I have the following code that is doing weird things with one of my student's modules:
forall i in cpus.domain {
cpus[i] = new CPU(schedulerToCPUs, "" + i);
begin {
cpus[i].start();
}
writeln("launched CPU: ", i);
}
writeln("done launching CPUs!");
It is printinq out that it has launched all the CPUs, but it is not reaching the writeln() at the end. This only happens when the size of cpus.domain is above 1. (It seems to be okay when the number of CPUs is large, like 50. I'm testing mostly on 3.)
The CPU class is my own design. This is only happening for this one student's project. I'm not sure what could be causing this problem. I would really like some advice for where I can start looking.
Related
I am currently developing a stimuli provider for the brain's visual cortex as a part of a university project. The program is to (preferably) be written in c++, utilising visual studio and OpenCV. The way it is supposed to work is that the program creates a number of threads, accordingly to the amount of different frequencies, each running a timer for their respective frequency.
The code looks like this so far:
void timerThread(void *param) {
t *args = (t*)param;
int id = args->data1;
float freq = args->data2;
unsigned long period = round((double)1000 / (double)freq)-1;
while (true) {
Sleep(period);
show[id] = 1;
Sleep(period);
show[id] = 0;
}
}
It seems to work okay for some of the frequencies, but others vary quite a lot in frame rate. I have tried to look into creating my own timing function, similar to what is done in Arduino's "blinkWithoutDelay" function, though this worked very badly. Also, I have tried with the waitKey() function, this worked quite like the Sleep() function used now.
Any help would be greatly appreciated!
You should use timers instead of "sleep" to fix this, as sometimes the loop may take more or less time to complete.
Restart the timer at the start of the loop and take its value right before the reset- this'll give you the time it took for the loop to complete.
If this time is greater than the "period" value, then it means you're late, and you need to execute right away (and even lower the period for the next loop).
Otherwise, if it's lower, then it means you need to wait until it is greater.
I personally dislike sleep, and instead constantly restart the timer until it's greater.
I suggest looking into "fixed timestep" code, such as the one below. You'll need to put this snippet of code on every thread with varying values for the period (ns) and put your code where "doUpdates()" is.
If you need a "timer" library, since I don't know OpenCV, I recommend SFML (SFML's timer docs).
The following code is from here:
long int start = 0, end = 0;
double delta = 0;
double ns = 1000000.0 / 60.0; // Syncs updates at 60 per second (59 - 61)
while (!quit) {
start = timeAsMicro();
delta+=(double)(start - end) / ns; // You can skip dividing by ns here and do "delta >= ns" below instead //
end = start;
while (delta >= 1.0) {
doUpdates();
delta-=1.0;
}
}
Please mind the fact that in this code, the timer is never reset.
(This may not be completely accurate but is the best assumption I can make to fix your problem given the code you've presented)
Question summary: all four cores used when running a single threaded programme. Why?
Details: I have written a non-parallelised programme in Xcode (C++). I was in the process of parallelising it, and wanted to see whether what I was doing was actually resulting in more cores being used. To that end I used Instruments to look at the core usage. To my surprise, while my application is single threaded, all four cores were being utilised.
To test whether it changed the performance, I dialled down the number of cores available to 1 (you can do it in Instruments, preferences) and the speed wasn't reduced at all. So (as I knew) the programme isn't parallelised in any way.
I can't find any information on what it means to use multiple cores to perform single threaded tasks. Am I reading the Instruments output wrong? Or is the single-threaded process being shunted between different cores for some reason (like changing lanes on a road instead of driving in two lanes at once - i.e. actual parallelisation)?
Thanks for any insight anyone can give on this.
EDIT with MWE (apologies for not doing this initially).
The following is C++ code that finds primes under 500,000, compiled in Xcode.
