hhvm: efficient and scalable php/hack execution / guilherme ottoni (facebook)

HHVM: Efficient and Scalable PHP/Hack Execution

Guilherme OttoniHHVM TeamFacebookNovember 2016

Speaker Intro

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• Background in compilers, performance analysis and optimizations

• 15+ years working in these areas• Spent last 5.5 years pushing the

limits of PHP performance at Facebook

Motivation: Why PHP?• Many claim it’s a poorly designed language• But it’s pretty successful and widely used, especially

for server-side web development• Biggest strengths:– Integration with web servers– Rapid development cycle

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FAST

Motivation: Why PHP?

• Top websites [http://www.alexa.com/topsites]:1. Google2. Youtube3. Facebook4. Baidu5. Wikipedia6. Yahoo

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PHP

Motivation: Why HHVM?• High load requires high

performance

• Standard PHP – Not very efficient– Interpreter-based– Only industry-strength

implementation of PHP that existed

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• HHVM’s goals:1. Improve performance of PHP execution2. Compatibility with Standard PHP

More Efficient… By How Much?

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HHVM released to production

HipHopCompiler

Motivation: Why HHVM?

• Top websites [http://www.alexa.com/topsites]:1. Google2. Youtube3. Facebook4. Baidu5. Wikipedia6. Yahoo

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HHVM

• Many other adopters, e.g. Box, Etsy, Slack, Wordpress

• Wikipedia’s CPU usage dropped by 6x when it switched to HHVM[http://hhvm.com/blog/7205/wikipedia-on-hhvm]

Overview of HHVM Pipeline

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PHP AST

Optimize

Parser

HHBC

BytecodeEmitter

Optimize,Type InferenceAh

ead

of T

ime

HHBC HHIR

Optimize

x86Vasm

Optimize

Runt

ime

Challenges for PHP Performance

1. Lack of static type information2. Huge amounts of code

a. JIT speedb. Code locality

3. Reference counting

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Challenge #1: Dynamic Typing

• HHVM’s basic principle to achieve performance: operate on type-specialized code

• Ahead-of-time type inference– AST-based– Bytecode-based

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HHBC

Optimize,Type Inference

Challenge #1: Dynamic Typing

• Runtime type specialization1. Tracelet JIT• Inspect live types & JIT small code blocks as you go

2. Profile-driven Region JIT• Collect type information in the beginning, then

later compile larger code regions

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$elem: uncount; stk: dbl

114: CGetL 4116: CGetL2 2118: Add119: SetL 2121: PopC


$sum: dbl ; $elem: int$sum: dbl ; $elem: dbl

$sum: int ; $elem: dbl114: CGetL 4116: CGetL2 2118: Add119: SetL 2121: PopC

Example

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81: CGetM <L:0 EL:3>

94: SetL 4 96: PopC 97: Int 0 106: CGetL2 4 108: Gt 109: JmpZ 13 (122)


71: CGetL 1 73: CGetL2 3 75: Lt 76: JmpZ 55 (131)

122: IncDecL 3 PreInc125: PopC126: Jmp -55

function addPositive($arr, $n) { $sum = 0; for ($i = 0; $i < $n; $i++) { $elem = $arr[$i]; if ($elem > 0) { $sum = $sum + $elem; } } return $sum;}

94: SetL 4 96: PopC 97: Int 0106: CGetL2 4108: Gt109: JmpZ 13 (122)

$elem: uncount; stk: int

$i: int ; $n: int

$arr: array ; $i: int

$sum: int ; $elem: int

$i: int

$sum: dbl ; $elem: int




$sum: dbl ; $elem: dbl$sum: int ; $elem: dbl114: CGetL 4116: CGetL2 2118: Add119: SetL 2121: PopC

Example

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94: SetL 4 96: PopC 97: Int 0 106: CGetL2 4 108: Gt 109: JmpZ 13 (122)






$elem: uncount; stk: int

$i: int ; $n: int


$sum: int ; $elem: int

$i: int


$sum: dbl ; $elem: dbl

Example

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$i: int ; $n: int


$i: int


$sum: dbl

Example

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$i: int ; $n: int

$arr: array

Challenge #2: Huge Code Size

• HHVM dynamic (JITed) code: ~300 MB• HHVM static code: ~130 MB

• Intel IvyBridge cache sizes:– L1 i-cache: 32 KB / core– LLC: 25 MB (shared)– L1 I-TLB: 16.5 MB

• 8 x 2 MB = 16 MB• 128 x 4 KB = 512 KB

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Challenge #2a: JIT Speed

• Custom JIT compiler– E.g. LLVM isn’t appropriate

• Translation Cache (TC)– Reuse machine code across requests

• Only perform more expensive code optimizations when there’s a big performance impact on the generated code

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Challenge #2b: Code Locality1. Very selective function inlining in the JIT

2. Profile-guided JITed code layout

3. Hot/cold splitting of JITed code

4. Highly tuned runtime helpers (even hand-written in assembly)

5. Profile-guided binary layout for the C++ written runtime, using HFSort tool: https://github.com/facebook/hhvm/tree/master/hphp/tools/hfsort

6. Selective use of Intel x86’s huge (2 MB) pages to reduce I-TLB misses

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Challenge #3: Reference Counting• PHP uses reference counting for memory management

– And it’s visible at the language level:• Precise object destruction• Copy-on-write of arrays

• Reference-counting optimization in the JIT– Very complex and expensive optimization– 5% performance impact

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SetL 4 07: t4:Int = LdStack<Int,0> t3:StkPtr 09: StLoc<4> t1:FramePtr, t4:Int 10: IncRef t4:Str ...PopC 11: DecRef t4:Str ...

count=3 object

Challenge #3: Reference Counting

• Ongoing effort to move to a tracing garbage collector– Currently used to collect cycles

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More Than Just Performance: Hack Language Support

• PHP dialect with extended type annotations, among other language features

• Gradual typing to allow gradual migration of large code bases

• Powerful static type checker

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Summary

• High-load websites need high-performance software• PHP is very popular and used to build many large-scale

websites• Many challenges to efficiently execute PHP• A lot of effort put into building and optimizing HHVM

• HHVM is open source (https://github.com/facebook/hhvm)

• And it also supports the Hack language (http://hacklang.org/)

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Questions?

hhvm: efficient and scalable php/hack execution / guilherme ottoni (facebook)

Engineering