Essentially I have 2 "while True:" loops in my code. Both of the loops are right at the end. However when I run the code, only the first while True: loop gets run, and the second one gets ignored.
For example:
while True:
print "hi"
while True:
print "bye"
Here, it will continuously print hi, but wont print bye at all (the actual code has a tracer.execute() for one loop, and the other is listening to a port, and they both work on their own).
Is there any way to get both loops to work at the same time independently?
Yes.A way to get both loops to work at the same time independently:
Your initial surprise was related to the nature, how Finite-State-Automata actually work.
[0]: any-processing-will-always-<START>-here
[1]: Read a next instruction
[2]: Execute the instruction
[3]: GO TO [1]
The stream of abstract instructions is being executed in a pure-[SERIAL] manner, one after another. There is no other way in the CPU since uncle Turing.
Your desire to have more streams-of-instructions run at the same time independently is called [CONCURRENT] process-scheduling.
You have several tools for achieving a wanted modus-operandi:
Read about a weaker form, using just a thread-based concurrency ( which, due to a Python-specific GIL-locking, yet executes on the physical hardware as a [CONCURRENT]-processing, but GIL-interleaving ( which was knowingly implemented as a very cheap form of a collision-avoidance for each and every case, that this [CONCURRENCY] might introduce ) will finally interleave each of the ( now ) [CONCURRENT]-streams, so as to principally avoid colliding access to any Python object at the same time. If you are fine with this execute-just-one-instruction-stream-fragment-at-a-time ( and round-robin their actual order of GIL-stepped execution ), you can live in a safe and collision-free world.
Another tool, Python may use, is the joblib.Parallel()( joblib.delayed() ), where you will have to master a bit more things to make these ( now a set of fully spawned subprocesses, each ( yes, each ) having a full-copy of python-state + all variables ( read: a lot of time and memory needed to spawn 'em ) and no mutual coordination ).
So decide about which form is just-enough for the kind of your use-case, and better check the new Amdahl's Law re-formulation carefully ( implications on costs of going distributed or parallel )
Related
I wrote a test for ZeroMQ to convince myself that it manages to map replies to the client independent from processing order, which would prove it thread safe.
It is a multi-threaded server, which just throws the received messages back at the sender. The client sends some messages from several threads and checks, if it receives the same message back. For multi-threading I use OpenMP.
That test worked fine and I wanted to move on and re-implement it with C++ bindings for ZeroMQ. And now it doesn't work in the same way anymore.
Here's the code with ZMQPP:
#include <gtest/gtest.h>
#include <zmqpp/zmqpp.hpp>
#include <zmqpp/proxy.hpp>
TEST(zmqomp, order) {
zmqpp::context ctx;
std::thread proxy([&ctx] {
zmqpp::socket dealer(ctx, zmqpp::socket_type::xrequest);
zmqpp::socket router(ctx, zmqpp::socket_type::xreply);
router.bind("tcp://*:1234");
dealer.bind("inproc://workers");
zmqpp::proxy(router, dealer);
});
std::thread worker_starter([&ctx] {
#pragma omp parallel
{
zmqpp::socket in(ctx, zmqpp::socket_type::reply);
in.connect("inproc://workers");
#pragma omp for
for (int i = 0; i < 1000; i++) {
std::string request;
in.receive(request);
in.send(request);
}
}
});
std::thread client([&ctx] {
#pragma omp parallel
{
zmqpp::socket out(ctx, zmqpp::socket_type::request);
out.connect("tcp://localhost:1234");
#pragma omp for
for (int i = 0; i < 1000; i++) {
std::string msg("Request " + std::to_string(i));
out.send(msg);
std::string reply;
out.receive(reply);
EXPECT_EQ(reply, msg);
}
}
});
client.join();
worker_starter.join();
ctx.terminate();
proxy.join();
}
The test blocks and doesn't get executed to the end. I played around with #pragmas a little bit and found out that only one change can "fix" it:
//#pragma omp parallel for
for (int i = 0; i < 250; i++) {
The code is still getting executed parallel in that case, but I have to divide the loop executions number by a number of my physical cores.
