What is the function that describes the relation between CPU utilization and consumption of energy (electricity/heat wise).
I wonder if it's linear/sub-linear/exp etc..
I am writing a program that decreases the CPU utilization/load of other programs and my main concern is how much do I benefit energy wise..
Moreover, my server is mostly being used as a web-server or DB in a data-center (headless).
In case the data center need more power for cooling I need to consider that as well..
I also need to know what is the effect of CPU utilization on the entire machine power consumption ..
Here you can find a short ppt answering your questions, and providing additional info.
Although there is no Copyright notice in the ppt, the work is probably protected, so I will copy here only three graphs relevant to your main question and follow-ups in comments.
HTH!
For the CPU alone linear would be the most likely.
It gets complicated with CPUs that can reduce the clock speed under low load (like laptops) but for a server it's probably a good approximation.
Remember though that the CPU isn't the only component - you have to multiply by the percentage of power the CPU is using compared to the entire system.
Related
When using the desktop PC's in my university (Which have 4Gb of ram), calculations in Matlab are fairly speedy, but on my laptop (Which also has 4Gb of ram), the exact same calculations take ages. My laptop is much more modern so I assume it also has a similar clock speed to the desktops.
For example, I have written a program that calculates the solid angle subtended by 50 disks at 500 points. On the desktop PC's this calculation takes about 15 seconds, on my laptop it takes about 5 minutes.
Is there a way to reduce the time taken to perform these calculations? e.g, can I allocate more ram to MATLAB, or can I boot up my PC in a way that optimises it for using MATLAB? I'm thinking that if the processor on my laptop is also doing calculations to run other programs this will slow down the MATLAB calculations. I've closed all other applications, but I know theres probably a lot of stuff going on I can't see. Can I boot my laptop up in a way that will have less of these things going on in the background?
I can't modify the code to make it more efficient.
Thanks!
You might run some of my benchmarks which, along with example results, can be found via:
http://www.roylongbottom.org.uk/
The CPU core used at a particular point in time, is the same on Pentiums, Celerons, Core 2s, Xeons and others. Only differences are L2/L3 cache sizes and external memory bus speeds. So you can compare most results with similar vintage 2 GHz CPUs. Things to try, besides simple number crunching tests.
1 - Try memory test, such as my BusSpeed, to show that caches are being used and RAM not dead slow.
2 - Assuming Windows, check that the offending program is the one using most CPU time in Task Manager, also that with the program not running, that CPU utilisation is around zero.
3 - Check that CPU temperature is not too high, like with SpeedFan (free D/L).
4 - If disk light is flashing, too much RAM might be being used, with some being swapped in and out. Task Manager Performance would show this. Increasing RAM demands can be checked my some of my reliability tests.
There are many things that go into computing power besides RAM. You mention processor speed, but there is also number of cores, GPU capability and more. Programs like MATLAB are designed to take advantage of features like parallelism.
Summary: You can't compare only RAM between two machines and expect to know how they will perform with respect to one another.
Side note: 4 GB is not very much RAM for a modern laptop.
Firstly you should perform a CPU performance benchmark on both computers.
Modern operating systems usually apply the most aggressive power management schemes when it is run on laptop. This usually means turning off one or more cores, or setting them to a very low frequency. For example, a Quad-core CPU that normally runs at 2.0 GHz could be throttled down to 700 MHz on one CPU while the other three are basically put to sleep, while it is on battery. (Remark. Numbers are not taken from a real example.)
The OS manages the CPU frequency in a dynamic way, tweaking it on the order of seconds. You will need a software monitoring tool that actually asks for the CPU frequency every second (without doing busy work itself) in order to know if this is the case.
Plugging in the laptop will make the OS use a less aggressive power management scheme.
(If this is found to be unrelated to MATLAB, please "flag" this post and ask moderator to move this question to the SuperUser site.)