#include <iostream>
int main(int argc, const char * argv[]) {
clock_t start, end;
double runTime;
start = clock();
int i, num = 1, primes = 0;
int num_max = 500000;
while (num <= num_max) {
i = 2;
while (i <= num) {
if(num % i == 0)
break;
i++;
}
if (i == num){
primes++;
std::cout << "Prime: " << num << std::endl;
}
num++;
}
end = clock();
runTime = (end - start) / (double) CLOCKS_PER_SEC;
std::cout << "This machine calculated all " << primes << " under " << num_max << " in " << runTime << " seconds." << std::endl;
return 0;
}
This runs in 36s or thereabouts on my machine, as shown by the final out and my phone's stopwatch. When I profile it (using instruments launched from within Xcode) it gives a run-time of around 28s. The following image shows the core usage.
instruments showing core usage with all 4 cores (with hyper threading)
Now I reduce number of available cores to 1. Re-running from within the profiler (pressing the record button), it says a run-time of 29s; a picture is shown below.
instruments output with only 1 core available
That would accord with my theory that more cores doesn't improve performance for a single thread programme! Unfortunately, when I actually time the programme with my phone, the above took about 1 minute 30s, so there is a meaningful performance gain from having all cores switched on.
One thing that is really puzzling me, is that, if you leave the number of cores at 1, go back to Xcode and run the program, it again says it takes about 33s, but my phone says it takes 1 minute 50s. So changing the cores is doing something to the internal clock (perhaps).
Hopefully that describes the problem fully. I'm running on a 2015 15 inch MBP, with 2.2GHz i7 quad core processor. Xcode 7.3.1
I want to premise your answer lacks a lots of information in order to proceed an accurate diagnostic. Anyway I'll try to explain you the most common reason IHMO, supposing you application doesn't use 3-rd part component which perform in a multi-thread way.
I think that could be a result of scheduler effect. I'm going to explain what I mean.
Each core of the processor takes a process in the system and executed it for a "short" amount of time. This is the most common solution in desktop operative system.
Your process is executed on a single core for this amount of time and then stopped in order to allow other process to continue. When your same process is resumed it could be executed in another core (always one core, but a different one). So a poor precise task manager with a low resolution time could register the utilization of all cores, even if it does not.
In order to verify whether the cause could be that, I suggest you to see the amount of CPU % used in the time your application is running. Indeed in case of a single thread application the CPU should be about 1/#numberCore , in your case 25%.
If it's a release build your compiler may be vectorising parallelise your code. Also libraries you link against, say the standard library for example, may be threaded or vectorised.
Hello I'm brand new to go (and concurrent programming in general :() and trying to distribute a slow computation to a pool of workers.
http://play.golang.org/p/lTv4Tm75A4
func main() {
test := []int{1, 2, 3, 4, 5, 6, 7, 8, 9, 10}
answer := getSmallestMultiple(test)
fmt.Println(answer)
}
I am trying to find the smallest number that is evenly divisible by all the numbers in test.
I have created a pool of workers and am sending them values until one of the goroutines finds a number that can be evenly divided by all the numbers in test
for w := 0; w < 100; w++ {
go divisibleByAllNumbers(&numbers, jobs, answer)
}
go func() {
for i := max; ; i += max {
fmt.Printf("Sending # %d\n", i)
jobs <- i
}
}()
The program seems to be running at the same speed despite how many workers I start. I have tried many number of workers and it always takes the same number of seconds to run, which seems like the work is not being done concurrently at all.
Each worker is consuming work from the queue using range:
for j := range jobs {}
And i was hoping the more processes consuming off the jobs channel the faster the program would execute.
I have also tried different values for the jobs := make(chan int) buffer value
I have stared at this all day and was hoping someone could see what the issue is. I would expect the more workers I add the faster the computation takes but am not experiencing that. I'm sure I"m missing some key concepts,
Thank you
http://golang.org/doc/effective_go.html#parallel
The current implementation of the Go runtime will not parallelize this code by default. It dedicates only a single core to user-level processing. An arbitrary number of goroutines can be blocked in system calls, but by default only one can be executing user-level code at any time. It should be smarter and one day it will be smarter, but until it is if you want CPU parallelism you must tell the run-time how many goroutines you want executing code simultaneously. There are two related ways to do this. Either run your job with environment variable GOMAXPROCS set to the number of cores to use or import the runtime package and call runtime.GOMAXPROCS(NCPU). A helpful value might be runtime.NumCPU(), which reports the number of logical CPUs on the local machine. Again, this requirement is expected to be retired as the scheduling and run-time improve.
