I asked question here and someone leaved a comment saying that the problem is I'm sharing a static variable.
Why is that a problem?
Sharing a static variable of and by itself should have no adverse effect on performance. Global data is common is all programs starting with the JVM and OS constructs.
Mutable shared data is a different story as the mutation of shared data can lead to both performance issues (cache misses at the very least) and correctness issues which are a pain and are often solved using locks, which lead to potentially other performance issues.
The wiki static variable looks like a pretty substantial part of your program. Not knowing anything about what it's going or how it's coded, I would guess that it does locking in order to keep a consistent state. If most of your threads are spending their time blocking waiting to acquire access to this same object then that would explain why you're not seeing any gain from using multiple threads.
For threads to make a difference to the performance of your program they have to be reasonably independent, and not all locking on the same thing. The more locking they have to do, the less gain you will see. So try to split out the work so as much can be done independently as possible. For instance if there are work items that can be gathered independently, then you might be better off by having multiple threads go find the work items, then feed them to a queue that a dedicated thread can use to pull work items off the queue and feed them to the wiki object.
Related
So I’m currently listing to this talk.
At minute 28:50 the following statement is made: „the fact that on the hardware it could be in main memory, in multiple level 3 caches, in four level 2 caches […] is not your problem. That’s the problem for the hardware designers.“
Yet, in java we have to declare a boolean stopping a thread as volatile, since when another thread calls the stop method, it’s not guaranteed that the running thread will be aware of this change.
Why is this the case, when the hardware level should take care of updating every cache with the correct value?
I’m sure I’m missing something here.
Code in question:
public class App {
public static void main(String[] args) {
Worker worker = new Worker();
worker.start();
try {
Thread.sleep(10);
} catch (InterruptedException e) {
e.printStackTrace();
}
worker.signalStop();
System.out.println(worker.isShouldStop());
System.out.println(worker.getVal());
System.out.println(worker.getVal());
}
static class Worker extends Thread {
private /*volatile*/ boolean shouldStop = false;
private long val = 0;
#Override
public void run() {
while (!shouldStop) {
val++;
}
System.out.println("Stopped");
}
public void signalStop() {
this.shouldStop = true;
}
public long getVal() {
return val;
}
public boolean isShouldStop() {
return shouldStop;
}
}
}
You are assuming the following:
Compiler doesn't reorder the instructions
CPU performs the loads and stores in the order as specified by your program
Then your reasoning makes sense and this consistency model is called sequential consistency (SC): There is a total order over loads/stores and consistent with the program order of each thread. In simple terms: just some interleaving of the loads/stores. The requirements for SC are a bit more strict, but this captures the essence.
If Java and the CPU would be SC, then there would not be any purpose of making something volatile.
The problem is that you would get terrible performance. A lot of compiler optimizations rely on rewriting the instructions to something more efficient and this can lead to reordering of loads and stores. It could even decide to optimize-out a load or a store so that it doesn't happen. This is all perfectly fine as long as there is just a single thread involved because the thread will not be able to observe these reordering of loads/stores.
Apart from the compiler, the CPU also likes to reorder loads/store. Imagine that a CPU needs to make a write, and the cache-line for that write isn't in the right state. So the CPU would block and this would be very inefficient. Since the store is going to be made anyway, it is better to queue the store in a buffer so that the CPU can continue and as soon as the cache-line is returned in the right state, the store is written to the cache-line and then committed to the cache. Store buffering is a technique used by a lot of processors (e.g. ARM/X86). One problem with it is that it can lead to an earlier store to some address being reordering with a newer load to a different address. So instead of having a total order over all loads and stores like SC, you only get a total order over all stores. This model is called TSO (Total Store Order) and you can find it on the x86 and SPARC v8/v9. This approach assumes that the stores in the store buffer are going to be written to the cache in program order; but there is also a relaxation possible such that store in the store buffer to different cache-lines can be committed to the cache in any order; this is called PSO (Partial Store Order) and you can find it on the SPARC v8/v9.
SC/TSO/PSO are strong memory models because every load and store is a synchronization action; so they order surrounding loads/stores. This can be pretty expensive because for most instructions, as long as the data-dependency-order is preserved, any ordering is fine because:
most memory is not shared between different CPUs.
if memory is shared, often there is some external synchronization like a unlock/lock of a mutex or release-store/acquire-load that takes care of synchronization. So the synchronization can be delayed.
CPU's with weak memory models like ARM, Itanium make use of this. They make a separation between plain loads and stores and synchronizing loads/stores. And for plain loads and stores, any ordering is fine. And modern processors execute instructions out of order any way; there is a lot of parallelism inside a single CPU.
