Scala to assembly

JAREKJAREK
Started on C64 (Basic, 6502), Amiga (68k, C), C++, Java, JS, Scala, Kotlin...

IS THAT ALL TRUE?IS THAT ALL TRUE?

long time ago
tmp = $fb
Start:
ldx #10; !! decimal
clc
ldy #$0
lda #$1
fib: dex
beq end
sty tmp
tay
adc tmp
jmp fib
end:
tay

long time ago
branch not taken 2 cycles
tmp = $fb
Start:
ldx #10; !! decimal
clc
ldy #$0
lda #$1
fib: dex
beq end
sty tmp
tay
adc tmp
jmp fib
end:
tay

long time ago
branch not taken 2 cycles
branch taken 3(!!!) cycles
tmp = $fb
Start:
ldx #10; !! decimal
clc
ldy #$0
lda #$1
fib: dex
beq end
sty tmp
tay
adc tmp
jmp fib
end:
tay

Rewrite if statement to save a cycle

motorola 68040
branch taken �xed prediction strategy

PERFORMANCE MYTHSPERFORMANCE MYTHS
not precise,
valid only in certain contexts,
valid in the past,
not relevant

COST OF PERFORMANCE MYTHSCOST OF PERFORMANCE MYTHS
Code:
less readable
less �exible
less abstract
Lost focus.

PERFORMANCE IS A GOOD MARKETING TOOLPERFORMANCE IS A GOOD MARKETING TOOL

Marketing my shiny new library

SCALASCALA
Scala combines object-oriented and functional programming in one
concise, high-level language.

SOME ARTICLESSOME ARTICLES
applicative-functors-vs-monad-composing-performance-in-scala
No More Transformers: High-Performance E�ects in Scalaz 8
ON PERFORMANCE OF THE NEW COLLECTIONS
Scala’s immutable collections can be slow as a snail
...

SCALA DOES NOT MAKE IT EASYSCALA DOES NOT MAKE IT EASY

A given piece of code may and will perform di�erently when
called/used in a di�erent context

WE WILL GO DEEPWE WILL GO DEEP

TOOLSTOOLS
All the benchmarks and numbers in this talk are presented only to
show the tools
DO NOT make any decisions based on those numbers

LET'S TALK ABOUT IMMUTABILITYLET'S TALK ABOUT IMMUTABILITY

object ImmutableCalc {
case class Calculator(total : Int = 0) {
def add(i: Int) :Calculator = this.copy(total = total + i)
def get() : Int = total
}
}

object MutableCalc {
class Calculator {
var total = 0
def add(i: Int) : Unit = total+=i
}
}

INTO THE BYTECODEINTO THE BYTECODE

ls
'ImmutableCalc$Calculator.class'
'ImmutableCalc$Calculator$.class'
ImmutableCalc.class
'ImmutableCalc$.class'k

and ~ 20 other methods
//CODE decompiled using decompile Scala to Java
public final class ImmutableCalc {
public static class Calculator implements Product, Serializable {
private final int total;
public Iterator productElementNames() {
return Product.productElementNames$(this);
}
public int total() {
return this.total;
}
public ImmutableCalc.Calculator add(final int i) {
return this.copy(this.total() + i);
}
public int get() {
return this.total();
}
public ImmutableCalc.Calculator copy(final int total) {

javap -p -c 'ImmutableCalc$Calculator.class'
public calculator.immuable.ImmutableCalc$Calculator add(int);
Code:
0: aload_0
1: aload_0
2: invokevirtual #31 // Method total:()I
5: iload_1
6: iadd
7: invokevirtual #34 // Method copy:(I)Lcalculator/immuable/ImmutableCalc$Calcula
10: areturn

public calculator.immuable.ImmutableCalc$Calculator add(int);
Code:
0: aload_0 ; stack = [this]
1: aload_0 ; stack = [this,this]
2: invokevirtual #31 ; total() ; stack = [this,this.total]
5: iload_1 ; stack = [this, this.total, i]
6: iadd ; stack = [this, total + i]
7: invokevirtual #34 ; copy(...); stack = [newObject]
10: areturn ;return newObject
https://en.wikipedia.org/wiki/Java_bytecode_instruction_listings

