We're building a high performance computing scientific application (lots and lots of computations) using Java. To the best of our knowledge, google compute engine does not provide the "true" physical socket information, nor do they have a service like AWS's dedicated hosting (https://aws.amazon.com/ec2/dedicated-hosts/ and then see section on "affinity") where (for a fee) one could see the actual physical sockets.
However, based on our understanding, JIT compiler will do a lot better if it knows that all the threads are really on a single physical socket. Would it be reasonable, therefore, to assume that even though google compute engines do NOT display the true underlying physical socket structure, that if we have a google compute engine that's <= 16 cores, that's it's definitely (or most likely, eg >95%) coming from a single physical socket? If so, we can also then assume that the cpu numbers (when doing cat /proc/cpuinfo) correspond logically (in sequence) to the physical cpu cores/logical cores so that if we wanted our program to put two threads onto the same physical core (but two logical cores), we could just tell it to put the two threads on CPU 0 and 1 and we would know that CPU0 and CPU1 belong to the same physical CPU core, and that CPU2 and CPU3 belong to the same physical core and so on?
If so, would it be reasonable to assume that for compute engines that are 32 VCPUs or 64 VCPUs that the number of sockets are 2 and 4 respectively? And that the result of cat proc/cpuinfo also follows a logical order, so that not only is CPU0 and CPU1 on the same phyisical CPU core, but that we can assume that CPU0 through CPU15 is on physical socket #1, and CPU16 to CPU31 is on physical socket #2 etc?
as you inferred, GCE currently does not expose the actual NUMA architecture of the machine, and we do not guarantee that a VM will run entirely on one socket, nor can you intentionally land VM threads on specific cores/hyperthreads. These capabilities are on our radar for possible future enhancements/features.
I don't believe this is specifically documented currently, however I am speaking as a Product Manager for GCE.
Related
I have one VM Compute Engine to host simple apps. My apps is growing and the number of users too.
Now my users work basicaly from 08:00 AM to 07:00 PM, in this period the usage os CPU and Memory is High and the speed of work is very important.
I'm preparing to expand the memory and processor in the next days, but i search a more scalable and cost efective way.
Is there a way for automatic add resources when i need and reduce after no more need?
Thanks
The cost of running your VMs is directly related to a number of different factors i.e. the type of network in use (premium vs standard), the machine type, the boot disk image you use (premium vs open-source images) and the region/zone where your workloads are running, among other things.
Your use case seems to fit managed instance groups (MIGs). With MIGs you essentially configure a template for VMs that share the same attributes. During the configuration of your MIG, you will be able to specify the CPU/memory limit beyond which the MIG autoscaler will kick off. When your CPU/memory reading goes below that threshold, MIG scales your VMs down to the number of instances specified in your template.
You can also use requests per second as a threshold for autoscaling and I would recommend you explore the docs to know more about it.
See docs
Let say I write a program which contains many functions/methods. In this program some functions are used many times as compared to others.
In this case does the positioning of a function/method matters in terms of altering the speed at lower level(memory).
As currently, I am learning Computer Organization & Architecture, so this doubt arrived in my mind.
RAM itself is "flat", equal performance at any address (except for NUMA local vs. remote memory in a multi-socket machine, or mixed-size DIMMs on a single socket leading to only partial dual-channel benefits1).
i-cache and iTLB locality can make a difference, so grouping "hot" functions together can be useful even if you don't just inline them.
Locality also matters for demand paging of code in from disk: If a whole block of your executable is "cold", e.g. only needed for error handling, program startup doesn't have to wait for it to get page-faulted in from disk (or even soft page faults if it was hot in the OS's pagecache). Similarly, grouping "startup" code into a page can allow the OS to drop that "clean" page later when it's no longer needed, freeing up physical memory for more caching.
Compilers like GCC do this, putting CRT startup code like _start (which eventually calls main) into a .init section in the same program segment (mapping by the program loader) as .text and .fini, just to group startup code together. Any C++ non-const static-initializer functions would also go in that section.
Footnote 1: Usually; IIRC it's possible for a computer with one 4G and one 8G stick of memory to run dual channel for the first 8GB of physical address space, but only single channel for the last 4, so half the memory bandwidth. I think some real-life Intel chipsets / CPUs memory controllers are like that.
But unless you were making an embedded system, you don't choose where in physical memory the OS loads your program. It's also much more normal for computers to use matched memory on multi-channel memory controllers so the whole range of memory can be interleaved between channels.
BTW, locality matters for DRAM itself: its laid out in a row/column setup, and switching rows takes an extra DDR controller command vs. just reading another column in the same open "page". DRAM pages aren't the same thing as virtual-memory pages; a DRAM page is memory in the same row on the same channel, and is often 2kiB. See What Every Programmer Should Know About Memory? for more details than you'll probably ever want about DDR DRAM, and some really good stuff about cache and memory layout.
I always thought that Hyper-Q technology is nothing but the streams in GPU. Later I found I was wrong(Am I?). So I was doing some reading about Hyper-Q and got confused more.