I have been developing an individual base model. All you need to know is that individuals are born, reproduce and die. I have a GUI in which i can see these processes happening.
I have a mac pro, with 8 cores and 16GB ram.
Considering that the simulation will have to be repeated a few times to get error bars, etc, I thought i could run the main class and then have separate simulations (all run from the same program) ran on separate cores. Simple. Each parallel simulation would have no knowledge of the other simulations, hence no need for synchronization blocks.
When the main method is run, it invokes the constructor of the main class - which creates the other objects and the simulation begins. Hence - to parallelise - I created a fixed thread pool which would all separately invoke the main class constructor and multiple (well, 8, the number of cores) simulations.
BUT - it is running as slow as if I was running the simulations in serial. The animation in the GUIs for each simulation are updated in order, not simultaneously.
In fact, if I run the program 8 times simultaneously from the command line (and place in the background with '&') it is much faster and behaves much more like I would have hoped. Which is irritating!
At the start of the simulation some IO operations are performed to read in data about the individuals, but only at the start.
Interestingly, the first objects to be created by the `parallel' processes were made at the same memory addresses - but I don't think that is a problem.
If anybody has any insight into this lack of performance from the java concurrency tools, why the program appears to be running in serial and why simply running the main method from the command line 8 times is better than attempting to parallelise that would be most helpful.
Because to be frank I am losing faith in java's parallelisation capabilities.
Cheers
James
noOfProcessors = (byte)Runtime.getRuntime().availableProcessors();
ExecutorService eservice = Executors.newFixedThreadPool( noOfProcessors );
List<Future> futuresList = new ArrayList<Future>();
for( int i = 0; i < noOfProcessors; i++ ){
futuresList.add( eservice.submit( new simulation() ) );
}//end for
for( Future future : futuresList ){
try{
future.get();
}catch( InterruptedException ex ){
Logger.getLogger( simPanel.class.getName() ).log( Level.SEVERE, null, ex );
System.exit( 1 );
}catch( ExecutionException ex ){
Logger.getLogger( simPanel.class.getName() ).log( Level.SEVERE, null, ex );
System.exit( 1 );
}//end try-catch
}//end for loop
While not too familiar with Java's Executors class, the serial behaviour seems to indicate that your thread pool is running all threads on the same processor. Perhaps it has something to do with how the JVM handles threads? Anyway, see if you can create separate processes in Java and see if that makes a difference.
I have a 'server' program that updates many linked lists in shared memory in response to external events. I want client programs to notice an update on any of the lists as quickly as possible (lowest latency). The server marks a linked list's node's state_ as FILLED once its data is filled in and its next pointer has been set to a valid location. Until then, its state_ is NOT_FILLED_YET. I am using memory barriers to make sure that clients don't see the state_ as FILLED before the data within is actually ready (and it seems to work, I never see corrupt data). Also, state_ is volatile to be sure the compiler doesn't lift the client's checking of it out of loops.
Keeping the server code exactly the same, I've come up with 3 different methods for the client to scan the linked lists for changes. The question is: Why is the 3rd method fastest?