Does anybody have a clue what's going on here?
Prologue: ZeroMQ is by-definition and by-design NOT Thread-Safe.
This normally does not matter as there are some safe-guarding design practices, but situation here goes even worse, once following the proposed TEST(){...} design.
Having spent some time with ZeroMQ, your proposal headbangs due to violations on several principal things, that otherwise help distributed architectures to work smarter, than a pure SEQ of monolithic code.
ZeroMQ convinces in ( almost ) every third paragraph to avoid sharing of resources. Zero-sharing is one of the ZeroMQ's fabulous scalable performance and minimised latency maxims, so to say in short.
So one has better to avoid sharing zmq.Context() instance at all ( unless one knows pretty well, why and how the things work under the hood ).
Thus an attempt to fire 1000-times ( almost ) in parallel ( well, not a true PAR ) some flow of events onto a shared instance of zmq.Context ( the less once it was instantiated with default parameters and having none performance tuning adaptations ) will certainly suffer from doing the very opposite from what is, performance-wise and design-wise, recommended to do.
What are some of the constraints, not to headbang into?
1) Each zmq.Context() instance has a limited amount of I/O-threads, that were created during the instantiation process. Once a fair design needs some performance-tuning, it is possible to increase such number of I/O-threads and data-pumps will work that better ( sure, none amount of data-pumps will salvage a poor, the less a disastrous design / architecture of a distributed computing system. This is granted. ).
2) Each zmq.Socket() instance has an { implicit | explicit } mapping onto a respective I/O-thread ( Ref. 1) ). Once a fair design needs some increased robustness against sluggish event-loop handlings or against other adverse effects arisen from data-flow storms ( or load-balancing or you name it ), there are chances to benefit from a divide-and-conquer approach to use .setsockopt( zmq.AFFINITY, ... ) method to directly map each zmq.Socket() instance onto a respective I/O-thread, and remain thus in control of what buffering and internal queues are fighting for which resources during the real operations. In any case, where a total amount of threads goes over the localhost number of cores, the just-CONCURRENT scheduling is obvious ( so a dream of a true PAR execution is principally and inadvertently lost. This is granted. ).
3) Each zmq.Socket() has also a pair of "Hidden Queue Devastators", called High-Watermarks. These get set either { implicitly | explicitly }, the latter being for sure a wiser manner for performance tuning. Why Devastators? Because these stabilise and protect the distributed computing systems from overflows and are permitted to simply discard each and every message above the HWM level(s) so as to protect the systems capability to run forever, even under heavy storms, spurious blasts of crippled packets or DDoS-types of attack. There are many tools for tuning this domain of ZeroMQ Context()-instance's behaviour, which go beyond the scope of this answer ( Ref.: other my posts on ZeroMQ AFFINITY benefits or the ZeroMQ API specifications used in .setsockopt() method ).
4) Each tcp:// transport-class based zmq.Socket() instance has also inherited some O/S dependent heritage. Some O/S demonstrate this risk by extended accumulation of ip-packets ( outside of any ZeroMQ control ) until some threshold got passed and thus a due design care ought be taken for such cases to avoid adverse effects on the intended application signalling / messaging dynamics and robustness against such uncontrollable ( exosystem ) buffering habits.
5) Each .recv() and .send() method call is by-definition blocking, a thing a massively distributed computing system ought never risk to enter into. Never ever. Even in a school-book example. Rather use non-blocking form of these calls. Always. This is granted.
6) Each zmq.Socket() instance ought undertake a set of careful and graceful termination steps. A preventive step of .setsockopt( zmq.LINGER, 0 ) + an explicit .close() methods are fair to be required to be included in every use-case ( and made robust to get executed irrespective of any exceptions that may get appeared. ). A poor { self- | team- }-discipline in this practice is a sure ticket into hanging up the whole application infrastructure due to just not paying due care on a mandatory resources management policy. This is a must-have part of any serious distributed computing Project. Even the school-book examples ought have this. No exceptions. No excuse. This is granted.
