7

Cache-oblivious Transpose

Matrix Transpose(4096 x 4096 elements)

ops/sec

Matrix Transpose(4096 x 4096 elements)

Cores

ops/sec

• Memory access patterns matter

• Avoid main memory jumps

• Algorithms should support prefetching

How muchmemorydo weconsume?

How large is an object?

double = 8 byteDouble = ? byte

BigDecimal = ? byte

java.lang.Double


BigDecimal = ? byte

0 4 8 12 16 20 24 28

Padding8 bytealignment

Objectheader

„Mark word“• HashCode• Flags

Class pointer

Data

java.lang.Double


BigDecimal = ? byte

0 4 8 12 16 20 24 28

Objectheader


Class pointer

Data

double = 8 byteDouble = 24 byte

BigDecimal = ? byte

double array0 4 8 12 16 20 24 28


Class pointer

Data 0 Data 1Arraylength

...


BigDecimal = ? byte

java.lang.Double


BigDecimal = ? byte

BigDecimal0 4 8 12 16 20 24 28 32 36 40

ref to BigInteger

ref to int[ ]

Σ 40 byte

Σ 40 byte

Σ >16 byte>96 byte


BigDecimal = >96 byte

BigDecimal

Tool Support - JOL• OpenJDK tool – JOL (Java Object Layout)

• Insight into memory layout

• Heap dump analysis

• Exact memory usage

• Graphical layout visualization

• Maven module

http://openjdk.java.net/projects/code-tools/jol/

Tool Support - JOL

java -jar jol-cli.jar \internals java.math.BigDecimal

or

java –cp jol-cli.jar:my-own.jar \org.openjdk.jol.Main \internals foo.MyClass

Tool Support - JOL

java.math.BigDecimal object internals:OFFSET SIZE TYPE DESCRIPTION VALUE

0 12 (object header) N/A12 4 int BigDecimal.scale N/A16 8 long BigDecimal.intCompact N/A24 4 int BigDecimal.precision N/A28 4 BigInteger BigDecimal.intVal N/A32 4 String BigDecimal.stringCache N/A36 4 (loss due to the next object alignment)

Instance size: 40 bytes (estimated, the sample instance is not available)Space losses: 0 bytes internal + 4 bytes external = 4 bytes total

Data Locality

56 + 520 = 576 bytes 64 Bytes

Memory LayoutTransactionPlain[100]

TransactionCompact[100]

276 kB

• Keep data compact

• Think about data types

• Keep memory allocations low

• Keep garbage collection rate low

And...

Which patternsshould Iuse?

Tree Model

Flattened Data Model

Streaming Data Access

!𝑧𝑦𝑥

4

%𝑦𝑥

1%𝑥𝑥

3

2

Decoupled Algorithms

• Prefer primitives over classes

• Prefer arrays over object trees

• Process one array at a time

What aboutclusterreplication?

MVCC(Multi Version Concurrency Control)

V1

V2

V3

V4

View

1Vi

ew 1

+2Vi

ew 1

+2+3

View

1+2

+3+4

Compact Data

V1

V2

V3

V4

View

1Vi

ew 1

+2Vi

ew 1

+2+3

View

1+2

+3+4

Off heap RAMOn disk

From Disk to RAM

V1

V2

V3

V1 V2 V3

MappedByteBuffer

Data Distribution

Apache Kafka

Data Distribution

Data Distribution

Summary

• Large speedups through cache optimized

algorithms

• Memory layout is crucial

• Scale by cache replication

@marcusgruendler

Thankyou!

Financial Portfolio Management with Java on Steroids - JAX Finance 2016

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