I am trying to find simple examples for what are exactly the wait time and execution time in determining the size of the thread pool. According to brian Goetz:
For tasks that may wait for I/O to complete -- for example, a task
that reads an HTTP request from a socket -- you will want to increase
the pool size beyond the number of available processors, because not
all threads will be working at all times. Using profiling, you can
estimate the ratio of waiting time (WT) to service time (ST) for a
typical request. If we call this ratio WT/ST, for an N-processor
system, you'll want to have approximately N*(1+WT/ST) threads to keep
the processors fully utilized.
I really didn't understand what he meant the Input/output. Who's doing the I/O tasks.
Imagine a task that reads some data from disk. What actually happens:
Open file.
Wait for (the spinning) disk to awake from sleep, to position the head at the right spot and for the desired blocks to appear underneath the head until all bytes arrive in a buffer.
Read from the buffer.
The whole task takes 0.1s to complete. Of this 0.1s 10 percent are spent on step 1 and 3 and the remaining 90 percent on step 2. So 0.01s are "working time" and 0.09s "wait time" that is spent waiting for the disk.
Summary
I am trying to understand the limits of my compute resources when performing multiple simulations. My task is trivial in terms of parallelisation - I need to run a large number of simple independent simulations, i.e. each simulation program does not rely on another for information. Each simulation has roughly the same running time. For this purpose I have created an experiment that is detailed below.
Details
I have two shell scripts located in the same directory.
First script called simple:
#!/bin/bash
# Simple Script
echo "Running sleep with arg= $1 "
sleep 5s
echo "Finished sleeping with arg= $1"
Second script called runall:
#!/bin/bash
export PATH="$PATH:./"
# Fork off a new process for each program by running in background
# Run N processes at a time and wait until all of them have finished
# before executing the next batch. This is sub-optimal if the running
# time of each process varies significantly.
# Note if the number of total processes is not divisible by the alloted pool something weird happens
echo "Executing runall script..."
for ARG in $(seq 600); do
simple $ARG &
NPROC=$(($NPROC+1))
if [ "$NPROC" -ge 300 ]; then
wait
echo "New batch"
NPROC=0
fi
done
Here are some specs on my computer (MAC OS X):
$ ulimit -u
709
$ sysctl hw.ncpu
hw.ncpu: 8
$ sysctl hw.physicalcpu
hw.physicalcpu: 4
From this I interpret that I have 709 processes at my disposal and 8 processor cores available.
However when I execute $ ./runall I eventually end up with:
...
Running sleep with arg= 253
Running sleep with arg= 254
Running sleep with arg= 255
Running sleep with arg= 256
Running sleep with arg= 257
Running sleep with arg= 258
./runall: fork: Resource temporarily unavailable
Running sleep with arg= 259
./simple: fork: Resource temporarily unavailable
Running sleep with arg= 260
$ Running sleep with arg= 261
Finished sleeping with arg= 5
Finished sleeping with arg= 7
Finished sleeping with arg= 4
Finished sleeping with arg= 8
Finished sleeping with arg= 3
...
SO:
Question 1
Does this mean that out of the 709 processes available, only 258 can be dedicated to my runall program, the rest remaining probably being used by other processes on my computer?
Question 2
I substituted the simple script with something else which does something more complicated than just sleep (it reads a file and processes the data in the file to create a graph) and now I start to notice some differences. With the help of using $ time ./runall I can get the total run time and whereas before when calling simple for up to the 258 processes I always got a run time of about 5s:
real 0m5.071s
user 0m0.184s
sys 0m0.263s
i.e, running many simulations in parallel gives the same runtime as a single simulation. However now that I am calling a more complex program instead of simple I get a longer total run time than the single simulation time (calling a single simulation takes 1.5s whereas 20 simulations in parallel takes about 8.5s). How do I explain this behavior?
Question 3
Im not sure how the number of processor cores is related to the parallel performance - Since I have 8 cores at my disposal I thought I would be able to run 8 programs in parallel at the same time it would take me to just run one. Im not sure about my reasoning on this...
If you have 8 cpu threads available, and your programs consume 100% of a single CPU, it does not make sense to run more than 8 programs at a time.