INFOINFO
There is also lot of metadata in .class �les (not only bytecode)
No one needs to read bytecode - unless You work on a compiler

Run it
object CalcRun {
def main(args: Array[String]): Unit = {
var sum = 0L
for ( i <-0 to 100000) {
val calc = ImmutableCalc.Calculator()
sum+=(1 to 10000).foldLeft( calc)((c,i) => c.add(i)).get()
}
println(sum)
}
}

0,1,2,3,4 - compilation Tier - higher ~ more optimized
% - on stack replacement
made not entrant - do not use this
! - exception handler
s - synchronized
298 485 2 runners.CalcRun$::$anonfun$main$2 (6 bytes)
298 486 2 calculator.immuable.ImmutableCalc$Calculator::add (11 bytes)
298 487 2 calculator.immuable.ImmutableCalc$Calculator::copy (9 bytes)
298 472 1 java.lang.Integer::intValue (5 bytes)
298 473 1 scala.collection.immutable.RangeIterator::hasNext (5 bytes)
298 475 2 java.lang.Integer::<init> (10 bytes)
298 474 1 calculator.immuable.ImmutableCalc$Calculator::total (5 bytes)
298 476 2 java.lang.Integer::valueOf (32 bytes)
...
335 501 % 4 scala.collection.IterableOnceOps::foldLeft @ 13 (43 bytes)
341 499 % 3 scala.collection.IterableOnceOps::foldLeft @ -2 (43 bytes) made not entrant

0,1,2,3,4 - compilation Tier - higher ~ more optimized
% - on stack replacement
made not entrant - do not use this
! - exception handler
s - synchronized
298 485 2 runners.CalcRun$::$anonfun$main$2 (6 bytes)
298 486 2 calculator.immuable.ImmutableCalc$Calculator::add (11 bytes)
298 487 2 calculator.immuable.ImmutableCalc$Calculator::copy (9 bytes)
298 472 1 java.lang.Integer::intValue (5 bytes)
298 473 1 scala.collection.immutable.RangeIterator::hasNext (5 bytes)
298 475 2 java.lang.Integer::<init> (10 bytes)
298 474 1 calculator.immuable.ImmutableCalc$Calculator::total (5 bytes)
298 476 2 java.lang.Integer::valueOf (32 bytes)
...
335 501 % 4 scala.collection.IterableOnceOps::foldLeft @ 13 (43 bytes)
341 499 % 3 scala.collection.IterableOnceOps::foldLeft @ -2 (43 bytes) made not entrant
https://gist.github.com/rednaxelafx/1165804#�le_notes.md

-XX:+PrintInlining
1275 579 4 runners.CalcRun$$$Lambda$3/1967205423::apply$mcVI$sp (9 bytes)
@ 5 runners.CalcRun$::$anonfun$main$1 (56 bytes) inline (hot)
@ 7 calculator.immuable.ImmutableCalc$Calculator$::apply$default
@ 10 calculator.immuable.ImmutableCalc$Calculator::<init> (
@ 6 java.lang.Object::<init> (1 bytes) inline (hot)
@ 10 scala.Product::$init$ (1 bytes) inline (hot)
@ 26 scala.LowPriorityImplicits::intWrapper (2 bytes) inline (hot)
@ 32 scala.runtime.RichInt$::to$extension (15 bytes) inline (hot)
@ 11 scala.collection.immutable.Range$Inclusive::<init> (
@ 4 scala.collection.immutable.Range::<init> (215 bytes) inline
@ 16 scala.collection.immutable.AbstractSeq::<init> (
@ 1 scala.collection.AbstractSeq::<init> (5 bytes) inline (h
@ 1 scala.collection.AbstractIterable::<init> (5 bytes) in
@ 1 java.lang.Object::<init> (1 bytes) inline (hot)
@ 78 scala.collection.immutable.Range::isEmpty (5 bytes) acces
@ 89 scala.collection.immutable.Range::longLength (25 bytes) i
@ 1 scala.collection.immutable.Range::gap (12 bytes) inline
@ 1 scala.collection.immutable.Range::end (5 bytes) access
@ 6 scala.collection.immutable.Range::start (5 bytes) acce
@ 5 scala.collection.immutable.Range::step (5 bytes) accesso
@ 11 scala.collection.immutable.Range::hasStub (18 bytes) in
@ 1 scala.collection.immutable.Range$Inclusive::isInclusive