I can't find a neat explanation about how I'm supposed to write a piece of inline asm, and what are the problem that can possibly arise from a concurrent use of a foo function that contains asm code in it.
The problem that I see is that in asm the registers are uniquely named, and so 1 name is strictly tied to a really precise portion of your cpu, and that's a big problem if you are writing 1 piece of code that is supposed to run concurrently because you can't simply extra registers with the same name.
The other problem is that asm doesn't really uses a calling convention, you simply call registers and/or values, and sometimes calling a register implies a silent action on another register that doesn't even shows up explicitly in your code; so I can't even expect that my C/C++ function foo will be packed and sealed inside its own stack if it contains asm code .
Now with what gcc calls extended asm I can basically declare where the input and the output goes, so each function can use its own parameters "as registers" , and the pattern is the following
asm ( assembler template
: output
: input
: registers
);
Assuming that my main target for now are mathematical operations, and my function is only supposed to give a certain functionality and perform some computation ( no internal lock ), is extended asm good for concurrency ? How I should design a piece of asm that is supposed to be used by a concurrent application ?
For now I'm using gcc, but I would like a generic answer about the general asm design that I'm supposed to give to this kind of code snippets.
You seem to be misunderstanding what threading actually is. Let's consider a single-processor system first. The threads don't actually run concurrently, since there is only one unit that can successfully decode and execute them. Your operating system is only creating the illusion of running multiple threads (and processes, too) by employing scheduling inside of it : every thread, or process, is allocated a certain amount of time it gets to execute on the processor.
This is why, when threads are executed, they don't overwrite each other's registers. When a currently executed thread or process is switched, the operating system asks the processor to perform something that's called a context switch. In a nutshell, the processor saves its state when it was executing the previous task/thread/process into some memory area, which is controlled by the OS. The new task/thread/process has its context restored from the previously stored state and continues its execution. When this task/thread/process' time slice on the CPU is up, the scheduler decides which task/thread/process to resume next. The time slice is usually very small, which is why you're given the illusion of multiple streams of code running at the same time. Keep in mind that this is a very, very simplified description : refer to CPU manuals or books on operating systems for more detail.
The situation is analogous on multi-processor systems : only with the exception that, then, there is more than one unit that can execute the instructions. This is also true for multi-core processors : every one of the cores has its own set of registers. The basic stuff stays the same - the scheduler in your OS decides whether the code being executed is actually executed at the same time by multiple cores in one processor.
Thus, your concerns in this case are not valid. However, they were raised for very valid reasons. Remember that the only things that threads share is the main memory : each thread has its own registers, and its own stack.
Let me come back to the actual question about gcc's extended inline assembly. The compiler itself cannot work out which registers are modified by the assembly you wrote. That's why you need to specify it. However, it is very rare that an instruction modifies a register without you being able to control it, and it happens only with a small number of instructions - assuming that we're talking about x86. Moreover, gcc can work out the destination/source operands by itself when you want to refer to a C/C++ variable from inside the assembly. In fact, this is the preferred method, since it leaves the compiler much more room for optimization.
Consider this piece of code :
unsigned int get_cr0(void)
{
unsigned int rc;
__asm__ (
"movl %%cr0, %0\n"
: "=r"(rc)
:
:
);
return rc;
}
This function's purpose is to return the contents of the control register cr0. This is a privileged instruction, so the program will not work when you run it in user mode, but this is not important right now. See how I put %0 in the instruction, and then specified "=r"(rc) in the output list. This means that %0 will be automagically aliased by the compiler to your rc variable. You can do this for every variable you specify on the input/output list. They are numbered starting from zero, as you can see.
I can't really remember the instructions which used registers that were not encoded as operands, so I can't give you an example right now. In this case, you would need to put them on the clobber list (the last one). I'm pretty sure you can refer to this for more information.
I also can't answer anything regarding "general asm design", since this is a non-standard extension and thus varies between compilers. The 64-bit Visual Studio compilers don't support it at all, for example.
