The HipHop Virtual Machine (HHVM) is the PHP and Hack execution engine developed at Facebook. HHVM is the fastest existing PHP/Hack engine, and it powers not only Facebook's servers, but also Wikipedia, Baidu, and many other sites across the web. In this talk, we'll give an overview of the design of HHVM, with particular focus on its JIT compiler. I'll share some of our experiences building this high performance system, as well as some of the key challenges that we've faced to improve PHP performance.

2. HHVM: Efficient and Scalable
PHP/Hack Execution
Guilherme Ottoni
HHVM Team
Facebook
November 2016

3. Speaker Intro
3
• 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

4. 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
4
FAST

5. Motivation: Why PHP?
• Top websites [http://www.alexa.com/topsites]:
1. Google
2. Youtube
3. Facebook
4. Baidu
5. Wikipedia
6. Yahoo
5
PHP

6. Motivation: Why HHVM?
• High load requires high
performance
• Standard PHP
– Not very efficient
– Interpreter-based
– Only industry-strength
implementation of PHP
that existed
6
• HHVM’s goals:
1. Improve performance of PHP execution
2. Compatibility with Standard PHP

7. 0
2
4
6
8
10
12
14
16
18
20
22
24
26
28PerformanceRelativetoPHP5.1running
facebook.com
More Efficient… By How Much?
7
HHVM released to
production
HipHop
Compiler

8. Motivation: Why HHVM?
• Top websites [http://www.alexa.com/topsites]:
1. Google
2. Youtube
3. Facebook
4. Baidu
5. Wikipedia
6. Yahoo
8
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]

9. Overview of HHVM Pipeline
9
PHP AST
Optimize
Parser
HHBC
Bytecode
Emitter
Optimize,
Type Inference
AheadofTime
HHBC HHIR
Optimize
x86Vasm
Optimize
Runtime

10. Challenges for PHP Performance
1. Lack of static type information
2. Huge amounts of code
a. JIT speed
b. Code locality
3. Reference counting
10

11. 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
11
HHBC
Optimize,
Type Inference

12. Challenge #1: Dynamic Typing
• Runtime type specialization
1. 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
12

13. $elem: uncount; stk: dbl
114: CGetL 4
116: CGetL2 2
118: Add
119: SetL 2
121: PopC
114: CGetL 4
116: CGetL2 2
118: Add
119: SetL 2
121: PopC
$sum: dbl ; $elem: int
$sum: dbl ; $elem: dbl
$sum: int ; $elem: dbl
114: CGetL 4
116: CGetL2 2
118: Add
119: SetL 2
121: PopC
Example
13
81: CGetM <L:0 EL:3>
94: SetL 4
96: PopC
97: Int 0
106: CGetL2 4
108: Gt
109: JmpZ 13 (122)
114: CGetL 4
116: CGetL2 2
118: Add
119: SetL 2
121: PopC
71: CGetL 1
73: CGetL2 3
75: Lt
76: JmpZ 55 (131)
122: IncDecL 3 PreInc
125: PopC
126: Jmp -55
function addPositive($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 0
106: CGetL2 4
108: Gt
109: JmpZ 13 (122)
$elem: uncount; stk: int
$i: int ; $n: int
$arr: array ; $i: int
$sum: int ; $elem: int
$i: int

14. $sum: dbl ; $elem: int
$elem: uncount; stk: dbl
114: CGetL 4
116: CGetL2 2
118: Add
119: SetL 2
121: PopC
114: CGetL 4
116: CGetL2 2
118: Add
119: SetL 2
121: PopC
$sum: dbl ; $elem: dbl
$sum: int ; $elem: dbl
114: CGetL 4
116: CGetL2 2
118: Add
119: SetL 2
121: PopC
Example
14
81: CGetM <L:0 EL:3>
94: SetL 4
96: PopC
97: Int 0
106: CGetL2 4
108: Gt
109: JmpZ 13 (122)
114: CGetL 4
116: CGetL2 2
118: Add
119: SetL 2
121: PopC
71: CGetL 1
73: CGetL2 3
75: Lt
76: JmpZ 55 (131)
122: IncDecL 3 PreInc
125: PopC
126: Jmp -55
function addPositive($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 0
106: CGetL2 4
108: Gt
109: JmpZ 13 (122)
$elem: uncount; stk: int
$i: int ; $n: int
$arr: array ; $i: int
$sum: int ; $elem: int
$i: int

15. $elem: uncount; stk: dbl
$sum: dbl ; $elem: dbl
Example
15
81: CGetM <L:0 EL:3>
114: CGetL 4
116: CGetL2 2
118: Add
119: SetL 2
121: PopC
71: CGetL 1
73: CGetL2 3
75: Lt
76: JmpZ 55 (131)
122: IncDecL 3 PreInc
125: PopC
126: Jmp -55
function addPositive($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 0
106: CGetL2 4
108: Gt
109: JmpZ 13 (122)
$i: int ; $n: int
$arr: array ; $i: int
$i: int

16. $elem: uncount; stk: dbl
$sum: dbl
Example
16
81: CGetM <L:0 EL:3>
114: CGetL 4
116: CGetL2 2
118: Add
119: SetL 2
121: PopC
71: CGetL 1
73: CGetL2 3
75: Lt
76: JmpZ 55 (131)
122: IncDecL 3 PreInc
125: PopC
126: Jmp -55
function addPositive($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 0
106: CGetL2 4
108: Gt
109: JmpZ 13 (122)
$i: int ; $n: int
$arr: array

17. 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
17

18. 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
18

19. Challenge #2b: Code Locality
1. 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
19

20. 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
20
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

21. Challenge #3: Reference Counting
• Ongoing effort to move to a tracing garbage
collector
– Currently used to collect cycles
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22. 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
22

23. 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/)
23

24. Questions?