WHY BOTHER WITH PRINTCOMPILATION,WHY BOTHER WITH PRINTCOMPILATION,
INLININGINLINING
1. check if critical code was in fact JITed
2. sometimes problems with bytecode can be seen (method never
compiled)
3. look for uncommon traps

MEASURINGMEASURING
JMH
jvm warmup
prevent dead code elimination
addSbtPlugin("pl.project13.scala" % "sbt-jmh" % "0.3.7")

VM version: JDK 11.0.6, OpenJDK 64-Bit Server VM, 11.0.6+9-jvmci-20.0-
b02
@Benchmark
def measureWrong: Unit =
Math.log(x)
@Benchmark
def measureRight: Double =
Math.log(x)
[info] JMHSample_08_DeadCode.measureRight avgt 3 9.891 ± 13.465 ns/op
[info] JMHSample_08_DeadCode.measureWrong avgt 3 0.383 ± 0.132 ns/op

@Benchmark
def measureWrong: Unit =
Math.log(x) // in fact same as = {}

@Benchmark
def imperative: Int = {
val calc = new MutableCalc.Calculator()
(1 to numOps).foreach(calc.add)
calc.get()
}
@Benchmark
def immutable: Int = {
val calc = ImmutableCalc.Calculator()
(1 to numOps).foldLeft( calc)((c,i) => c.add(i)).get()
}

object ImmutableCalc {
case class Calculator(total : Int = 0) {
def add(i: Int) :Calculator = this.copy(total = total + i)
}
}
object MutableCalc {
class Calculator {
var total = 0
def add(i: Int) : Unit = total+=i
}
}

VM version: JDK 1.8.0_242, OpenJDK 64-Bit Server VM, 25.242-b08

VM version: JDK 1.8.0_242, OpenJDK 64-Bit Server VM, 25.242-b08
[info] Benchmark Mode Cnt Score Error Units
[info] CalcCost.freeMonadScalaZ avgt 5 154761.225 ± 10652.856 ns/op
[info] CalcCost.immutable avgt 5 15393.204 ± 670.565 ns/op
[info] CalcCost.imperative avgt 5 284.435 ± 11.790 ns/op
[info] CalcCost.tfIO avgt 5 123841.734 ± 30096.835 ns/op

VM version: JDK 14.0.1, OpenJDK 64-Bit Server VM, 14.0.1+7

20.0.0.r11-grl

INSPECTING THE JVMINSPECTING THE JVM

JavaFlightRecorder - record "events"
Java Mission Control - browse results

The Java Flight Recorder (JFR) is a commercial feature. You can use it
for free on developer desktops/laptops, and for evaluation purposes in
test, development, and production environments. However, to enable
JFR on a production server, you require a commercial license. Using
JMC UI for other purposes on the JDK does not require a commercial
license.

Java Flight Recorder
low overhead,
detailed jvm events,
additional graphs and tools (for jfr) (intellij)
own events, (use for low overhead trace log)
only on some jvms
license - check your jvm version
-XX:+FlightRecorder
-XX:StartFlightRecording=duration=60s,filename=myrecording.j

