Is it possible to limit CPU & Memory for the *nix Process?
The CPU limit may look like "use no more than 10% of one core".
The memory limit may look like "use no more than 100Mb", the OS may limit it or kill the process if it try to exceed the limit, both ways are fine.
Any *nix that could do that would be fine.
It seems it is possible to implement it with virtual machines, but it is not acceptable because the overhead is too huge.
If you happen to use Solaris, the ability to limit resource usage is a native feature.
Memory (RAM) usage can be capped per process using the rcap.max-rss setting while CPU usage can be limited per project using the project.cpu-caps.
Note that Solaris also allows OS level virtualization (a.k.a. zones) which have no significant overhead, if any, compared to a bare metal OS instance.
Resource capping is part of Solaris zones configuration.
Try CPULimit
cpulimit is a simple program which attempts to limit the cpu usage of a process (expressed in percentage, not in cpu time). This is useful to control batch jobs, when you don't want them to eat too much cpu. It does not act on the nice value or other scheduling priority stuff, but on the real cpu usage. Also, it is able to adapt itself to the overall system load, dynamically and quickly.
Related
I was wondering if there is a scientific differentiation in terminology when speaking of CPU Usage and CPU Utilization. I have the feeling that both words are used as synonyms. They both describe the relation between CPU Time and CPU Capacity. Wikipedia calls it CPU Usage. Microsoft uses CPU Utilization. But I also found an article where Microsoft uses the term CPU Usage. Now VMware defines to use CPU Utilization in the context of physical CPUs and CPU Usage in the context of logical CPUs. Also, there is no tag for cpu_utilization in stackoverflow.
Does anyone know a scientific differentiation?
Usage
CPU usage as a percentage during the interval.
o VM - Amount of actively used virtual CPU, as a percentage of total available CPU. This is the host's view of the CPU usage, not the guest operating system view. It is the average CPU utilization over all available virtual CPUs in the virtual machine. For example, if a virtual machine with one virtual CPU is running on a host that has four physical CPUs and the CPU usage is 100%, the virtual machine is using one physical CPU completely.
virtual CPU usage = usagemhz / (# of virtual CPUs x core frequency)
o Host - Actively used CPU of the host, as a percentage of the total available CPU. Active CPU is approximately equal to the ratio of the used CPU to the available CPU.
available CPU = # of physical CPUs x clock rate
100% represents all CPUs on the host. For example, if a four-CPU host is running a virtual machine with two CPUs, and the usage is 50%, the host is using two CPUs completely.
o Cluster - Sum of actively used CPU of all virtual machines in the cluster, as a percentage of the total available CPU.
CPU Usage = CPU usagemhz / effectivecpu
CPU usage, as measured in megahertz, during the interval.
o VM - Amount of actively used virtual CPU. This is the host's view of the CPU usage, not the guest operating system view.
o Host - Sum of the actively used CPU of all powered on virtual machines on a host. The maximum possible value is the frequency of the processors multiplied by the number of processors. For example, if you have a host with four 2GHz CPUs running a virtual machine that is using 4000MHz, the host is using two CPUs completely.
4000 / (4 x 2000) = 0.50
Used:
Time accounted to the virtual machine. If a system service runs on behalf of this virtual machine, the time spent by that service (represented by cpu.system) should be charged to this virtual machine. If not, the time spent (represented by cpu.overlap) should not be charged against this virtual machine.
Reference:http://pubs.vmware.com/vsphere-51/index.jsp?topic=%2Fcom.vmware.wssdk.apiref.doc%2Fcpu_counters.html
Very doubtful. You will probably find exact definitions in some academic text books but I bet they'll be inconsistent between text books. I've seen definitions in manpages that are inconsistent with the actual implementation within the code. This is a case where everyone assumes the definitions are so obvious they never check to see if theirs is consistent with others.
My suggestion is to fully definite your use and go with that. Others can then have a reference (your formula/algorithm) and can translate between yours and theirs.
By the way, figuring out utilization, usage, etc. is very complicated and fraught with traps. OSs move tasks around, logical CPUs move between cores, turbo modes temporarily bump clock rates, work is offloaded to internal coprocessors, processors go to sleep or drop in frequency, hyperthreading where multiple logical CPUs contend for shared resources, etc. What's worse is that it is a moving target. Exact and well-defined metrics today will start to get out of date quickly as hardware and software architectures continue to evolve per Moore's law and any SW equivalent.
Within a single context (paper, book, web article, etc.), there may be a difference, but there are not, as far as I know, consistent universally accepted standard definitions for these terms.
Within one authors writings, however, they might be used to describe different things. For example (not an exhaustive list):
How much of a single CPUs computing capacity is being used over a specific sample period
How much of a single CPUs computing capacity is being used by a specific schedulable entity (thread, process, light-weight process, kernel, interrupt routine, etc.) over a specific sample period
Either of the above, but taking all CPUs in the system into account
Any of the above, but with a difference in perspective between real CPUs and virtual CPUs (whether hyperthreading or CPUs actually being emulated by VMware, KVM/QEMU, Xen, Virtualbox or the like)
A comparative measure of how much CPU capacity is being used in one algorithm over another
Probably several other possibilities as well....