I am trying to understand the term lwt supported.
So assume I have a piece of code which connect a database and write some data: Db.write conn data. It has nothing to do with lwt yet and each write will cost 10 sec.
Now, I would like to use lwt. Can I directly code like below?
let write_all data_list = Lwt_list.iter (Db.write conn) data_list
let _ = Lwt_main.run(write_all my_data_list)
Support there are 5 data items in my_data_list, will all 5 data items be written into the database sequentially or in parallel?
Also in Lwt manually or http://ocsigen.org/tutorial/application, they say
Using Lwt is very easy and does not cause troubles, provided you never
use blocking functions (non cooperative functions). Blocking functions
can cause the entre server to hang!
I quite don't understand how to not using blocking functions. For every my own function, can I just use Lwt.return to make it lwt support?
Yes, your code is correct. The principle of lwt supported is that everything that can potentially takes time in your code should return an Lwt value.
About Lwt_list.iter, you can choose whether you want the treatment to be parallel or sequential, by choosing between iter_p and iter_s :
In iter_s f l, iter_s will call f on each elements
of l, waiting for completion between each element. On the
contrary, in iter_p f l, iter_p will call f on all
elements of l, then wait for all the threads to terminate.
About the non-blocking functions, the principle of the Light Weight Threads is that they keep running until they reach a "cooperation point", i.e. a point where the thread can be safely interrupted or has nothing to do, like in a sleep.
But you have to declare you enter a "cooperation point" before actually doing the sleep. This is why the whole Unix library has been wrapped, so that when you want to do an operation that takes time (e.g. a write), a cooperation point is automatically reached.
For your own function, if you use IOs operations from Unix, you should instead use the Lwt version (Lwt_unix.sleep instead of Unix.sleep)
I just tried the following code, but the result seems a little strange. It prints odd numbers first, and then even numbers. I'm really confused about it. I had hoped it outputs odd number and even number one after another, just like 1, 2, 3, 4... . Who can help me?
package main
import (
"fmt"
"time"
)
func main() {
go sheep(1)
go sheep(2)
time.Sleep(100000)
}
func sheep(i int) {
for ; ; i += 2 {
fmt.Println(i,"sheeps")
}
}
More than likely you are only running with one cpu thread. so it runs the first goroutine and then the second. If you tell go it can run on multiple threads then both will be able to run simultaneously provided the os has spare time on a cpu to do so. You can demonstrate this by setting GOMAXPROCS=2 before running your binary. Or you could try adding a runtime.Gosched() call in your sheep function and see if that triggers the runtime to allow the other goroutine to run.
In general though it's better not to assume ordering semantics between operations in two goroutines unless you specify specific synchronization points using a sync.Mutex or communicating between them on channels.
Unsynchronized goroutines execute in a completely undefined order. If you want to print out something like
1 sheeps
2 sheeps
3 sheeps
....
in that exact order, then goroutines are the wrong way to do it. Concurrency works well when you don't care so much about the order in which things occur.
You could impose an order in your program through synchronization (locking a mutex around the fmt.Println calls or using a channel), but it's pointless since you could more easily just write code that uses a single goroutine.
I have been watching the growing visibility of functional programming languages and features for a while. I looked into them and didn't see the reason for the appeal.
Then, recently I attended Kevin Smith's "Basics of Erlang" presentation at Codemash.
I enjoyed the presentation and learned that a lot of the attributes of functional programming make it much easier to avoid threading/concurrency issues. I understand the lack of state and mutability makes it impossible for multiple threads to alter the same data, but Kevin said (if I understood correctly) all communication takes place through messages and the mesages are processed synchronously (again avoiding concurrency issues).
But I have read that Erlang is used in highly scalable applications (the whole reason Ericsson created it in the first place). How can it be efficient handling thousands of requests per second if everything is handled as a synchronously processed message? Isn't that why we started moving towards asynchronous processing - so we can take advantage of running multiple threads of operation at the same time and achieve scalability? It seems like this architecture, while safer, is a step backwards in terms of scalability. What am I missing?
