I would like my software that scans disk structure to work in background but lowing the priority for the thread that that scans disk structure doesn't work. I mean you still have the feeling of the computer hard working and even freezing even if your program consumes only 1 percent of the processor time. Is it possible to implement "hard disk time consumption" equivalent of CPU consumption in Win32
Since Vista you can lower your IO priority, which is separate from CPU priority.
http://msdn.microsoft.com/en-us/library/ms686219(VS.85).aspx
SetPriorityClass(GetCurrentProcess(), PROCESS_MODE_BACKGROUND_BEGIN)
For XP, 2003 and older, you'd have to find some other way to throttle your disk activity, like using Sleep() often.
Disk accesses are typically measured by a few different metrics transfers per second (which can be broken down into reads/writes), and data read or written per second. If you want to limit the impact of your disk scanning application, one way to do this would be to track one (or both) of these metrics, determine a reasonable cap, and periodically sleep your thread for some time period. Nothing you can do to CPU scheduling is going to be effective at accomplishing this task except in the most diaphanous, indirect way.
Related
I have 12 tasks to run on an octo-core machine. All tasks are CPU intensive and each will max out a core.
Is there a theoretical reason to avoid stacking tasks on a maxed out core (such as overhead, swapping across tasks) or is it faster to queue everything?
Task switching is a waste of CPU time. Avoid it if you can.
Whatever the scheduler timeslice is set to, the CPU will waste its time every time slice by going into the kernel, saving all the registers, swapping the memory mappings and starting the next task. Then it has to load in all its CPU cache, etc.
Much more efficient to just run one task at a time.
Things are different of course if the tasks use I/O and aren't purely compute bound.
Yes it's called queueing theory https://en.wikipedia.org/wiki/Queueing_theory. There are many different models https://en.wikipedia.org/wiki/Category:Queueing_theory for a range of different problems I'd suggest you scan them and pick the one most applicable to your workload then go and read up on how to avoid the worst outcomes for that model, or pick a different, better, model for dispatching your workload.
Although the graph at this link https://commons.wikimedia.org/wiki/File:StochasticQueueingQueueLength.png applies to Traffic it will give you an idea of what is happening to response times as your CPU utilisation increases. It shows that you'll reach an inflection point after which things get slower and slower.
More work is arriving than can be processed with subsequent work waiting longer and longer until it can be dispatched.
The more cores you have the further to the right you push the inflection point but the faster things go bad after you reach it.
I would also note that unless you've got some really serious cooling in place you are going to cook your CPU. Depending on it's design it will either slow itself down, making your problem worse, or you'll trigger it's thermal overload protection.
So a simplistic design for 8 cores would be, 1 thread to manage things and add tasks to the work queue and 7 threads that are pulling tasks from the work queue. If the tasks need to be performed within a certain time you can add a TimeToLive value so that they can be discarded rather than executed needlessly. As you are almost certainly running your application in an OS that uses a pre-emptive threading model consider things like using processor affinity where possible because as #Zan-Lynx says task/context switching hurts. Be careful not to try to build your OS'es thread management again as you'll probably wind up in conflict with it.
tl;dr: cache thrash is Bad
You have a dozen tasks. Each will have to do a certain amount of work.
At an app level they each processed a thousand customer records or whatever. That is fixed, it is a constant no matter what happens on the hardware.
At the the language level, again it is fixed, C++, java, or python will execute a fixed number of app instructions or bytecodes. We'll gloss over gc overhead here, and page fault and scheduling details.
At the assembly level, again it is fixed, some number of x86 instructions will execute as the app continues to issue new instructions.
But you don't care about how many instructions, you only care about how long it takes to execute those instructions. Many of the instructions are reads which MOV a value from RAM to a register. Think about how long that will take. Your computer has several components to implement the memory hierarchy - which ones will be involved? Will that read hit in L1 cache? In L2? Will it be a miss in last-level cache so you wait (for tens or hundreds of cycles) until RAM delivers that cache line? Did the virtual memory reference miss in RAM, so you wait (for milliseconds) until SSD or Winchester storage can page in the needed frame? You think of your app as issuing N reads, but you might more productively think of it as issuing 0.2 * N cache misses. Running at a different multi-programming level, where you issue 0.3 * N cache misses, could make elapsed time quite noticeably longer.
Every workload is different, and can place larger or smaller demands on memory storage. But every level of the memory hierarchy depends on caching to some extent, and higher multi-programming levels are guaranteed to impact cache hit rates. There are network- and I/O-heavy workloads where very high multi-programming levels absolutely make sense. But for CPU- and memory-intensive workloads, when you benchmark elapsed times you may find that less is more.
We have a transaction intensive process at one customer site running on a quad core server with four processors. The process is designed to take advantage of every core available. So in this installation, we take an input queue, divide it by 16th's and allocate each fraction of the queue to a core. It works well and keeps up with the transaction volume on the box.
Looking at the CPU utilization on the box, it never seems to go above 33%. Now we have a new customer with at least double the volume of the existing customer. Some of us are arguing that since CPU usage is way below maximum utilization, that we should go with the same configuration.
Others claim that there is no direct correlation between cpu utilization and transaction processing speed and since the logic of the underlying software module is based on the number of available cores, that it makes sense to obtain a box with proportionately more cores available for the new client to accommodate the increased traffic volume.
Does anyone have a sense as to who is right in this instance?
Thank you,
To determine the optimum configuration for your new customer, understanding the reason for low CPU usage is paramount.
