Efficient Independent Synchronized Blocks? - performance

I have a scenario where, at certain points in my program, a thread needs to update several shared data structures. Each data structure can be safely updated in parallel with any other data structure, but each data structure can only be updated by one thread at a time. The simple, naive way I've expressed this in my code is:
synchronized updateStructure1();
synchronized updateStructure2();
// ...
This seems inefficient because if multiple threads are trying to update structure 1, but no thread is trying to update structure 2, they'll all block waiting for the lock that protects structure 1, while the lock for structure 2 sits untaken.
Is there a "standard" way of remedying this? In other words, is there a standard threading primitive that tries to update all structures in a round-robin fashion, blocks only if all locks are taken, and returns when all structures are updated?
This is a somewhat language agnostic question, but in case it helps, the language I'm using is D.

If your language supported lightweight threads or Actors, you could always have the updating thread spawn a new a new thread to change each object, where each thread just locks, modifies, and unlocks each object. Then have your updating thread join on all its child threads before returning. This punts the problem to the runtime's schedule, and it's free to schedule those child threads any way it can for best performance.
You could do this in langauges with heavier threads, but the spawn and join might have too much overhead (though thread pooling might mitigate some of this).

I don't know if there's a standard way to do this. However, I would implement this something like the following:
do
{
if (!updatedA && mutexA.tryLock())
{
scope(exit) mutexA.unlock();
updateA();
updatedA = true;
}
if (!updatedB && mutexB.tryLock())
{
scope(exit) mutexB.unlock();
updateB();
updatedB = true;
}
}
while (!(updatedA && updatedB));
Some clever metaprogramming could probably cut down the repetition, but I leave that as an exercise for you.

Sorry if I'm being naive, but do you not just Synchronize on objects to make the concerns independent?
e.g.
public Object lock1 = new Object; // access to resource 1
public Object lock2 = new Object; // access to resource 2
updateStructure1() {
synchronized( lock1 ) {
...
}
}
updateStructure2() {
synchronized( lock2 ) {
...
}
}

To my knowledge, there is not a standard way to accomplish this, and you'll have to get your hands dirty.
To paraphrase your requirements, you have a set of data structures, and you need to do work on them, but not in any particular order. You only want to block waiting on a data structure if all other objects are blocked. Here's the pseudocode I would base my solution on:
work = unshared list of objects that need updating
while work is not empty:
found = false
for each obj in work:
try locking obj
if successful:
remove obj from work
found = true
obj.update()
unlock obj
if !found:
// Everything is locked, so we have to wait
obj = randomly pick an object from work
remove obj from work
lock obj
obj.update()
unlock obj
An updating thread will only block if it finds that all objects it needs to use are locked. Then it must wait on something, so it just picks one and locks it. Ideally, it would pick the object that will be unlocked earliest, but there's no simple way of telling that.
Also, it's conceivable that an object might become free while the updater is in the try loop and so the updater would skip it. But if the amount of work you're doing is large enough, relative to the cost of iterating through that loop, the false conflict should be rare, and it would only matter in cases of extremely high contention.