Modern processors do implement cache coherence. The only modern processor that doesn't need to implement cache coherence is a GPU. Cache coherence can be implemented in 2 ways
for small systems the caches can sniff the bus traffic. This is where you see MESI protocol. This technique is called is called sniffing (or snooping).
for larger systems you can have a directory that knows the state of each cache-line and which CPUs are sharing the cache-line and which CPU is owning the cache-line (here there is some MESI-like protocol). And all requests for cache-line go through the directory.
The cache coherence protocol make sure that the cache-line is invalidated on CPUs before a different CPU can write to the cache line. Cache coherence will give you a total order of loads/stores on a single address, but will not provide any ordering of loads/stores between different addresses.
Coming back to volatile:
So what volatile does is:
prevent reordering loads and stores by the compiler and CPU.
ensure that a load/store becomes visible; so it will the compiler from optimizing-out a load or store.
the load/store is atomic; so you don't get problems like a torn read/write. This includes compiler behavior like natural alignment of the field.
I have give you some technical information about what is happening behind the scenes. But to properly understand volatile, you need to understand the Java Memory Model. It is an abstract model that doesn't care about any implementation details as described above. If you would not apply volatile in your example, you would have a data race because a happens-before edge is missing between concurrent conflicting accesses.
A great book on this topic is
A Primer on Memory Consistency and Cache Coherence, Second Edition. You can download it for free.
I can't recommend you any book on the Java Memory Model because it is all explained in an awful manner. Best to get an understanding of memory models in general before diving into the JMM. Probably the best sources are this doctoral dissertation by Jeremy Manson, and Aleksey Shipilëv: One Stop Page.
PS:
There are situations when you don't care about any ordering guarantees, e.g.
stop flag for a thread
progress indicators
blackholes for microbenchmarks.
This is where the VarHandle.getOpaque/setOpaque can be useful. It provides visibility and atomicity, but it doesn't provide any ordering guarantees with respect to other variables. This is mostly a compiler concern. Most engineers will never need this level of control.
What you're suggesting is that hardware designers just make the world all ponies and rainbows for you.
They cannot do that - what you want makes the notion of an on-core cache completely impossible. How could a CPU core possibly know that a given memory location needs to be synced up with another core before accessing it any further, short of just keeping the entire cache in sync on a permanent basis, completely invalidating the entire idea of an on-core cache?
If the talk is strongly suggesting that you as a software engineer can just blame hardware engineers for not making life easy for you, it's a horrible and stupid talk. I bet it's brought a little more nuanced than that.
At any rate, you took the wrong lesson from it.
It's a two-way street. The hardware engineering team works together with the JVM team, effectively, to set up a consistent model that is a good equilibrium between 'With these constraints and limited guarantees to the software engineer, the hardware team can make reliable and significant performance improvements' and 'A software engineer can build multicore software with this model without tearing their hair out'.
This happy equilibrium in java is the JMM (Java Memory Model), which primarily boils down to: All field accesses may have a local thread cache or not, you do not know, and you cannot test if it does. Essentially the JVM has an evil coin an will flip it every time you read a field. Tails, you get the local copy. Heads, it syncs first. The coin is evil in that it is not fair and will land heads through out development, testing, and the first week, every time, even if you flip it a million times. And then the important potential customer demoes your software and you start getting tails.
The solution is to make the JVM never flip it, and this means you need to establish Happens-Before/Happens-After relationships anytime you have a situation anywhere in your code where one thread writes a field and another reads it. volatile is one way to do it.
In other words, to give hardware engineers something to work with, you, the software engineer, effectively made the promise that you'll establish HB/HA if you care about synchronizing between threads. So that's your part of the 'deal'. Their part of the deal is that the hardware guarantees the behaviour if you keep up your end of the deal, and that the hardware is very very fast.
Quote from book java concurrency in practice:
The performance cost of synchronization comes from several sources.
The visibility guarantees provided by synchronized and volatile may
entail using special instructions called memory barriers that can
flush or invalidate caches, flush hardware write buffers, and stall
execution pipelines. Memory barriers may also have indirect
performance consequences because they inhibit other compiler
optimizations; most operations cannot be reordered with memory
barriers. When assessing the performance impact of synchronization, it
is important to distinguish between contended and uncontended
synchronization. The synchronized mechanism is optimized for the
uncontended case (volatile is always uncontended), and at this
writing, the performance cost of a "fastͲpath" uncontended
synchronization ranges from 20 to 250 clock cycles for most systems.
Can you clarify this more clear?
What if I have huge amount threads which read volaile variable ?
Can you provide contention definition?
Is there tool to meausure contention? In which values it is measures?
Can you clarify this more clear?
That is one dense paragraph that touches a lot of topics. Which topic or topics specifically are you asking for clarification? Your question is too broad to answer satisfactorily. Sorry.