Very likely, the reason is one of the following:
Your process is limited by memory bandwidth. In this case, faster RAM will help if supported by the motherboard. If possible, a redesign to limit the amount of data accessed during processing will improve performance. Adding more CPU cores will, on its own, do nothing to improve performance.
Your process is limited by disk I/O. Using faster disk connections (SATA etc.) and/or upgrading to a SSD might help, but more CPU power will not.
Your process is limited by synchronization contention. In this case, adding more threads for more cores might even be counter productive. Redesigning your algorithm might help in this case.
Having said this, I have also seen situations where processes that are definitely CPU bound fail to achieve 100% CPU usage on modern processors (Core i7 etc.) because in certain turbo boost relevant cases, task manager will show less than 100%.
As 9000 said, you need to find out what your bottlenecks are when under load. Perfmon might provide enough data to find out.
Another afterthought: You could limit your process on the existing machine to part of the cores (but still at least 30% so that theoretically, CPU doesn't become a bottleneck due to this limitation) and check if overall throughput degrades. If it does not, adding more cores will not improve performance.
I'm trying to speed up the time taken to compile my application and one thing I'm investigating is to check what resources, if any, I can add to the build machine to speed things up. To this end, how do I figure out if I should invest in more CPU, more RAM, a better hard disk or whether the process is being bound by some other resource? I already saw this (How to check if app is cpu-bound or memory-bound?) and am looking for more tips and pointers.
What I've tried so far:
Time the process on the build machine vs. on my local machine. I found that the build machine takes twice the time as my machine.
Run "Resource Monitor" and look at the CPU usage, Memory usage and Disk usage while the process is running - while doing this, I have trouble interpreting the numbers, mainly because I don't understand what each column means and how that translates to a Virtual Machine vs. a physical box and what it means with multi-CPU boxes.
Start > Run > perfmon.exe
Performance Monitor can graph many system metrics that you can use to deduce where the bottlenecks are including cpu load, io operations, pagefile hits and so on.
Additionally, the Platform SDK now includes a tool called XPerf that can provide information more relevant to developers.
Random-pausing will tell you what is your percentage split between CPU and I/O time.
Basically, if you grab 10 random stackshots, and if 80% (for example) of the time is in I/O, then on 8+/-1.3 samples the stack will reach into the system routine that reads or writes a buffer.
If you want higher precision, take more samples.
Not sure if this is the appropriate place for this question, but it seems related to threading and system resources and all that.
Why Does my Task Manager show that the System Idle Process is using 90%+ of the CPU power when I have 3 different processes going!?!?
Is it because of I/O bottlenecks?
For example, if I do an SVN checkout, and empty my recycle bin, and browse the web at the same time, why is the System Idle process at 97%, and the other processes around 1% each? None of them seem to actually be going very fast.
Mostly the processes are waiting for disk or network operations to complete, or waiting for user input.
You might think you have a fast disk or network connection, but compared to memory/cpu it's like walking to the nearest library, looking up a book in the catalog and finding it on the shelf vs already having the book in your hand.
This is why you pay thousands or dollars for 10,000 and 15,000 rpm scsi drives (or even more for SANs) on high performance servers.
I can't say for sure. But I'd say I/O bottlenecks would be a large part of it. In fact, I wouldn't imagine any of the tasks you described would be very CPU intensive.
Now, try to re-encode a raw AVI file into DIVX format while simultaneously rendering a 3D animation in Maya, and your CPU should be plenty busy.
It really depends on what your processes are doing. If they are IO bound, then there's a good chance that they're sitting and waiting most of the time.
If they're winforms apps that wait for user input, then they sit there, doing nothing, and waiting for input.
SVN checkout and recycle bin emptying are both very heavy disk activities with not much demand on the CPU, and web browsing is very spiked in terms of CPU usage (spikes when rendering pages, for example, but costs very little once that is done).
If you want to see your CPU sustain high utilization, do something that is almost purely CPU/memory based, like Folding#Home.
For some of the customers that we develop software for, we are required to "guarantee" a certain amount of spare resources (memory, disk space, CPU). Memory and disk space are simple, but CPU is a bit more difficult.
One technique that we have used is to create a process that consumes a guaranteed amount of CPU time (say 2.5 seconds every 5 seconds). We run this process at highest priority in order to guarantee that it runs and consumes all of its required CPU cycles.
If our normal applications are able to run at an acceptable level of performance and can pass all of their functionality tests while the spare time process is running as well, then we "assume" that we have met our commitment for spare CPU time.
I'm sure that there are other techniques for doing the same thing, and would like to learn about them.
So this may not be exactly the answer you're looking for, but if all you want to do is make sure your application doesn't exceed certain limits on resource consumption and you're running on linux you can customize /etc/security/limits.con (may be different file on your distro of choice) to force the limits on a particular user and only run the process under that user. This is of course assuming that you have that level of control on your client's production environment.
If I understand correctly, your concern is wether the application also runs while a given percentage of the processing power is not available.
The most incontrovertible approach is to use underpowered hardware for your testing. If the processor in your setup allows you to, you can downclock it online. The Linux kernel gives you an easy interface for doing this, see /sys/devices/system/cpu/cpu0/cpufreq/. There is also a bunch of GUI applications for this available.
If your processor isn't capable of changing clock speed online, you can do it the hard way and select a smaller multiplier in your BIOS.
I think you get the idea. If it runs on 1600 Mhz instead of 2400 Mhz, you can guarantee 33% of spare CPU time.
SAR is a standard *nix process that collects information about the operational use of system resources. It also has a command line tool that allows you to create various reports, and it's common for the data to be persisted in a database.
With a multi-core/processor system you could use Affinity to your advantage.