If your programs are multi-threaded, then you may want to have fewer than 8 processes running at a time. If your programs occasionally use less than 100% of a single CPU (perhaps if they're waiting for IO), then you may want to run more than 8 processes at a time.
Even if the process limit for your user is extremely high, other resources could be exhausted much sooner - for instance, RAM. If you launch 200 processes and they exhaust RAM, then the operating system will respond by satisfying requests for RAM by swapping out some other process's RAM to disk; and now the computer needlessly crawls to a halt because 200 processes are waiting on IO to get their memory back from disk, only to have it be written out again because some other process wants to run. This is called thrashing.
If your goal is to perform some batch computation, it does not make sense to load the computer any more than enough processes to keep all CPU cores at 100% utilization. Anything more is waste.
Edit - Clarification on terminology.
A single computer can have more than one CPU socket.
A single CPU can have more than one CPU core.
A single CPU core can support simultaneous execution of more than one stream of instructions. Hyperthreading is an example of this.
A stream of instructions is what we typically call a "thread", either in the context of the operating system, processes, or in the CPU.
So I could have a computer with 2 sockets, with each socket containing a 4-core CPU, where each of those CPUs supports hyperthreading and thus supports two threads per core.
Such a computer could execute 2 * 4 * 2 = 16 threads simultaneously.
A single process can have as many threads as it wants, until some resources is exhausted - raw RAM, internal operating system data structures, etc. Each process has at least one thread.
It's important to note that tricks like hyperthreading may not scale performance linearly. When you have unhyperthreaded CPU cores, those cores contain enough parts to be able to execute a single stream of instructions all by itself; aside from memory access, it doesn't share anything with the rest of the other cores, and so performance can scale linearly.
However, each core has a lot of parts - and during some types of computations, some of those parts are inactive while others are active. And during other types of computations could be the opposite. Doing a lot of floating-point math? Well, then the integer math unit in the core might be idle. Doing a lot of integer math? Well, then the floating-point math unit might be idle.
Hyperthreading seeks to increase perform, even if only a little bit, by exploiting these temporarily unused units within a core; while the floating point unit is busy, schedule something that can use the integer unit.
...
As far as the operating system is concerned when it comes to scheduling is how many threads across all processes are runnable. If I have one process with 3 runnable threads, a second process with one runnable thread, and a third process with 10 runnable threads, then the OS will want to run a total of 3 + 1 + 10 = 14 threads.
If there are more runnable program threads than there are CPU execution threads, then the operating system will run as many as it can, and the others will sit there doing nothing, waiting. Meanwhile, those programs and those threads may have allocated a bunch of memory.
Lets say I have a computer with 128 GB of RAM and CPU resources such that the hardware can execute a total of 16 threads at the same time. I have a program that uses 2 GB of memory to perform a simple simulation, and that program only creates one thread to perform its execution, and each program needs 100s of CPU time to finish. What would happen if I were to try to run 16 instances of that program at the same time?
Each program would allocate 2 GB * 16 = 32 GB of ram to hold its state, and then begin performing its calculations. Since each program creates a single thread, and there are 16 CPU execution threads available, every program can run on the CPU without competing for CPU time. The total time we'd need to wait for the whole batch to finish would be 100 s: 16 processes / 16 cpu execution threads * 100s.
Now what if I increase that to 32 programs running at the same time? Well, we'll allocate a total of 64GB of RAM, and at any one point in time, only 16 of them will be running. This is fine, nothing bad will happen because we've not exhausted RAM (and presumably any other resource), and the programs will all run efficiently and eventually finish. Runtime will be approximately twice as long at 200s.
Ok, now what happens if we try to run 128 programs at the same time? We'll run out of memory: 128 * 2 = 256 GB of ram, more than double what the hardware has. The operating system will respond by swapping memory to dis and reading it back in as needed, but it'll have to do this very frequently, and it'll have to wait for the disk.
If you had enough ram, this would run in 800s (128 / 16 * 100). Since you don't, it's very possible it could take an order of magnitude longer.