I understand the creators of Erlang intentionally avoided supporting threading to avoid concurrency problems, but I thought multi-threading was necessary to achieve scalability.
How can functional programming languages be inherently thread-safe, yet still scale?
A functional language doesn't (in general) rely on mutating a variable. Because of this, we don't have to protect the "shared state" of a variable, because the value is fixed. This in turn avoids the majority of the hoop jumping that traditional languages have to go through to implement an algorithm across processors or machines.
Erlang takes it further than traditional functional languages by baking in a message passing system that allows everything to operate on an event based system where a piece of code only worries about receiving messages and sending messages, not worrying about a bigger picture.
What this means is that the programmer is (nominally) unconcerned that the message will be handled on another processor or machine: simply sending the message is good enough for it to continue. If it cares about a response, it will wait for it as another message.
The end result of this is that each snippet is independent of every other snippet. No shared code, no shared state and all interactions coming from a a message system that can be distributed among many pieces of hardware (or not).
Contrast this with a traditional system: we have to place mutexes and semaphores around "protected" variables and code execution. We have tight binding in a function call via the stack (waiting for the return to occur). All of this creates bottlenecks that are less of a problem in a shared nothing system like Erlang.
EDIT: I should also point out that Erlang is asynchronous. You send your message and maybe/someday another message arrives back. Or not.
Spencer's point about out of order execution is also important and well answered.
The message queue system is cool because it effectively produces a "fire-and-wait-for-result" effect which is the synchronous part you're reading about. What makes this incredibly awesome is that it means lines do not need to be executed sequentially. Consider the following code:
r = methodWithALotOfDiskProcessing();
x = r + 1;
y = methodWithALotOfNetworkProcessing();
w = x * y
Consider for a moment that methodWithALotOfDiskProcessing() takes about 2 seconds to complete and that methodWithALotOfNetworkProcessing() takes about 1 second to complete. In a procedural language this code would take about 3 seconds to run because the lines would be executed sequentially. We're wasting time waiting for one method to complete that could run concurrently with the other without competing for a single resource. In a functional language lines of code don't dictate when the processor will attempt them. A functional language would try something like the following:
Execute line 1 ... wait.
Execute line 2 ... wait for r value.
Execute line 3 ... wait.
Execute line 4 ... wait for x and y value.
Line 3 returned ... y value set, message line 4.
Line 1 returned ... r value set, message line 2.
Line 2 returned ... x value set, message line 4.
Line 4 returned ... done.
How cool is that? By going ahead with the code and only waiting where necessary we've reduced the waiting time to two seconds automagically! :D So yes, while the code is synchronous it tends to have a different meaning than in procedural languages.
EDIT:
Once you grasp this concept in conjunction with Godeke's post it's easy to imagine how simple it becomes to take advantage of multiple processors, server farms, redundant data stores and who knows what else.
It's likely that you're mixing up synchronous with sequential.
The body of a function in erlang is being processed sequentially.
So what Spencer said about this "automagical effect" doesn't hold true for erlang. You could model this behaviour with erlang though.
For example you could spawn a process that calculates the number of words in a line.
As we're having several lines, we spawn one such process for each line and receive the answers to calculate a sum from it.
That way, we spawn processes that do the "heavy" computations (utilizing additional cores if available) and later we collect the results.
-module(countwords).
-export([count_words_in_lines/1]).
count_words_in_lines(Lines) ->
% For each line in lines run spawn_summarizer with the process id (pid)
% and a line to work on as arguments.
% This is a list comprehension and spawn_summarizer will return the pid
% of the process that was created. So the variable Pids will hold a list
% of process ids.
Pids = [spawn_summarizer(self(), Line) || Line <- Lines],
% For each pid receive the answer. This will happen in the same order in
% which the processes were created, because we saved [pid1, pid2, ...] in
% the variable Pids and now we consume this list.
Results = [receive_result(Pid) || Pid <- Pids],
% Sum up the results.