I was going through one article and it had these two statements:
A. Hyper-Q is a flexible solution that allows separate connections from multiple CUDA streams, from multiple Message Passing Interface (MPI) processes, or even from multiple threads within a process
B. Hyper-Q increases the total number of connections (work queues) between the host and the GK110 GPU by allowing 32 simultaneous, hardware-managed connections (compared to the single connection available with Fermi)
In aforementioned points, Point B says that there can be multiple connected created to a single GPU from host. Does it mean I can create multiple context on a simple GPU through different applications? Does it mean that I will have to execute all applications on different streams?What if all my connections are memory and compute resource consuming, who manages the resource (memory/cores) scheduling?
Think of HyperQ as streams implemented in hardware on the device side.
Before the arrival of HyperQ, e.g. on Fermi, commands (kernel launches, memory transfers, etc.) from all streams were placed in a single work queue by the driver on the host. That meant that commands could not overtake each other, and you had to be careful issuing them in the right order on the host to achieve best overlap.
On the GK110 GPU and later devices with HyperQ, there are (at least) 32 work queues on the device. This means that commands from different queues can be reordered relative to each other until they start execution. So both orderings in the example linked above lead to good overlap on a GK110 device.
This is particularly important for multithreaded host code, where you can't control the order without additional synchronization between threads.
Note that of the 32 hardware queues only 8 are used by default to save resources. Set the CUDA_DEVICE_MAX_CONNECTIONS environment variable to a higher value if you need more.
I have a single machine with 32 cores (2 processors), and 32G RAM. I installed gridengine to submit jobs to those queues I created. But it seems jobs are running on all cores.
I wonder if there is way to limit cores and RAMs for each job. For example I have two queues: parallel.q and serial.q, so that I allocate 20G RAMS and 20 cores to serial.q but I want each job only use one core and maximum 1G RAMs, and 8G RAMs + 8 cores to a single parallel job. All 4 cores and 4G rams left for other usage.
How can I config my queue or gridengine to get the setting right? I tried to read the manual, but don't have a clue.
Thanks!
I don't have problem with parallel jobs. I have some serial jobs will call several different programs somehow the system will assign them all cores available. But I don't want all cores be used for jobs rather for example only two cores available for each job.(Each job has several programs run sequentially, in which case systems allocate each program a core). BTW, I would like have some idle cores all the time to process other jobs, like processing data. Is it possible or necessary?
In fact, if I understand well, you want to partition a single machine with several sub-queues, is that right?
This may be problematic with SGE because the host configuration allows you to set the number of CPU available on a given node. Than you create your queues and assign different hosts to different queues.
In your case, you shoud assign the same host to one master queue, and then add subordinate queues that can use only a given MAX_SLOTS slots.
But if I may ask one question: why should you partition it? If you set up only one queue and configure some parallel environment then you can just submit your jobs using qsub -pe <parallelEnvironment> <NSLOTS> and the grid engine takes care of everything. I suggest you setup at least an OpenMP parallel environment, because you won't probably need MPI on a shared memory machine like yours (it seems a great machine BTW).
Another thing is that you must be able to configure your model run so that the code that you are using can be used with a limited number of CPU; this is very important. In practice you must assign the same number of CPUs to the simulation code than to the SGE. This information is contained in the $NSLOTS variable of your qsub-script.
How to measure current load of MySQL server? I know I can measure different things like CPU usage, RAM usage, disk IO etc but is there a generic load measure for example the server is at 40% load etc?
mysql> SHOW GLOBAL STATUS;
Found here.
The notion of "40% load" is not really well-defined. Your particular application may react differently to constraints on different resources. Applications will typically be bound by one of three factors: available (physical) memory, available CPU time, and disk IO.
On Linux (or possibly other *NIX) systems, you can get a snapshot of these with vmstat, or iostat (which provides more detail on disk IO).
However, to connect these to "40% load", you need to understand your database's performance characteristics under typical load. The best way to do this is to test with typical queries under varying amounts of load, until you observe response times increasing dramatically (this will mean you've hit a bottleneck in memory, CPU, or disk). This load should be considered your critical level, which you do not want to exceed.
is there a generic load measure for example the server is at 40% load ?
Yes! there is:
SELECT LOAD_FILE("/proc/loadavg")
Works on a linux machine. It displays the system load averages for the past 1, 5, and 15 minutes.
System load averages is the average number of processes that are either in a runnable or uninterruptable state. A process in a runnable state is either using the CPU or waiting to use the CPU.
A process in uninterruptable state is waiting for some I/O access, eg waiting for disk. The averages are taken over the three time intervals. Load averages are not normalized for the number of
CPUs in a system, so a load average of 1 means a single CPU system is loaded all the time while on a 4 CPU system it means it was idle 75% of the time.
So if you want to normalize you need to count de number of cpu's also.
you can do that too with
SELECT LOAD_FILE("/proc/cpuinfo")
see also 'man proc'
with top or htop you can follow the usage in Linux realtime
On linux based systems the standard check is usually uptime, a load index is returned according to metrics described here.
aside from all the good answers on this page (SHOW GLOBAL STATUS, VMSTAT, TOP...) there is also a very simple to use tool written by Jeremy Zawodny, it is perfect for non-admin users. It is called "mytop". more info # http://jeremy.zawodny.com/mysql/mytop/
Hi friend as per my research we have some command like
MYTOP: open source program written using PERL language
MTOP: also an open source program written on PERL, It works same as MYTOP but it monitors the queries which are taking longer time and kills them after specific time.
Link for details of above command