I am tunning my GEMM code and comparing with Eigen and MKL. I have a system with four physical cores. Until now I have used the default number of threads from OpenMP (eight on my system). I assumed this would be at least as good as four threads. However, I discovered today that if I run Eigen and my own GEMM code on a large dense matrix (1000x1000) I get better performance using four threads instead of eight. The efficiency jumped from 45% to 65%. I think this can be also seen in this plot
https://plafrim.bordeaux.inria.fr/doku.php?id=people:guenneba
The difference is quite substantial. However, the performance is much less stable. The performance jumps around quit a bit each iteration both with Eigen and my own GEMM code. I'm surprised that Hyperthreading makes the performance so much worse. I guess this is not not a question. It's an unexpected observation which I'm hoping to find feedback on.
I see that not using hyper threading is also suggested here.
How to speed up Eigen library's matrix product?
I do have a question regarding measuring max performance. What I do now is run CPUz and look at the frequency as I'm running my GEMM code and then use that number in my code (4.3 GHz on one overclocked system I use). Can I trust this number for all threads? How do I know the frequency per thread to determine the maximum? How to I properly account for turbo boost?
The purpose of hyperthreading is to improve CPU usage for code exhibiting high latency. Hyperthreading masks this latency by treating two threads at once thus having more instruction level parallelism.
However, a well written matrix product kernel exhibits an excellent instruction level parallelism and thus exploits nearly 100% of the CPU ressources. Therefore there is no room for a second "hyper" thread, and the overhead of its management can only decrease the overall performance.
Unless I've missed something, always possible, your CPU has one clock shared by all its components so if you measure it's rate at 4.3GHz (or whatever) then that's the rate of all the components for which it makes sense to figure out a rate. Imagine the chaos if this were not so, some cores running at one rate, others at another rate; the shared components (eg memory access) would become unmanageable.
As to hyperthreading actually worsening the performance of your matrix multiplication, I'm not surprised. After all, hyperthreading is a poor-person's parallelisation technique, duplicating instruction pipelines but not functional units. Once you've got your code screaming along pushing your n*10^6 contiguous memory locations through the FPUs a context switch in response to a pipeline stall isn't going to help much. At best the other pipeline will scream along for a while before another context switch robs you of useful clock cycles, at worst all the careful arrangement of data in the memory hierarchy will be horribly mangled at each switch.
Hyperthreading is designed not for parallel numeric computational speed but for improving the performance of a much more general workload; we use general-purpose CPUs in high-performance computing not because we want hyperthreading but because all the specialist parallel numeric CPUs have gone the way of all flesh.
As a provider of multithreaded concurrency services, I have explored how hyperthreading affects performance under a variety of conditions. I have found that with software that limits its own high-utilization threads to no more that the actual physical processors available, the presence or absence of HT makes very little difference. Software that attempts to use more threads than that for heavy computational work, is likely unaware that it is doing so, relying on merely the total processor count (which doubles under HT), and predictably runs more slowly. Perhaps the largest benefit that enabling HT may provide, is that you can max out all physical processors, without bringing the rest of the system to a crawl. Without HT, software often has to leave one CPU free to keep the host system running normally. Hyperthreads are just more switchable threads, they are not additional processors.
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 have built software that I deploy on Windows 2003 server. The software runs as a service continuously and it's the only application on the Windows box of importance to me. Part of the time, it's retrieving data from the Internet, and part of the time it's doing some computations on that data. It's multi-threaded -- I use thread pools of roughly 4-20 threads.
I won't bore you with all those details, but suffice it to say that as I enable more threads in the pool, more concurrent work occurs, and CPU use rises. (as does demand for other resources, like bandwidth, although that's of no concern to me -- I have plenty)
My question is this: should I simply try to max out the CPU to get the best bang for my buck? Intuitively, I don't think it makes sense to run at 100% CPU; even 95% CPU seems high, almost like I'm not giving the OS much space to do what it needs to do. I don't know the right way to identify best balance. I guessing I could measure and measure and probably find that the best throughput is achived at a CPU avg utilization of 90% or 91%, etc. but...
I'm just wondering if there's a good rule of thumb about this??? I don't want to assume that my testing will take into account all kinds of variations of workloads. I'd rather play it a bit safe, but not too safe (or else I'm underusing my hardware).