I don't know any "standard" way of doing this, sorry. So this below is just a ThreadGroup, abstracted by a Swarm-class, that »hacks» at a job list until all are done, round-robin style, and makes sure that as many threads as possible are used. I don't know how to do this without a job list.
Disclaimer: I'm very new to D, and concurrency programming, so the code is rather amateurish. I saw this more as a fun exercise. (I'm too dealing with some concurrency stuff.) I also understand that this isn't quite what you're looking for. If anyone has any pointers I'd love to hear them!
import core.thread,
core.sync.mutex,
std.c.stdio,
std.stdio;
class Swarm{
ThreadGroup group;
Mutex mutex;
auto numThreads = 1;
void delegate ()[int] jobs;
this(void delegate()[int] aJobs, int aNumThreads){
jobs = aJobs;
numThreads = aNumThreads;
group = new ThreadGroup;
mutex = new Mutex();
}
void runBlocking(){
run();
group.joinAll();
}
void run(){
foreach(c;0..numThreads)
group.create( &swarmJobs );
}
void swarmJobs(){
void delegate () myJob;
do{
myJob = null;
synchronized(mutex){
if(jobs.length > 0)
foreach(i,job;jobs){
myJob = job;
jobs.remove(i);
break;
}
}
if(myJob)
myJob();
}while(myJob)
}
}
class Jobs{
void job1(){
foreach(c;0..1000){
foreach(j;0..2_000_000){}
writef("1");
fflush(core.stdc.stdio.stdout);
}
}
void job2(){
foreach(c;0..1000){
foreach(j;0..1_000_000){}
writef("2");
fflush(core.stdc.stdio.stdout);
}
}
}
void main(){
auto jobs = new Jobs();
void delegate ()[int] jobsList =
[1:&jobs.job1,2:&jobs.job2,3:&jobs.job1,4:&jobs.job2];
int numThreads = 2;
auto swarm = new Swarm(jobsList,numThreads);
swarm.runBlocking();
writefln("end");
}

There's no standard solution but rather a class of standard solutions depending on your needs.
http://en.wikipedia.org/wiki/Scheduling_algorithm

Related

V8 Garbage Collection Differs For ObjectTemplates and Objects Created With Them

V8's garbage collection seems to easily clean up as it goes a Local<T> value where T anything stored in the Local, however if you create an ObjectTemplate and then create an instance of that Object, v8 will wait to clean up the memory. Consider the following example where the resident set size remains stable throughout program execution:
Isolate* isolate = Isolate::New(create_params);
Persistent<Context> *context= ContextNew(isolate); // creates a persistent context
for(int i = 1 ; i <= 1000000; i ++ ) {
isolate->Enter();
EnterContext(isolate, context); // enters the context
{
HandleScope handle_scope(isolate);
Local<Object> result = Object::New(isolate);
}
ExitContext(isolate, context);
isolate->Exit();
}
Above, all we do is create a new Object in a loop, and then handle_scope goes out of scope and it looks like the Local values allocated are garbage collected right away as the residential set size remains steady. However, there is an issue when this object is created through an ObjectTemplate that is also created in the loop:
Isolate* isolate = Isolate::New(create_params);
Persistent<Context> *context= ContextNew(isolate); // creates a persistent context
for(int i = 1 ; i <= 1000000; i ++ ) {
isolate->Enter();
EnterContext(isolate, context); // enters the context
{
HandleScope handle_scope(isolate);
Local<Object> result;
Local<ObjectTemplate> templ = ObjectTemplate::New(isolate);
if (!templ->NewInstance(context->Get(isolate)).ToLocal(&result)) { exit(1); }
}
ExitContext(isolate, context);
isolate->Exit();
}
Here, the resident set size increases linearly until an unnecessary amount of ram is used for such a small program. Just looking to understand what is happening here. Sorry for the long explanation, i tried to keep it short and to the point :p. Thanks in advance!
V8 assumes that ObjectTemplates are long-lived and hence allocates them in the "old generation" part of the heap, where it takes longer for them to get collected by a (comparatively slow and rare) full GC cycle -- if the assumption was right and they actually are long-lived, this is an overall performance win. Objects themselves, on the other hand, are allocated in the "young generation", where they are quick and easy to collect by the (comparatively frequent) young-generation GC cycles.
If you run with --trace-gc you should see this explanation confirmed.

JAVA 8 Extract predicates as fields or methods?