We have a process that is computationally intensive. When it runs it typically it uses 99% of the available CPU. It is configured to take advantage of all available processors so I believe this is OK. However, one of our customers is complaining because alarms go off on the server on which this process is running because of the high CPU utilization. I think that there is nothing wrong with high CPU utilization per se. The CPU drops back to normal when the process stops running and the process does run to completion (no infinite loops, etc.). I'm just wondering if I am on solid ground when I say that high CPU usage is not a problem per se.
Thank you,
Elliott
if I am on solid ground when I say that high CPU usage is not a problem per se
You are on solid ground.
We have a process that is computationally intensive
Then I'd expect high CPU usage.
The CPU drops back to normal when the process stops running
Sounds good so far.
Chances are that the systems you client are using are configured to notify when the CPU usage goes over a certain limit, as sometimes this is indicative of a problem (and sustained high usage can cause over heating and associated problems).
If this is expected behavior, your client needs to adjust their monitoring - but you need to ensure that the behavior is as expected on their systems and that it is not likely to cause problems (ensure that high CPU usage is not sustained).
Alarm is not a viable reason for poor design. The real reason may be that it chokes other tasks on the system. Modern OSes usually take care of this by lowering dynamic priority of the CPU hungry process in such a way that others that are less demanding of CPU time will get higher priority. You may tell the customer to "nice" the process to start with, since you probably don't care if it runs 10 mins of 12 mins. Just my 2 cents :)
I was just wondering why there is a need to go through all the trouble of creating distributed systems for massive parallel processing when, we could just create individual machines that support hundreds or thousands of cores/CPUs (or even GPGPUs) per machine?
So basically, why should you do parallel processing over a network of machines when it can rather be done at much lower cost and much more reliably on 1 machine that supports numerous cores?
I think it is simply cheaper. Those machines are available today, no need of inventing something new.
Next problem will be in complexity of the motherboard, imagine 10 CPUs on one MB - so much links! And if one of those CPUs dies, it could destroy whole machine..
You can write a program for GPGPU of course, but it is not as easy as write it for CPU. There are many limitations, eg. cache per core is really small if any, you can not communicate between cores (or you can, but it is very costly) etc.
Linking many computers is more stable, more scalable and cheaper due to long usage history.
What Petr said. As you add cores to an individual machine, communication overhead increases. If memory is shared between cores then the locking architecture for shared memory, and caching, generates increasingly large overheads.
If you don't have shared memory, then effectively you're working with different machines, even if they're all in the same box.
Hence it's usually better to develop very large scale apps without shared memory. And usually possible as well - although communications overhead is often still large.
Given that this is the case, there's little use for building highly multicore individual machines - though some do exist e.g. nvidia tesla...
I am new to OpenCL, please tell me that the host cpu can be used only for allocating memory to the device, or we can use it can as an openCL device. (Because after the allocation is done, the host cpu will be idle).
You can use a cpu as a compute device. Opencl even allows multicore/processor systems to segment cores into separate compute units. I like to use this feature to divide the cpus on my system into groups based on NUMA nodes. It is possible to divide a cpu into compute devices which all share the same level of cache memory (L1, L2, L3 or L4).
You need a platform that supports it, such as AMD's SDK. I know there are ways to have Nvidia and AMD platforms on the same machine, but I have never had to do so myself.
Also, the opencl event/callback system allows you to use your cpu as you normally would while the gpu kernels are executing. In this way, you can use openmp or any other code on the host while you wait for the gpu kernel to finish.
There's no reason the CPU has to be idle, but it needs a separate job to do. Once you've submitted work to OpenCL you can:
Get on with something else, like preparing the next set of work, or performing calculation on something completely different.
Have the CPU set up as another compute device, and so submit a piece of work to it.
Personally I tend to find myself needing the first case more often as it's rare I find myself with two tasks that are independent and lend themselves to OpenCL style. The trick is keeping things balanced so you're not waiting a long time for the GPU task to finish, or having the GPU idle while the CPU is getting on with other work.
It's the same problem OpenGL coders had to conquer. Avoiding being CPU or GPU bound, and balancing between the two for best performance.
I want to use the highest possible number of threads (to use less computers) but without making the bottleneck to be in the client.
JMeter can simulate a very High Load provided you use it right.
Don't listen to Urban Legends that say JMeter cannot handle high load.
Now as for answer, it depends on:
your machine power
your jvm 32 bits or 64 bits
your jvm allocated memory -Xmx
your test plan ( lot of beanshell, post processor, xpath ... Means lots of cpu)
your os configuration (tunable)
Gui / non gui mode
So there is no theorical answer but following Best Practices will ensure JMeter performs well.