Method 1: Round robin over all the linked lists (called 'channels') continuously, looking to see if any nodes have changed to 'FILLED':
void method_one()
{
std::vector<Data*> channel_cursors;
for(ChannelList::iterator i = channel_list.begin(); i != channel_list.end(); ++i)
{
Data* current_item = static_cast<Data*>(i->get(segment)->tail_.get(segment));
channel_cursors.push_back(current_item);
}
while(true)
{
for(std::size_t i = 0; i < channel_list.size(); ++i)
{
Data* current_item = channel_cursors[i];
ACQUIRE_MEMORY_BARRIER;
if(current_item->state_ == NOT_FILLED_YET) {
continue;
}
log_latency(current_item->tv_sec_, current_item->tv_usec_);
channel_cursors[i] = static_cast<Data*>(current_item->next_.get(segment));
}
}
}
Method 1 gave very low latency when then number of channels was small. But when the number of channels grew (250K+) it became very slow because of looping over all the channels. So I tried...
Method 2: Give each linked list an ID. Keep a separate 'update list' to the side. Every time one of the linked lists is updated, push its ID on to the update list. Now we just need to monitor the single update list, and check the IDs we get from it.
void method_two()
{
std::vector<Data*> channel_cursors;
for(ChannelList::iterator i = channel_list.begin(); i != channel_list.end(); ++i)
{
Data* current_item = static_cast<Data*>(i->get(segment)->tail_.get(segment));
channel_cursors.push_back(current_item);
}
UpdateID* update_cursor = static_cast<UpdateID*>(update_channel.tail_.get(segment));
while(true)
{
ACQUIRE_MEMORY_BARRIER;
if(update_cursor->state_ == NOT_FILLED_YET) {
continue;
}
::uint32_t update_id = update_cursor->list_id_;
Data* current_item = channel_cursors[update_id];
if(current_item->state_ == NOT_FILLED_YET) {
std::cerr << "This should never print." << std::endl; // it doesn't
continue;
}
log_latency(current_item->tv_sec_, current_item->tv_usec_);
channel_cursors[update_id] = static_cast<Data*>(current_item->next_.get(segment));
update_cursor = static_cast<UpdateID*>(update_cursor->next_.get(segment));
}
}
Method 2 gave TERRIBLE latency. Whereas Method 1 might give under 10us latency, Method 2 would inexplicably often given 8ms latency! Using gettimeofday it appears that the change in update_cursor->state_ was very slow to propogate from the server's view to the client's (I'm on a multicore box, so I assume the delay is due to cache). So I tried a hybrid approach...
Method 3: Keep the update list. But loop over all the channels continuously, and within each iteration check if the update list has updated. If it has, go with the number pushed onto it. If it hasn't, check the channel we've currently iterated to.
void method_three()
{
std::vector<Data*> channel_cursors;
for(ChannelList::iterator i = channel_list.begin(); i != channel_list.end(); ++i)
{
Data* current_item = static_cast<Data*>(i->get(segment)->tail_.get(segment));
channel_cursors.push_back(current_item);
}
UpdateID* update_cursor = static_cast<UpdateID*>(update_channel.tail_.get(segment));
while(true)
{
for(std::size_t i = 0; i < channel_list.size(); ++i)
{
std::size_t idx = i;
ACQUIRE_MEMORY_BARRIER;
if(update_cursor->state_ != NOT_FILLED_YET) {
//std::cerr << "Found via update" << std::endl;
i--;
idx = update_cursor->list_id_;
update_cursor = static_cast<UpdateID*>(update_cursor->next_.get(segment));
}
Data* current_item = channel_cursors[idx];
ACQUIRE_MEMORY_BARRIER;
if(current_item->state_ == NOT_FILLED_YET) {
continue;
}
found_an_update = true;
log_latency(current_item->tv_sec_, current_item->tv_usec_);
channel_cursors[idx] = static_cast<Data*>(current_item->next_.get(segment));
}
}
}
The latency of this method was as good as Method 1, but scaled to large numbers of channels. The problem is, I have no clue why. Just to throw a wrench in things: if I uncomment the 'found via update' part, it prints between EVERY LATENCY LOG MESSAGE. Which means things are only ever found on the update list! So I don't understand how this method can be faster than method 2.