JPSJPS
hint: jps -mlVv (detailed jvm args)
58661 Main
93654 Jps
57464 NailgunRunner
77659 org.eclipse.equinox.launcher_1.3.0.v20140415-2008.jar
80924 Launcher

jstack 91221 (java 8)
"main" #1 prio=5 os_prio=0 tid=0x00007f33b400d800 nid=0x14812 runnable [0x00007f33bdf38000]
java.lang.Thread.State: RUNNABLE
at calculator.immuable.ImmutableCalc$Calculator.<init>(ImmutableCalc.scala:4)
at calculator.immuable.ImmutableCalc$Calculator.copy(ImmutableCalc.scala:4)
at calculator.immuable.ImmutableCalc$Calculator.add(ImmutableCalc.scala:6)
at runners.CalcRun$.$anonfun$main$2(CalcRun.scala:10)
at runners.CalcRun$.$anonfun$main$2$adapted(CalcRun.scala:10)
at runners.CalcRun$$$Lambda$4/801197928.apply(Unknown Source)
at scala.collection.IterableOnceOps.foldLeft(IterableOnce.scala:636)
at scala.collection.IterableOnceOps.foldLeft$(IterableOnce.scala:632)
at scala.collection.AbstractIterable.foldLeft(Iterable.scala:921)

jstack 94038 (graal)
"main" #1 prio=5 os_prio=0 cpu=26791.23ms elapsed=26.89s tid=0x00007fafb0028800 nid=0x13d86 runnable [
at scala.collection.IterableOnceOps.foldLeft(IterableOnce.scala:635)
at scala.collection.IterableOnceOps.foldLeft$(IterableOnce.scala:632)
at scala.collection.AbstractIterable.foldLeft(Iterable.scala:921)
at runners.CalcRun$$$Lambda$17/0x00000008400d8040.apply$mcVI$sp(Unknown Source)
at scala.collection.immutable.Range.foreach$mVc$sp(Range.scala:190)
at runners.CalcRun$.main(CalcRun.scala:8)
at runners.CalcRun.main(CalcRun.scala)
"Reference Handler" #2 daemon prio=10 os_prio=0 cpu=0.32ms elapsed=26.87s tid=0x00007fafb024c800 nid=
at java.lang.ref.Reference.waitForReferencePendingList(java.base@11.0.6/Native Method)
at java.lang.ref.Reference.processPendingReferences(java.base@11.0.6/Reference.java:241
at java.lang.ref.Reference$ReferenceHandler.run(java.base@11.0.6/Reference.java:213)

BEWARE OF SAFEPOINTSBEWARE OF SAFEPOINTS

INTO ASSEMBLYINTO ASSEMBLY
We need to go deeper

[Constants]
# {method} {0x00007f93f0e2fc48} 'add' '(I)Lcalculator/immuable/ImmutableCalc$Calculator;' in
# this: rsi:rsi = 'calculator/immuable/ImmutableCalc$Calculator'
# parm0: rdx = int
# [sp+0x80] (sp of caller)
0x00007f93f1422680: mov 0x8(%rsi),%r10d
0x00007f93f1422684: shl $0x3,%r10
0x00007f93f1422688: cmp %rax,%r10
0x00007f93f142268b: jne 0x00007f93f1045ba0 ; {runtime_call}
0x00007f93f1422691: nopl 0x0(%rax,%rax,1)
0x00007f93f1422699: nopl 0x0(%rax)
[Verified Entry Point]
0x00007f93f14226a0: mov %eax,-0x14000(%rsp)
0x00007f93f14226a7: push %rbp
0x00007f93f14226a8: sub $0x70,%rsp
0x00007f93f14226ac: movabs $0x7f93f0e43db0,%rdi
0x00007f93f14226b6: mov 0x8(%rdi),%eax
0x00007f93f14226b9: add $0x8,%eax
0x00007f93f14226bc: mov %eax,0x8(%rdi)
0x00007f93f14226bf: movabs $0x7f93f0e2fc48,%rdi ; {metadata({method} {0x00007f93f0e2fc48} 'add' '(I
0x00007f93f14226c9: and $0x3ff8,%eax
0x00007f93f14226cf: cmp $0x0,%eax

