Java/Scala meetup within "Open Technology Week" at Sigma Software Kharkiv.

1. 1
Nicety of java 8 multithreading for advanced
Maksym Voroniy
Software Architect of GlobalLogic

2. 2
Common points01

3. 3
History
Text
The Dream Time sharing
Stone Age Intel 80386
Multiple
data
multiple
handlers
Multiple
processo
rs
Multiple
data
single
handler
Hyper threading
IBM 360
CUDA
Multicore
Today

4. 4
Point #1
Maurice Herlihy & Nir Shavit “The Art of Multiprocessor Programming”

5. 5
Atomicity vs Consistence
static int a = 5;
a *= 3;
int b = 22;
b += a;
mov EAX, 5
mov ds:[a], EAX
mul 3
mov ds:[a], EAX
mov EBX, 22
add EBX, EAX;vs add EBX,ds:[a]
atomicity
consistence

6. 6
Java 8 FAA vs CAS02

7. 7
Nicety #1 - Java 8 Atomics
http://ashkrit.blogspot.com/2014/02/atomicinteger-java-7-vs-java-8.html
fetch-and-add vs compare-and-swap (CAS)
JDK 8 - AtomicInteger
public final int getAndIncrement() {
return unsafe.getAndAddInt(
this, valueOffset, 1);
}
JDK7 - AtomicInteger
public final int getAndIncrement() {
for (;;) {
int current = get();
int next = current + 1;
if (compareAndSet(current, next))
return current;
}
}

8. 8
Why Atomic important?
Text
ConcurrentHashMap<>
Collections.synchronizedMap(new HashMap<>())
• ConcurrentLinkedDeque/Queue
• ConcurrentSkipListSet/Map
• ConcurrentHashMap
• wait-free, if every concurrent operation is guaranteed to be
finished in a finite number of steps
• lock-free, if some concurrent operations are guaranteed to
be finished in a finite number of steps.
DARE
develop concurrent hash-map
TODAY tomorrow

9. 9
ConcurrentHashMap
StampedLock goes to stage
Text
Simple cache implementation (value by key):
(T key, Map<T, V> map) -> {
return map.get(i);
}
synchronizedMap(new HashMap<>())
(T key, Map<T, V> map) -> {
synchronized (map){
return map.get(i);
}
}
HashMap + ReadWriteLock
(T key, Map<T, V> map) -> {
Lock l = rwLock.readLock();
l.lock();
try {
return map.get(i);
} finally {
l.unlock();
}}

10. 10
StampedLock goes to stage (II)
Text
HashMap + StampedLock
(T key, Map<T, V> map) -> {
long l = stampedLock.readLock();
try {
return map.get(i);
} finally {
stampedLock.unlockRead(l);
}}
HashMap + StampedLock+Optimistic
(T key, Map<T, V> map) -> {
long stamp = optimisticLock.tryOptimisticRead();
Integer r = map.get(i);
if (!optimisticLock.validate(stamp)) {
stamp = optimisticLock.readLock();
try {
return map.get(i);
} finally {
optimisticLock.unlockRead(stamp);
}
}
return r;
}
200`000 op for 10
Threads
Nanoseconds
ConcurrentHashMap 72645.057
Optimistic 359819.982
StampedLock 1303853.525
ReadWriteLock 1326939.766
Locked 1922399.053

11. 11
Java 8 Parallelism03

12. 12
.parallelStream()
Text
//from Oracle tutorial:
double average = roster
.parallelStream()
.filter(p -> p.getGender() == Person.Sex.MALE)
.mapToInt(Person::getAge)
.average()
.getAsDouble()
• Java runtime partitions the stream into
multiple substreams. Aggregate operations
iterate over and process these substreams in
parallel and then combine the results
static ExecutorService newWorkStealingPool()
static ExecutorService newFixedThreadPool(int nThreads)
static ScheduledExecutorService newScheduledThreadPool(int corePoolSize)
static ExecutorService newCachedThreadPool(ThreadFactory threadFactory)
• Work Stealing (An idle thread steals work from a thread having tasks queued up more than it
can process currently)
• Ability to recursively decompose the tasks and collect the results. (Apparently, this requirement
must have popped up along with the conception of the notion of parallel processing... but lacked
a solid implementation framework in Java till Java 7)
ForkJoinPool

13. 13
The trap!
List<SomeClass> list = // A list of objects
list.parallelStream()
.map(this::veryLongProcessing)
.collect(toList());
partition
Stream-1
Stream-2
Stream-3
Core -1 Core -2
ForkJoinPool forkJoinPool = new ForkJoinPool(2);
forkJoinPool.submit(() ->
list.parallel()
.map(this::veryLongProcessing)
.collect(toList())
).get();
"Arranges to asynchronously execute this task in the pool the
current task is running in, if applicable, or using the
ForkJoinPool.commonPool() if not in
ForkJoinPool()"

14. 14
The Future
Text
Time sharing
Stone Age Intel 80386
Hyper threading
IBM 360
CUDA
Multicore
HTMSTM

15. 15
Future direction
• How Databases works (SQL& NoSQL)
• Quantum computing
• Advanced data structures (R-Tree, B-Tree, Trie, Skip-lists, Heaps…)
http://goo.gl/forms/9LauaeJBlO
N

16. 16
Thank you!
Any Questions?