WordCount = lists:sum(Results),
io:format("We've got ~p words, Sir!~n", [WordCount]).
spawn_summarizer(S, Line) ->
% Create a anonymous function and save it in the variable F.
F = fun() ->
% Split line into words.
ListOfWords = string:tokens(Line, " "),
Length = length(ListOfWords),
io:format("process ~p calculated ~p words~n", [self(), Length]),
% Send a tuple containing our pid and Length to S.
S ! {self(), Length}
end,
% There is no return in erlang, instead the last value in a function is
% returned implicitly.
% Spawn the anonymous function and return the pid of the new process.
spawn(F).
% The Variable Pid gets bound in the function head.
% In erlang, you can only assign to a variable once.
receive_result(Pid) ->
receive
% Pattern-matching: the block behind "->" will execute only if we receive
% a tuple that matches the one below. The variable Pid is already bound,
% so we are waiting here for the answer of a specific process.
% N is unbound so we accept any value.
{Pid, N} ->
io:format("Received \"~p\" from process ~p~n", [N, Pid]),
N
end.
And this is what it looks like, when we run this in the shell:
Eshell V5.6.5 (abort with ^G)
1> Lines = ["This is a string of text", "and this is another", "and yet another", "it's getting boring now"].
["This is a string of text","and this is another",
"and yet another","it's getting boring now"]
2> c(countwords).
{ok,countwords}
3> countwords:count_words_in_lines(Lines).
process <0.39.0> calculated 6 words
process <0.40.0> calculated 4 words
process <0.41.0> calculated 3 words
process <0.42.0> calculated 4 words
Received "6" from process <0.39.0>
Received "4" from process <0.40.0>
Received "3" from process <0.41.0>
Received "4" from process <0.42.0>
We've got 17 words, Sir!
ok
4>
The key thing that enables Erlang to scale is related to concurrency.
An operating system provides concurrency by two mechanisms:
operating system processes
operating system threads
Processes don't share state – one process can't crash another by design.
Threads share state – one thread can crash another by design – that's your problem.
With Erlang – one operating system process is used by the virtual machine and the VM provides concurrency to Erlang programme not by using operating system threads but by providing Erlang processes – that is Erlang implements its own timeslicer.
These Erlang process talk to each other by sending messages (handled by the Erlang VM not the operating system). The Erlang processes address each other using a process ID (PID) which has a three-part address <<N3.N2.N1>>:
process no N1 on
VM N2 on
physical machine N3
Two processes on the same VM, on different VM's on the same machine or two machines communicate in the same way – your scaling is therefore independent of the number of physical machines you deploy your application on (in the first approximation).
Erlang is only threadsafe in a trivial sense – it doesn't have threads. (The language that is, the SMP/multi-core VM uses one operating system thread per core).
You may have a misunderstanding of how Erlang works. The Erlang runtime minimizes context-switching on a CPU, but if there are multiple CPUs available, then all are used to process messages. You don't have "threads" in the sense that you do in other languages, but you can have a lot of messages being processed concurrently.
Erlang messages are purely asynchronous, if you want a synchronous reply to your message you need to explicitly code for that. What was possibly said was that messages in a process message box is processed sequentially. Any message sent to a process goes sits in that process message box, and the process gets to pick one message from that box process it and then move on to the next one, in the order it sees fit. This is a very sequential act and the receive block does exactly that.
Looks like you have mixed up synchronous and sequential as chris mentioned.
Referential transparency: See http://en.wikipedia.org/wiki/Referential_transparency_(computer_science)
In a purely functional language, order of evaluation doesn't matter - in a function application fn(arg1, .. argn), the n arguments can be evaluated in parallel. That guarantees a high level of (automatic) parallelism.
Erlang uses a process modell where a process can run in the same virtual machine, or on a different processor -- there is no way to tell. That is only possible because messages are copied between processes, there is no shared (mutable) state. Multi-processor paralellism goes a lot farther than multi-threading, since threads depend upon shared memory, this there can only be 8 threads running in parallel on a 8-core CPU, while multi-processing can scale to thousands of parallel processes.