-XX:PrintAssemblyOptions=intel
0x00007f11f540f880: mov r10d,DWORD PTR [rsi+0x8]
0x00007f11f540f884: shl r10,0x3
0x00007f11f540f888: cmp r10,rax
0x00007f11f540f88b: jne 0x00007f11f5045ba0 ; {runtime_call}
0x00007f11f540f891: nop DWORD PTR [rax+rax*1+0x0]
0x00007f11f540f899: nop DWORD PTR [rax+0x0]
[Verified Entry Point]
0x00007f11f540f8a0: mov DWORD PTR [rsp-0x14000],eax
0x00007f11f540f8a7: push rbp
0x00007f11f540f8a8: sub rsp,0x70
0x00007f11f540f8ac: movabs rdi,0x7f11e3e43db0
0x00007f11f540f8b6: mov eax,DWORD PTR [rdi+0x8]
0x00007f11f540f8b9: add eax,0x8
0x00007f11f540f8bc: mov DWORD PTR [rdi+0x8],eax
0x00007f11f540f8bf: movabs rdi,0x7f11e3e2fc48 ; {metadata({method} {0x00007f11e3e2fc48} 'add' '(I)L
0x00007f11f540f8c9: and eax,0x3ff8
0x00007f11f540f8cf: cmp eax,0x0
0x00007f11f540f8d2: je 0x00007f11f540f92c ;*aload_0

jstat java 8
jstat -gccause 128409 1000 1000
S0 S1 E O M CCS YGC YGCT FGC FGCT CGC CGCT GCT LGCC
0.00 6.25 0.00 1.10 93.80 80.76 59 0.137 0 0.000 - - 0.137
0.00 0.00 56.00 1.10 93.80 80.76 63 0.146 0 0.000 - - 0.146
0.00 6.25 6.00 1.10 93.80 80.76 67 0.155 0 0.000 - - 0.155
6.25 0.00 76.00 1.10 93.80 80.76 70 0.162 0 0.000 - - 0.162
0.00 0.00 78.00 1.10 93.80 80.76 74 0.172 0 0.000 - - 0.172
6.25 0.00 70.00 1.10 93.80 80.76 78 0.181 0 0.000 - - 0.181
6.25 0.00 44.00 1.10 93.80 80.76 82 0.193 0 0.000 - - 0.193
0.00 0.00 22.00 1.10 93.80 80.76 86 0.199 0 0.000 - - 0.199
6.25 0.00 20.00 1.10 93.80 80.76 90 0.205 0 0.000 - - 0.205
0.00 0.00 44.00 1.10 93.80 80.76 95 0.213 0 0.000 - - 0.213
0.00 6.25 58.00 1.10 93.80 80.76 101 0.226 0 0.000 - - 0.226
0.00 0.00 76.00 1.10 93.80 80.76 104 0.239 0 0.000 - - 0.239
0.00 6.25 42.00 1.10 93.80 80.76 107 0.249 0 0.000 - - 0.249
0.00 0.00 30.00 1.10 93.80 80.76 110 0.255 0 0.000 - - 0.255

jstat graal
jstat -gccause 129920 1000 1000
S0 S1 E O M CCS YGC YGCT FGC FGCT CGC CGCT GCT LGCC
0.00 100.00 2.58 0.00 57.25 54.24 1 0.003 0 0.000 0 0.000 0.003
0.00 100.00 2.58 0.00 57.25 54.24 1 0.003 0 0.000 0 0.000 0.003
0.00 100.00 2.58 0.00 57.25 54.24 1 0.003 0 0.000 0 0.000 0.003
0.00 100.00 2.58 0.00 57.25 54.24 1 0.003 0 0.000 0 0.000 0.003
0.00 100.00 2.58 0.00 57.25 54.24 1 0.003 0 0.000 0 0.000 0.003
0.00 100.00 2.58 0.00 57.25 54.24 1 0.003 0 0.000 0 0.000 0.003
0.00 100.00 2.58 0.00 57.25 54.24 1 0.003 0 0.000 0 0.000 0.003
0.00 100.00 2.58 0.00 57.25 54.24 1 0.003 0 0.000 0 0.000 0.003