What do you recommend? What is a smart, performance minded rule of utilization for a multi-threaded, mixed load (some I/O, some CPU) application on Windows?
Yep, I'd suggest 100% is thrashing so wouldn't want to see processes running like that all the time. I've always aimed for 80% to get a balance between utilization and room for spikes / ad-hoc processes.
An approach i've used in the past is to crank up the pool size slowly and measure the impact (both on CPU and on other constraints such as IO), you never know, you might find that suddenly IO becomes the bottleneck.
CPU utilization shouldn't matter in this i/o intensive workload, you care about throughput, so try using a hill climbing approach and basically try programmatically injecting / removing worker threads and track completion progress...
If you add a thread and it helps, add another one. If you try a thread and it hurts remove it.
Eventually this will stabilize.
If this is a .NET based app, hill climbing was added to the .NET 4 threadpool.
UPDATE:
hill climbing is a control theory based approach to maximizing throughput, you can call it trial and error if you want, but it is a sound approach. In general, there isn't a good 'rule of thumb' to follow here because the overheads and latencies vary so much, it's not really possible to generalize. The focus should be on throughput & task / thread completion, not CPU utilization. For example, it's pretty easy to peg the cores pretty easily with coarse or fine-grained synchronization but not actually make a difference in throughput.
Also regarding .NET 4, if you can reframe your problem as a Parallel.For or Parallel.ForEach then the threadpool will adjust number of threads to maximize throughput so you don't have to worry about this.
-Rick
Assuming nothing else of importance but the OS runs on the machine:
And your load is constant, you should aim at 100% CPU utilization, everything else is a waste of CPU. Remember the OS handles the threads so it is indeed able to run, it's hard to starve the OS with a well behaved program.
But if your load is variable and you expect peaks you should take in consideration, I'd say 80% CPU is a good threshold to use, unless you know exactly how will that load vary and how much CPU it will demand, in which case you can aim for the exact number.
If you simply give your threads a low priority, the OS will do the rest, and take cycles as it needs to do work. Server 2003 (and most Server OSes) are very good at this, no need to try and manage it yourself.
I have also used 80% as a general rule-of-thumb for target CPU utilization. As some others have mentioned, this leaves some headroom for sporadic spikes in activity and will help avoid thrashing on the CPU.
Here is a little (older but still relevant) advice from the Weblogic crew on this issue: http://docs.oracle.com/cd/E13222_01/wls/docs92/perform/basics.html#wp1132942
If you feel your load is very even and predictable you could push that target a little higher, but unless your user base is exceptionally tolerant of periodic slow responses and your project budget is incredibly tight, I'd recommend adding more resources to your system (adding a CPU, using a CPU with more cores, etc.) over making a risky move to try to squeeze out another 10% CPU utilization out of your existing platform.
Assume an embedded environment which has either a DSP core(any other processor core).
If i have a code for some application/functionality which is optimized to be one of the best from point of view of Cycles consumed(MCPS) , will it also be a code, best from the point of view of Power consumed by that code in a real hardware system?
Can a code optimized for least MCPS be guaranteed to have least power consumption as well?
I know there are many aspects to be considered here like the architecture of the underlying processor and the hardware system(memory, bus, etc..).
Very difficult to tell without putting a sensitive ammeter between your board and power supply and logging the current drawn. My approach is to test assumptions for various real world scenarios rather than go with the supporting documentation.
No, lowest cycle count will not guarantee lowest power consumption.
It's a good indication, but you didn't take into account that memory bus activity consumes quite a lot of power as well.
Your code may for example have a higher cycle count but lower power consumption if you move often needed data into internal memory (on chip ram). That won't increase the cycle-count of your algorithms but moving the data in- and out the internal memory increases cycle-count.
If your system has a cache as well as internal memory, optimize for best cache utilization as well.
This isn't a direct answer, but I thought this paper (from this answer) was interesting: Real-Time Task Scheduling for Energy-Aware Embedded Systems.
As I understand it, it trying to run each task under the processor's low power state, unless it can't meet the deadline without high power. So in a scheme like that, more time efficient code (less cycles) should allow the processor to spend more time throttled back.