What is the cleaner way of extracting predicates which will have multiple uses. Methods or Class fields?
The two examples:
1.Class Field
void someMethod() {
IntStream.range(1, 100)
.filter(isOverFifty)
.forEach(System.out::println);
}
private IntPredicate isOverFifty = number -> number > 50;
2.Method
void someMethod() {
IntStream.range(1, 100)
.filter(isOverFifty())
.forEach(System.out::println);
}
private IntPredicate isOverFifty() {
return number -> number > 50;
}
For me, the field way looks a little bit nicer, but is this the right way? I have my doubts.
Generally you cache things that are expensive to create and these stateless lambdas are not. A stateless lambda will have a single instance created for the entire pipeline (under the current implementation). The first invocation is the most expensive one - the underlying Predicate implementation class will be created and linked; but this happens only once for both stateless and stateful lambdas.
A stateful lambda will use a different instance for each element and it might make sense to cache those, but your example is stateless, so I would not.
If you still want that (for reading purposes I assume), I would do it in a class Predicates let's assume. It would be re-usable across different classes as well, something like this:
public final class Predicates {
private Predicates(){
}
public static IntPredicate isOverFifty() {
return number -> number > 50;
}
}
You should also notice that the usage of Predicates.isOverFifty inside a Stream and x -> x > 50 while semantically the same, will have different memory usages.
In the first case, only a single instance (and class) will be created and served to all clients; while the second (x -> x > 50) will create not only a different instance, but also a different class for each of it's clients (think the same expression used in different places inside your application). This happens because the linkage happens per CallSite - and in the second case the CallSite is always different.
But that is something you should not rely on (and probably even consider) - these Objects and classes are fast to build and fast to remove by the GC - whatever fits your needs - use that.
To answer, it's better If you expand those lambda expressions for old fashioned Java. You can see now, these are two ways we used in our codes. So, the answer is, it all depends how you write a particular code segment.
private IntPredicate isOverFifty = new IntPredicate<Integer>(){
public void test(number){
return number > 50;
}
};
private IntPredicate isOverFifty() {
return new IntPredicate<Integer>(){
public void test(number){
return number > 50;
}
};
}
1) For field case you will have always allocated predicate for each new your object. Not a big deal if you have a few instances, likes, service. But if this is a value object which can be N, this is not good solution. Also keep in mind that someMethod() may not be called at all. One of possible solution is to make predicate as static field.
2) For method case you will create the predicate once every time for someMethod() call. After GC will discard it.

lock-free synchronization, fences and memory order (store operation with acquire semantics)