THERE ARE MORE TOOLSTHERE ARE MORE TOOLS
JPro�ler DynaTrace

IF WE HAVE SOME TIMEIF WE HAVE SOME TIME

LETS TALS ABOUT MONAD TRANSFORMERSLETS TALS ABOUT MONAD TRANSFORMERS

case class User(id: Long, name: String, parentId: Long)
object UserData {
val allUsers = List(
User(1, "Nikt", 8),
User(2, "Ktos", 7),
User(4, "A", 2),
User(7, "B", 4)
)
}

(scalaz used)
object Users {
val allUsers = UserData.allUsers
def findUser(id: Long): OptionT[Task, User] = OptionT.optionT {
Task.delay({
val found: Option[User] = allUsers.find(u => u.id == id)
found
}
)
}
}
val indices: Seq[Int] = (1 to 100).toList
def run = {
val users: Seq[OptionT[Task, User]] = indices.map(id => Users.findUser(id))
val parents = users.map(u => u.flatMap(z => Users.findUser(z.parentId)))
parents.map(u => u.run.unsafePerformSync).count(u => u.isDefined)
}

object StackedMonads {
def run = {
val users = indices.map ( id => Users.findUser(id))
val parents = users.map ((u: Task[Option[User]]) =>
u.flatMap((ou: Option[User]) => ou match {
case None => Task.delay({None})
case Some(x) => Users.findUser(x.parentId)
}
)
)
parents.map (u=>u.unsafePerformSync).count(u =>u.isDefined)
}
def findUser(id: Long): Task[Option[User]] = {
Task.delay {
val found: Option[User] = allUsers.find(u => u.id == id)
found
}
}

Monad transformer vs simply stacked (jvm 8)

Let's simply use null
object NOMonad {
def run = {
val users = indices.map(id => Users.findUser(id))
val parents: Seq[User] = users.map(u =>
if (u != null) {
Users.findUser(u.parentId)
} else {
null
}
)
parents.count((u: User) =>u != null)
}
object Users {
val allUsers = UserData.allUsers
def findUser(id: Long): User = {
val found= allUsers.find(u => u.id == id).orNull
found
}
}

wow, nulls are awesome! (jvm 8)

object SimpleDB {
val connection = DB.initializeDB
def run = {
val users = indices.map(id => Users.findUser(id))
val parents: Seq[User] = users.map(u =>
if (u != null) {
Users.findUser(u.parentId)
} else {
null
}
)
parents.count((u: User) => u != null)
}
object Users {
val userQuery = connection.prepareStatement("SELECT * FROM USERS WHERE id= ?")
def findUser(id: Long): User = {
userQuery.clearParameters()
userQuery.setLong(1, id)
val res = userQuery.executeQuery()

Monad Transformer combined with DB

object OptimizedDB {
val connection = DB.initializeDB
def run = {
val count = Users.countParents()
count
}
object Users {
val userQuery = connection.prepareStatement("SELECT " +
"count(*) FROM USERS u1, USERS u2 WHERE u1.id>1 and u1.id < 100 AND u1.parentId = u2.id"
def countParents(): Int = {
userQuery.clearParameters()
val res = userQuery.executeQuery()
try {
if (res.next()) {
res.getInt(1)
} else {
throw new IllegalStateException("no count from DB")
}

jvm 8
[info] MT.monadTR avgt 3 16210.589 ± 14114.784 ns/op
[info] MT.monadTRDB avgt 3 112424.770 ± 109957.046 ns/op
[info] MT.noMonad avgt 3 1910.297 ± 64.699 ns/op
[info] MT.optimizedDB avgt 3 685.030 ± 99.553 ns/op
[info] MT.simpleDB avgt 3 103198.262 ± 36190.463 ns/op
[info] MT.stackedMonads avgt 3 13040.031 ± 733.337 ns/op