Note that with jmeter you can distribute load through remote testing, read:
Remote Testing > 15.4 Using a different sample sender
And finally use cloud based testing if it's not enough.
Read this for tuning tips:
http://www.ubik-ingenierie.com/blog/jmeter_performance_tuning_tips/
Read this book for doing load testing and using JMeter correctly.
I have used JMeter a fair bit and found it is not great at generating really high load. On a 2Ghz Core2 Duo with 2Gb memory you can reasonably expect about 100 threads.
That being said, it is best to run it on your hardware so that the CPU of the PC does not peak at 100% - a stable 80%-90% is best otherwise the results are affected.
I have also tried WAPT 5 - it successfully ran 1000+ threads from the same PC. It is not free but it is more useable than JMeter but doesn't have all of the features.
Outdated answer since at least version 2.6 see https://stackoverflow.com/a/11922239/460802 for a more up to date one.
The JMeter Wiki reports cases where JMeter was used with as much as 1000 threads. I have used it with at most 100 threads, but the Links in the Wiki suggest resource reductions I never tried.
One of the issues we had with running JMeter on Windows XP was the Windows XP TCP Connection Limit. Limit should be removed in order to run use the JMeter to workstation’s full potential
More info here. AFAIK, does not apply to other OS.
I used JMeter since 2004 and i launched lot of load tests.
With PC Windows 7 64 bits 4Go RAM iCore5.
I think JMeter can support 300 to 400 concurrent threads for Http (Sampler) protocol with only one "Aggregate Report Listener" who writes in the log file results and timers between call pages.
For a big load test you could configure JMeter with slaves (load generators) like this
http://jmeter-plugins.org/wiki/HttpSimpleTableServer/
I have already done tests with 11 PC slaves to simulate 5000 threads.
I have not used JMeter, but the answer probably depends on your hardware. Best bet might be to establish metrics of performance, guess at the number of threads and then run a binary search as follows.
Source was Wikipedia.
Number guessing game...
This rather simple game begins something like "I'm thinking of an integer between forty and sixty inclusive, and to your guesses I'll respond 'High', 'Low', or 'Yes!' as might be the case." Supposing that N is the number of possible values (here, twenty-one as "inclusive" was stated), then at most questions are required to determine the number, since each question halves the search space. Note that one less question (iteration) is required than for the general algorithm, since the number is already constrained to be within a particular range.
Even if the number we're guessing can be arbitrarily large, in which case there is no upper bound N, we can still find the number in at most steps (where k is the (unknown) selected number) by first finding an upper bound by repeated doubling. For example, if the number were 11, we could use the following sequence of guesses to find it: 1, 2, 4, 8, 16, 12, 10, 11
One could also extend the technique to include negative numbers; for example the following guesses could be used to find −13: 0, −1, −2, −4, −8, −16, −12, −14, −13
It is more dependent on the kind of performance testing you do(load, spike, endurance etc) on a specific server (a little on hardware dependency)
Keep in mind around these parameters
- the client machine on which you are targeting the run of jmeter, there will be a certain amount of heap memory allocated, ensure to have a healthy allocation so that the script does not error out. The highest i had run on jmeter was 1500 on a local environment ( client - server arch), On a Web arch, the highest i had a run was based upon Non- functional requirement were limited to 250 threads,
so it ideally depends on the kinds of performance testing and deployment style and so on..
There is not standard number for this. The maximum number of threads that you can generate from one computer depends completely on the computer's hardware and the OS. The OS by default occupies certain amount of CPU and the RAM.
To find out the maximum threads your computer can handle you can prepare a sample test and run it with only a few threads. Then with each cycle of test run increase the number of threads gradually. During this you also need to monitor the CPU, RAM, Disk I/O and Network I/O of your computer. The moment any of these reach near or beyond 80% (Again for you to decide if near is okay for you or beyond), that is the maximum number of threads your computer can handle. To be on the safer side I would stop at the number when the resource utilization reaches 70%.
It'll depend on the hardware you run on as well as the underlying script. I've always felt that this fuzziness is the biggest problem with traditional load testing tools. If you've got a small budget ($200 or so gets you a LOT of testing), check out my company's load testing service, BrowserMob.
Besides our Real Browser Users (RBUs) which control thousands on actual browsers for the purpose of performance and load testing, we also have traditional virtual users (VUs). Scripts are written in JavaScript and can make various HTTP calls.
The reason I bring it up is that I always felt that the game of trying to figure out how many VUs you can fit on your load gen hardware is dangerous. It's so easy to get bad results without realizing it.
To solve that for BrowserMob, we took an extremely conservative approach on the number of VUs and RBUs per CPU core: no more than 1 browser or 50 threads per CPU core, and sometimes much less. In the world of cloud computing, CPU cycles are so cheap that it just doesn't make sense to try to overload machines.