The full, compilable code (requires GCC and boost-1.41) that generates random strings as test data is at: http://pastebin.com/0kuzm3Uf
Update: All 3 methods are effectively spinlocking until an update occurs. The difference is in how long it takes them to notice the update has occurred. They all continuously tax the processor, so that doesn't explain the speed difference. I'm testing on a 4-core machine with nothing else running, so the server and the client have nothing to compete with. I've even made a version of the code where updates signal a condition and have clients wait on the condition -- it didn't help the latency of any of the methods.
Update2: Despite there being 3 methods, I've only tried 1 at a time, so only 1 server and 1 client are competing for the state_ member.
Hypothesis: Method 2 is somehow blocking the update from getting written by the server.
One of the things you can hammer, besides the processor cores themselves, is your coherent cache. When you read a value on a given core, the L1 cache on that core has to acquire read access to that cache line, which means it needs to invalidate the write access to that line that any other cache has. And vice versa to write a value. So this means that you're continually ping-ponging the cache line back and forth between a "write" state (on the server-core's cache) and a "read" state (in the caches of all the client cores).
The intricacies of x86 cache performance are not something I am entirely familiar with, but it seems entirely plausible (at least in theory) that what you're doing by having three different threads hammering this one memory location as hard as they can with read-access requests is approximately creating a denial-of-service attack on the server preventing it from writing to that cache line for a few milliseconds on occasion.
You may be able to do an experiment to detect this by looking at how long it takes for the server to actually write the value into the update list, and see if there's a delay there corresponding to the latency.
You might also be able to try an experiment of removing cache from the equation, by running everything on a single core so the client and server threads are pulling things out of the same L1 cache.
I don't know if you have ever read the Concurrency columns from Herb Sutter. They are quite interesting, especially when you get into the cache issues.
Indeed the Method2 seems better here because the id being smaller than the data in general would mean that you don't have to do round-trips to the main memory too often (which is taxing).
However, what can actually happen is that you have such a line of cache:
Line of cache = [ID1, ID2, ID3, ID4, ...]
^ ^
client server
Which then creates contention.
Here is Herb Sutter's article: Eliminate False Sharing. The basic idea is simply to artificially inflate your ID in the list so that it occupies one line of cache entirely.
Check out the other articles in the serie while you're at it. Perhaps you'll get some ideas. There's a nice lock-free circular buffer I think that could help for your update list :)
I've noticed in both method 1 and method 3 you have a line, ACQUIRE_MEMORY_BARRIER, which I assume has something to do with multi-threading/race conditions?
Either way, method 2 doesn't have any sleeps which means the following code...
while(true)
{
if(update_cursor->state_ == NOT_FILLED_YET) {
continue;
}
is going to hammer the processor. The typical way to do this kind of producer/consumer task is to use some kind of semaphore to signal to the reader that the update list has changed. A search for producer/consumer multi threading should give you a large number of examples. The main idea here is that this allows the thread to go to sleep while it's waiting for the update_cursor->state to change. This prevents this thread from stealing all the cpu cycles.
The answer was tricky to figure out, and to be fair would be hard with the information I presented though if anyone actually compiled the source code I provided they'd have a fighting chance ;) I said that "found via update list" was printed after every latency log message, but this wasn't actually true -- it was only true for as far as I could scrollback in my terminal. At the very beginning there were a slew of updates found without using the update list.
The issue is that between the time when I set my starting point in the update list and my starting point in each of the data lists, there is going to be some lag because these operations take time. Remember, the lists are growing the whole time this is going on. Consider the simplest case where I have 2 data lists, A and B. When I set my starting point in the update list there happen to be 60 elements in it, due to 30 updates on list A and 30 updates on list B. Say they've alternated:
A
B
A
B
A // and I start looking at the list here
B
But then after I set the update list to there, there are a slew of updates to B and no updates to A. Then I set my starting places in each of the data lists. My starting points for the data lists are going to be after that surge of updates, but my starting point in the update list is before that surge, so now I'm going to check for a bunch of updates without finding them. The mixed approach above works best because by iterating over all the elements when it can't find an update, it quickly closes the temporal gap between where the update list is and where the data lists are.