I am migrating a project that was run on bare-bone to linux, and need to eliminate some {disable,enable}_scheduler calls. :)
So I need a lock-free sync solution in a single writer, multiple readers scenario, where the writer thread cannot be blocked. I came up with the following solution, which does not fit to the usual acquire-release ordering:
class RWSync {
std::atomic<int> version; // incremented after every modification
std::atomic_bool invalid; // true during write
public:
RWSync() : version(0), invalid(0) {}
template<typename F> void sync(F lambda) {
int currentVersion;
do {
do { // wait until the object is valid
currentVersion = version.load(std::memory_order_acquire);
} while (invalid.load(std::memory_order_acquire));
lambda();
std::atomic_thread_fence(std::memory_order_seq_cst);
// check if something changed
} while (version.load(std::memory_order_acquire) != currentVersion
|| invalid.load(std::memory_order_acquire));
}
void beginWrite() {
invalid.store(true, std::memory_order_relaxed);
std::atomic_thread_fence(std::memory_order_seq_cst);
}
void endWrite() {
std::atomic_thread_fence(std::memory_order_seq_cst);
version.fetch_add(1, std::memory_order_release);
invalid.store(false, std::memory_order_release);
}
}
I hope the intent is clear: I wrap the modification of a (non-atomic) payload between beginWrite/endWrite, and read the payload only inside the lambda function passed to sync().
As you can see, here I have an atomic store in beginWrite() where no writes after the store operation can be reordered before the store. I did not find suitable examples, and I am not experienced in this field at all, so I'd like some confirmation that it is OK (verification through testing is not easy either).
Is this code race-free and work as I expect?
If I use std::memory_order_seq_cst in every atomic operation, can I omit the fences? (Even if yes, I guess the performance would be worse)
Can I drop the fence in endWrite()?
Can I use memory_order_acq_rel in the fences? I don't really get the difference -- the single total order concept is not clear to me.
Is there any simplification / optimization opportunity?
+1. I happily accept any better idea as the name of this class :)
The code is basically correct.
Instead of having two atomic variables (version and invalid) you may use single version variable with semantic "Odd values are invalid". This is known as "sequential lock" mechanism.
Reducing number of atomic variables simplifies things a lot:
class RWSync {
// Incremented before and after every modification.
// Odd values mean that object in invalid state.
std::atomic<int> version;
public:
RWSync() : version(0) {}
template<typename F> void sync(F lambda) {
int currentVersion;
do {
currentVersion = version.load(std::memory_order_seq_cst);
// This may reduce calls to lambda(), nothing more
if(currentVersion | 1) continue;
lambda();
// Repeat until something changed or object is in an invalid state.
} while ((currentVersion | 1) ||
version.load(std::memory_order_seq_cst) != currentVersion));
}
void beginWrite() {
// Writer may read version with relaxed memory order
currentVersion = version.load(std::memory_order_relaxed);
// Invalidation requires sequential order
version.store(currentVersion + 1, std::memory_order_seq_cst);
}
void endWrite() {
// Writer may read version with relaxed memory order
currentVersion = version.load(std::memory_order_relaxed);
// Release order is sufficient for mark an object as valid
version.store(currentVersion + 1, std::memory_order_release);
}
};
Note the difference in memory orders in beginWrite() and endWrite():
endWrite() makes sure that all previous object's modifications have been completed. It is sufficient to use release memory order for that.
beginWrite() makes sure that reader will detect object being in invalid state before any futher object's modification is started. Such garantee requires seq_cst memory order. Because of that reader uses seq_cst memory order too.
As for fences, it is better to incorporate them into previous/futher atomic operation: compiler knows how to make the result fast.
Explanations of some modifications of original code:
1) Atomic modification like fetch_add() is intended for cases, when concurrent modifications (like another fetch_add()) are possible. For correctness, such modifications use memory locking or other very time-costly architecture-specific things.
Atomic assignment (store()) does not use memory locking, so it is cheaper than fetch_add(). You may use such assignment because concurrent modifications are not possible in your case (reader does not modify version).
2) Unlike to release-acquire semantic, which differentiate load and store operations, sequential consistency (memory_order_seq_cst) is applicable to every atomic access, and provide total order between these accesses.
The accepted answer is not correct. I guess the code should be something like "currentVersion & 1" instead of "currentVersion | 1". And subtler mistake is that, reader thread can go into lambda(), and after that, the write thread could run beginWrite() and write value to non-atomic variable. In this situation, write action in payload and read action in payload haven't happens-before relationship. concurrent access (without happens-before relationship) to non-atomic variable is a data race. Note that, single total order of memory_order_seq_cst does not means the happens-before relationship; they are consistent, but two kind of things.