Now, if you question is specific to uncontended synchronization, it means that threads within a JVM do not have to block, get unblocked/notified and then go back to a blocked state.
Under the hood, the JVM uses hardware specific memory barriers that ensure
A volatile field is always read and written to/from main memory, not from the CPU/core cache, and
Your thread will not block/unblock to access it.
There is no contention. When you use a synchronized block OTH, all your threads are in a blocked state except one, the one reading whatever data is being protected by the synchronized block.
Let's call that thread, the one accessing the synchronized data, thread A.
Now, here is the kicker, when thread A is done with the data and exists the synchronized block, this causes the JVM to wake up all the other threads that are/were waiting for thread A to exit the synchronization block.
They all wake up (and that is expensive CPU/memory wise). And they all race trying to get a hold of the synchronization block.
Imagine a whole bunch of people trying to exit a crowded room through a tiny room. Yep, like that, that's how threads act when they try to grab a synchronization lock.
But only one gets it and gets in. All the others go back to sleep, kind of, in what is called a blocked state. This is also expensive, resource wise.
So every time one of the threads exists a synchronization block, all the other threads go crazy (best mental image I can think of) to get access to it, one gets it, and all the others go back to a blocked state.
That's what makes synchronized blocks expensive. Now, here is the caveat: It used to be very expensive pre JDK 1.4. That's 17 years ago. Java 1.4 started seeing some improvements (2003 IIRC).
Then Java 1.5 introduced even greater improvements in 2005, 12 years ago, which made synchronized blocks less expensive.
It is important to keep such things in mind. There is a lot of outdated information out there.
What if I have huge amount threads which read volaile variable ?
It wouldn't matter that much in terms of correctness. A volatile field will always show a consistent value regardless of the number of threads.
Now, if you have a very large number of threads, performance can suffer because of context switches, memory utilization, etc (and not necessarily and/or primarily because of accessing a volatile field.
Can you provide contention definition?
Please don't take it the wrong way, but if you are asking that question, I'm afraid you are not fully prepared to use a book like the one you are reading.
You will need a more basic introduction to concurrency, and contention specifically.
https://en.wikipedia.org/wiki/Resource_contention
Best regards.
Under what circumstances do you need to synchronize an array?
My thoughts are, do you need to synchronize for access? Say two threads access the array at the same time, is that going to crash?
What if one edits, while one is reading? (separate values, and the same in different circumstances)
Both editing different things?
Or is there no JVM crash like for arrays when you don't synchronize?
Under what circumstances do you need to synchronize an array?
It's sort of you either always need to or never need to. Like #EJP said, he's never done it because there's almost always a better data structure than an array, anyway (edit: there are lots of good use cases for arrays, but they're almost always used in isolation. e.g. ArrayList). But if you insist on sharing arrays between threads, array elements aren't volatile, so because of possible caching, you'll get inconsistencies and corrupt data without using synchronized.
My thoughts are, do you need to synchronize for access? Say two threads access the array at the same time, is that going to crash?
Crash, no, but your data could be inconsistent, and extra inconsistent if they're 64-bits on a 32-bit architecture.
What if one edits, while one is reading? (separate values, and the same in different circumstances)
Please don't. Wrapping your head around the Java memory model is hard enough. If you haven't established that a read or a write happened-before another read or write, the ultimate sequencing is undefined.
This is a difficult question because it touches on a lot of Concurrency topics.
First I'd start with, http://docs.oracle.com/javase/tutorial/essential/concurrency/sync.html
Threads communicate primarily by sharing access to fields and the objects reference fields refer to. This form of communication is extremely efficient, but makes two kinds of errors possible: thread interference and memory consistency errors. The tool needed to prevent these errors is synchronization.
A. Thread Interference describes how errors are introduced when multiple threads access shared data.
B. Memory Consistency Errors describes errors that result from inconsistent views of shared memory.
So to answer the main question directly, You synchronize an array when you believe that your array maybe be accessed in a way that introduces Thread interference or Memory Consistency Errors mainly.
You end up with what's called a Race Condition. Whether that crashes your application or not depends on your application.
So if you do not synchronize access to an array that is shared between multiple threads you run the chance of threads interleaving modifications to this array ( ie. Thread Interference ). Or the chance that threads read inconsistent data in your array ( ie. Memory Consistency ).