Your questions are a little confusing. But here's an attempt to explain some of it:
Question 1 Does this mean that out of the 709 processes available, only 258 can be dedicated to my runall program, the rest remaining probably being used by other processes on my computer?
As the ulimit manpage explains, -u tells you how many processes you can start as a user. As you know, every process on Unix has a uid (there are some nitty gritty details here like euid, setuid etc.) which refers to the user on the system that owns that process. What -u tells you is the number of processes you (since you are logged in and executing the ulimit command) can start and simultaneously run on the computer. Note that once a process with pid p exits, OS is free to recycle that number p for some other processes.
Question 2
The answer to question 2 (which seems to be your main confusion) can only be given when we understand what the time command actually reports. Understanding the output of the time command needs some experimentation. For instance, when I run your experiment (on a comparable Mac) with 100 processes (i.e. $(seq 100)), I get:
./runall.sh 0.01s user 0.02s system 39% cpu 0.087 total
This means that only 39% of the available computing power was used resulting in 0.087s of the wall clock time. Roughly speaking, the wall clock time multiplied by the CPU utilization gives the running time (user time that your code needs + system time that system calls need to execute). Your simple script is rather too simple. It does not cause the CPU's to do any work by making the sleep system call!
Compare this example with a more real-life example to find a subset of a given set with given sum. This (Java) program, on the same computer produces the following times:
java SubsetSum 38.25s user 1.09s system 510% cpu 7.702 total
This means that the total wall clock time in about 7.7 seconds, but all the available cores are stressed extremely highly to execute this program. On a 4-CPU (8 logical CPU), I get a 500% CPU utilization! (And you can see that wall clock time (7.7) multiplied by CPU utilization (5.1) i.e. 39.27 is roughly equal to total time (38.25+1.09 = 39.34))
Question 3
Well, the way to parallelize your programs is by finding out parallelizable activity in solving the problem. You have 8 cores available and the OS will decide how to allocate them to the processes that ask for it. But what if a process goes into BLOCKING state (blocked on I/O)? Then, the OS will schedule this process out and schedule something else in. A simplistic view of this like "8 cores => 8 programs at the same time" is hardly true when you take into account the way the scheduling works.
How to execute a process for n cpu cycles on Linux? I have a batch processing system on a multi-core server and would like to ensure that each task gets exactle the same amount of cpu time. Once the cpu amount is consumed I would like to stop the process. So far I tried to do some thing with /proc/pid/stats utime and stime, but I did not succeed.
I believe it is impossible (to give the exact same number of cycles to several processes; a CPU cycle is often less than a nanosecond). You could execute a process for x CPU seconds. For that use setrlimit(2) with RLIMIT_CPU
Your batch processor could also manage time itself, see time(7). You could use timers (see timer_create(2) & timerfd_create(2)), have an event loop around poll(2), measure time with clock_getttime(2)
I'm not sure it is useful to write your own batch processing system. You could use the existing batch or slurm or gnqs (see also commercial products like pbsworks, lsf ..)
I am implementing a parallel NodeJS application to compute spatial joins. I am running on a MacBook pro i7 processor with 4 cores (8 after hyperthreading).
To make a fair comparison, I am running exact same operation on all threads. I am attaching activity monitor screenshots for the reference.
One process
Completion time: 11.25 seconds
Two processes
Max completion time: 11.53 seconds
Four processes
Max completion time: 14.08 seconds
Eight processes
Max completion time: 24.98 seconds
My question is, given that in none of the cases memory is full, why does it take such a huge
performance hit for extra spawned processes when technically it is equipped to run 8 processes independently?
Thanks in advance.
Actually I am trying to run some experiments where i need to run benchmarks under heavy load. Starting from CPU load, I schedule a sysbench daemon that generates 1000 primes. I set its priority to low so that it only runs once the cpu is not busy with other tasks so as to reduce its impact on the regular workload. Since the priority of the process is set to Low, the process keeps waiting in the queue until it finds a free cpu core to run on. The problem is that its result shows the execution time including the wait period (in the queue) which renders the result invalid.
Is there some way that I could actually calculate the wait period and subtract it from the result to get a valid result?