C++ memory management patterns for objects used in callback chains

A couple codebases I use include classes that manually call new and delete in the following pattern:
class Worker {
public:
void DoWork(ArgT arg, std::function<void()> done) {
new Worker(std::move(arg), std::move(done)).Start();
}
private:
Worker(ArgT arg, std::function<void()> done)
: arg_(std::move(arg)),
done_(std::move(done)),
latch_(2) {} // The error-prone Latch interface isn't the point of this question. :)
void Start() {
Async1(<args>, [=]() { this->Method1(); });
}
void Method1() {
StartParallel(<args>, [=]() { this->latch_.count_down(); });
StartParallel(<other_args>, [=]() { this->latch_.count_down(); });
latch_.then([=]() { this->Finish(); });
}
void Finish() {
done_();
// Note manual memory management!
delete this;
}
ArgT arg_
std::function<void()> done_;
Latch latch_;
};
Now, in modern C++, explicit delete is a code smell, as, to some extent is delete this. However, I think this pattern (creating an object to represent a chunk of work managed by a callback chain) is fundamentally a good, or at least not a bad, idea.
So my question is, how should I rewrite instances of this pattern to encapsulate the memory management?
One option that I don't think is a good idea is storing the Worker in a shared_ptr: fundamentally, ownership is not shared here, so the overhead of reference counting is unnecessary. Furthermore, in order to keep a copy of the shared_ptr alive across the callbacks, I'd need to inherit from enable_shared_from_this, and remember to call that outside the lambdas and capture the shared_ptr into the callbacks. If I ever wrote the simple code using this directly, or called shared_from_this() inside the callback lambda, the object could be deleted early.
I agree that delete this is a code smell, and to a lesser extent delete on its own. But I think that here it is a natural part of continuation-passing style, which (to me) is itself something of a code smell.
The root problem is that the design of this API assumes unbounded control-flow: it acknowledges that the caller is interested in what happens when the call completes, but signals that completion via an arbitrarily-complex callback rather than simply returning from a synchronous call. Better to structure it synchronously and let the caller determine an appropriate parallelization and memory-management regime:
class Worker {
public:
void DoWork(ArgT arg) {
// Async1 is a mistake; fix it later. For now, synchronize explicitly.
Latch async_done(1);
Async1(<args>, [&]() { async_done.count_down(); });
async_done.await();
Latch parallel_done(2);
RunParallel([&]() { DoStuff(<args>); parallel_done.count_down(); });
RunParallel([&]() { DoStuff(<other_args>); parallel_done.count_down(); };
parallel_done.await();
}
};
On the caller-side, it might look something like this:
Latch latch(tasks.size());
for (auto& task : tasks) {
RunParallel([=]() { DoWork(<args>); latch.count_down(); });
}
latch.await();
Where RunParallel can use std::thread or whatever other mechanism you like for dispatching parallel events.
The advantage of this approach is that object lifetimes are much simpler. The ArgT object lives for exactly the scope of the DoWork call. The arguments to DoWork live exactly as long as the closures containing them. This also makes it much easier to add return-values (such as error codes) to DoWork calls: the caller can just switch from a latch to a thread-safe queue and read the results as they complete.
The disadvantage of this approach is that it requires actual threading, not just boost::asio::io_service. (For example, the RunParallel calls within DoWork() can't block on waiting for the RunParallel calls from the caller side to return.) So you either have to structure your code into strictly-hierarchical thread pools, or you have to allow a potentially-unbounded number of threads.
One option is that the delete this here is not a code smell. At most, it should be wrapped into a small library that would detect if all the continuation callbacks were destroyed without calling done_().

Timing of parallel actions using the Task Parallel Library in C#

I am running some experiments, timing them and comparing the times to find the best "algorithm". The question that came up was if running the tasks in parallel would make the relative runningtimes of the experiments wrong and if I would get more representative results by running them sequentially. Here is a (simplified) version of the code:
public static void RunExperient(IEnumerable<Action> experiments)
{
Parallel.ForEach(experiments, experiment =>
{
var sw = Stopwatch.StartNew(); //line 1
experiment(); //line 2
sw.Stop(); //line 3
Console.WriteLine(#"Time was {0}", sw.ElapsedMilliseconds);
});
}
My questions are about what is happening "behind the scenes":
When a task has started, is it possible that the OS or the framework can suspend the task during its execution and continue on later making the running time of the experiment all wrong?
Would I get more representative results by running the experiments sequentially?
That depends on the machine that you are running on and what the experiments do, but generally the answer is yes, they may affect one another. Mainly through resource starvation. Here's an example:
public class Piggy {
public void GreedyExperiment() {
Thread.Priority = ThreadPriority.Highest;
for (var i=0;i<1000000000;i++) {
var j = Math.Sqrt(i / 5);
}
}
}
That's going to do a tight loop on a high priority thread, which will basically consume one processor until it is done. If you only have one processor in the machine and TPL decides to schedule two experiments on it, the other one is going to be starved for CPU time.

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