The solution is typically to synchronize the array, or us a Collection built for Concurrency, such as those discribed at https://docs.oracle.com/javase/tutorial/essential/concurrency/collections.html
I'm writing a highly concurrent application, needing access to a large fine-grained set of shared resources. I'm currently writing a global lock manager to organize this. I'm wondering if I can piggyback off the standard ConcurrentHashMap and use that to handle the locking? I'm thinking of a system like the following:
A single global ConcurrentHashMap object contains a mapping between the unique string id of the resource, and a lock protecting that resource unique id of the thread using the resource
Tune the concurrency factor to reflect the need for a high level of concurrency
Locks are acquired using the atomic conditional replace(K key, V oldValue, V newValue) method in the hashmap
To prevent lock contention when locking multiple resources, locks must be acquired in alphabetical order
Are there any major issues with the setup? How will the performance be?
I know this is probably going to be much slower and more memory-heavy than a properly written locking system, but I'd rather not spend days trying to write my own, especially given that I probably won't be able to match Java's professionally-written concurrency code implementing the map.
Also, I've never used ConcurrentHashMap in a high-load situation, so I'm interested in the following:
How well will this scale to large numbers of elements? (I'm looking at ~1,000,000 being a good cap. If I reach beyond that I'd be willing to rewrite this more efficiently)
The documentation states that re-sizing is "relatively" slow. Just how slow is it? I'll probably have to re-size the map once every minute or so. Is this going to be problematic with the size of map I'm looking at?
Edit: Thanks Holger for pointing out that HashMaps shouldn't have that big of an issue with scaling
Also, is there is a better/more standard method out there? I can't find any places where a system like this is used, so I'm guessing that either I'm not seeing a major flaw, or theres something else.
Edit:
The application I'm writing is a network service, handling a variable number of requests. I'm using the Grizzly project to balance the requests among multiple threads.
Each request uses a small number of the shared resources (~30), so in general, I do not expect a large great deal of contention. The requests usually finish working with the resources in under 500ms. Thus, I'd be fine with a bit of blocking/continuous polling, as the requests aren't extremely time-sensitive and contention should be minimal.
In general, seeing that a proper solution would be quite similar to how ConcurrentHashMap works behind the scenes, I'm wondering if I can safely use that as a shortcut instead of writing/debugging/testing my own version.
The re-sizing issue is not relevant as you already told an estimate of the number of elements in your question. So you can give a ConcurrentHashMap an initial capacity large enough to avoid any rehashing.
The performance will not depend on the number of elements, that’s the main goal of hashing, but the number of concurrent threads.
The main problem is that you don’t have a plan of how to handle failed locks. Unless you want to poll until locking succeeds (which is not recommended) you need a way of putting a thread to sleep which implies that the thread currently owning the lock has to wake up a sleeping thread on release if one exists. So you end up requiring conventional Lock features a ConcurrentHashMap does not offer.
Creating a Lock per element (as you said ~1,000,000) would not be a solution.
A solution would look a bit like the ConcurrentHashMap works internally. Given a certain concurrency level, i.e. the number of threads you might have (rounded up), you create that number of Locks (which would be a far smaller number than 1,000,000).
Now you assign each element one of the Locks. A simple assignment would be based on the element’s hashCode, assuming it is stable. Then locking an element means locking the assigned Lock which gives you up to the configured concurrency level if all currently locked elements are mapped to different Locks.
This might imply that threads locking different elements block each other if the elements are mapped to the same Lock, but with a predictable likelihood. You can try fine-tuning the concurrency level (as said, use a number higher than the number of threads) to find the best trade-off.
A big advantage of this approach is that you do not need to maintain a data structure that depends on the number of elements. Afaik, the new parallel ClassLoader uses a similar technique.
Using synchronization slows down the execution of a program. Is there a way to improve the speed of execution ?
Saying that a synchronization construct slows down execution is like saying that a parachute slows down a skydiver. Going without will be faster, but that's not exactly the point. Synchronization serves a purpose.
To improve the speed of execution, simply apply synchronization properly.
For example, using the Producer/Consumer design pattern may help you reduce the number of synchronization constructs required in your code.
It's simply not true that "synchronization slows down programs" - it only does when the synchronized actions are done very frequently, or when you actually have a lot of threads contending for them. For most applications, neither is true.
Also, some kinds of concurrent operations can be implemented safely without synchronization by using clever data structures or hardware primitives. Examples:
ConcurrentHashMap
CopyOnWriteArrayList
AtomicInteger
Profile your code, find out where the real bottlenecks lie.
Carefully re-analyse your critical regions. It's very easy to apply synchronization too broadly.
Sometimes changing algorithm can lead to a completely different synchronization profile. This doesn't always have a positive effect though!
Have you measured how much (if any) the slowdown is ?
The early JVMs suffered a penalty when using synchronisation. However that situation has improved vastly over the years. I wouldn't worry about a performance penalty when synchronising. There will be many more candidates for optimisations.
You might want to synchronize the block of code rather than the whole method. Without it, you are risking whole lot more!