jvm 14

graal 20

DEEPER THAN ASSEMBLYDEEPER THAN ASSEMBLY
Just looping

simple for
val array = Array.ofDim[Long](sz + 1, sz + 1)
@Benchmark
def forBasedLoop: Long = {
var sum: Long = 0
for (i <- 0 to sz) {
for (j <- 0 to sz) {
sum += array(i)(j)
}
}
sum
}

while
@Benchmark
def whileLoopA: Long = {
var sum: Long = 0
var i = 0
while (i < sz) {
var j = 0
while (j < sz) {
sum += array(i)(j)
j = j + 1
}
i = i + 1
}
sum
}

while reversed (and transposed)
@Benchmark
def whileRevLoop: Long = {
var sum: Long = 0
var j = sz
while (j >= 0) {
var i = sz
while (i >= 0) {
sum += array(i)(j)
i = i - 1
}
j = j - 1
}
sum
}

PERFORMANCE COUNTERSPERFORMANCE COUNTERS
jmh:run -prof perf -w 5s -r 5s -wi 3 -i 3 -f 3
.*ArrayLooping.*

[info] 14,650.91 msec task-clock # 0.482 CPUs utilized
[info] 525 context-switches # 0.036 K/sec
[info] 65 cpu-migrations # 0.004 K/sec
[info] 185 page-faults # 0.013 K/sec
[info] 30,370,384,927 cycles # 2.073 GHz (
[info] 285,624,461 stalled-cycles-frontend # 0.94% frontend cycles idle (
[info] 19,572,932,807 stalled-cycles-backend # 64.45% backend cycles idle (
[info] 35,907,154,673 instructions # 1.18 insn per cycle
[info] # 0.55 stalled cycles per insn (
[info] 6,706,376,215 branches # 457.745 M/sec (
[info] 12,810,882 branch-misses # 0.19% of all branches (
[info] 16,163,472,375 L1-dcache-loads # 1103.240 M/sec (
[info] 10,975,494,751 L1-dcache-load-misses # 67.90% of all L1-dcache hits (
[info] <not supported> LLC-loads
[info] <not supported> LLC-load-misses
[info] 175,788,792 L1-icache-loads # 11.998 M/sec (
[info] 7,051,118 L1-icache-load-misses # 4.01% of all L1-icache hits (
[info] 5,302,762,430 dTLB-loads # 361.941 M/sec (
[info] 14,779,238 dTLB-load-misses # 0.28% of all dTLB cache hits (
[info] 245,713 iTLB-loads # 0.017 M/sec (
[info] 1,195,242 iTLB-load-misses # 486.44% of all iTLB cache hits (
[info] 289,031,594 L1-dcache-prefetches # 19.728 M/sec (

while T+R
[info] 30,370,384,927 cycles # 2.073 GHz (

while T+R
L1
[info] 30,370,384,927 cycles # 2.073 GHz (

[info] 56,887,530,219 cycles # 3.896 GHz (
[info] 113,201,590 dTLB-loads # 7.753 M/sec (
[info] 939,260 iTLB-load-misses # 2031.80% of all iTLB cache hits (
[info] 7,001,888,370 L1-dcache-prefetches # 479.545 M/sec (

normal while
[info] 56,887,530,219 cycles # 3.896 GHz (

normal while
L1
[info] 56,887,530,219 cycles # 3.896 GHz (

NOT COVEREDNOT COVERED
Garbage collectors
Ahead of time compilation
JVM options

BENCHMARKSBENCHMARKS
All benchmarks above were performed unprofessionaly
All benchmarks lie

ON A GOOD BENCHMARKSON A GOOD BENCHMARKS
Benchmark might be ok:
you test it for yourself
you read about the tools tested
you can �nd what causes di�erences

HINTSHINTS
Tools will not magically solve your problems
Tools let you feel more comfortable
Do not expect PrintAssembly or Performance counters will help you in
typical problems

REAL PROBLEMS I HAD ON PRODUCTIONREAL PROBLEMS I HAD ON PRODUCTION
hidden read of whole database in a dirty piece of code
static synchronized
hashCode bugs
jvm bugs (metaspace)
Lot of them could be spotted by using jstack

Scala to assembly

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