I've now saved a bit of money for the hardware upgrade. What I'd like to know, which is the easiest way to measure which part of hardware is the bottleneck for compiling and should be upgraded?
Are there any clever techniques I could use? I've looked into perfmon, but it has too many counters and isn't very helpful without exact knowledge what should be looked at.
Conditions: Home development, Windows XP Pro, Visual Studio 2008
Thanks!
The question is really "what is maxed out during compilation?"
If you don't want to use perfmon, you can use something like the task monitor.
Run a compile.
See what's maxed out.
Did you go to 100% CPU for the whole time? Get more CPU -- faster or more cores or something.
Did you go to 100% memory for the whole time? Which number matters on the display? The only memory you can buy is "physical" memory. The only factor that matters is physical memory. The other things you see on the meter are not things you buy, they're adjustments to make to the way Windows works.
Did you go to "huge" amounts of I/O? You can't easily tell what's "huge", but you can conclude this. If you're not using memory and not using CPU, then you're using the only resource that's left -- you're I/O bound and you need a faster bus -- which usually means a whole new machine.
A faster HDD is of little or no value -- the bus clock speed is one limiting factor. The bus width is the other limiting factor. No one designs an ass-kicking I/O bus and then saddles it with junk HDD's. Usually, they design the bus that fits a specific cost target based on available HDD's.
A faster HDD is of little or no value -- the bus clock speed is one limiting factor. The bus width is the other limiting factor. No one designs an ass-kicking I/O bus and then saddles it with junk HDD's. Usually, they design the bus that fits a specific cost target based on available HDD's.
Garbage. Modern HDDs are slow compared to the I/O buses they are connected to. Name a single HDD that can max out a SATA 2 interface (and that is even a generation old now) for random IOPS... A hard drive is lucky to hit 10MB/s when the bus is capable of around 280MB/s.
E.g. http://www.anandtech.com/show/2948/3. Even there the SSDs are only hitting 50MB/s. It's clear the IOPs are NOT the bottleneck otherwise the HDD would do just as much as the SSDs.
I've never seen a computer IOPs bound rather than HDD bound. It doesn't happen.
Using the task monitor has already been suggested but the Sys Internals task monitor gives you more information than the built-in Windows task monitor:
Sys Internals task monitor
You might also want to see what other things are running on your PC which are using up memory and / or CPU processing power. It may be possible to remove or only run on demand things which are affecting performance.
Windows XP will only support 3GB of memory using a switch that you have to turn on and
I seem to remember that applications need to be written to actually take this into consideration.
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.)
We've just bought a 32-core Opteron machine, and the speedups we get are a little disappointing: beyond about 24 threads we see no speedup at all (actually gets slower overall) and after about 6 threads it becomes significantly sub-linear.
Our application is very thread-friendly: our job breaks down into about 170,000 little tasks which can each be executed separately, each taking 5-10 seconds. They all read from the same memory-mapped file of size about 4Gb. They make occasional writes to it, but it might be 10,000 reads to each write - we just write a little bit of data at the end of each of the 170,000 tasks. The writes are lock-protected. Profiling shows that the locks are not a problem. The threads use a lot of JVM memory each in non-shared objects and they make very little access to shared JVM objects and of that, only a small percentage of accesses involve writes.
We're programming in Java, on Linux, with NUMA enabled. We have 128Gb RAM. We have 2 Opteron CPU's (model 6274) of 16 cores each. Each CPU has 2 NUMA nodes. The same job running on an Intel quad-core (i.e. 8 cores) scaled nearly linearly up to 8 threads.
We've tried replicating the read-only data to have one-per-thread, in the hope that most lookups can be local to a NUMA node, but we observed no speedup from this.
With 32 threads, 'top' shows the CPU's 74% "us" (user) and about 23% "id" (idle). But there are no sleeps and almost no disk i/o. With 24 threads we get 83% CPU usage. I'm not sure how to interpret 'idle' state - does this mean 'waiting for memory controller'?
We tried turning NUMA on and off (I'm referring to the Linux-level setting that requires a reboot) and saw no difference. When NUMA was enabled, 'numastat' showed only about 5% of 'allocation and access misses' (95% of cache misses were local to the NUMA node). [Edit:] But adding "-XX:+useNUMA" as a java commandline flag gave us a 10% boost.
One theory we have is that we're maxing out the memory controllers, because our application uses a lot of RAM and we think there are a lot of cache misses.
What can we do to either (a) speed up our program to approach linear scalability, or (b) diagnose what's happening?
Also: (c) how do I interpret the 'top' result - does 'idle' mean 'blocked on memory controllers'? and (d) is there any difference in the characteristics of Opteron vs Xeon's?
I also have a 32 core Opteron machine, with 8 NUMA nodes (4x6128 processors, Mangy Cours, not Bulldozer), and I have faced similar issues.
I think the answer to your problem is hinted at by the 2.3% "sys" time shown in top. In my experience, this sys time is the time the system spends in the kernel waiting for a lock. When a thread can't get a lock it then sits idle until it makes its next attempt. Both the sys and idle time are a direct result of lock contention. You say that your profiler is not showing locks to be the problem. My guess is that for some reason the code causing the lock in question is not included in the profile results.
In my case a significant cause of lock contention was not the processing I was actually doing but the work scheduler that was handing out the individual pieces of work to each thread. This code used locks to keep track of which thread was doing which piece of work. My solution to this problem was to rewrite my work scheduler avoiding mutexes, which I have read do not scale well beyond 8-12 cores, and instead use gcc builtin atomics (I program in C on Linux). Atomic operations are effectively a very fine grained lock that scales much better with high core counts. In your case if your work parcels really do take 5-10s each it seems unlikely this will be significant for you.
I also had problems with malloc, which suffers horrible lock issues in high core count situations, but I can't, off the top of my head, remember whether this also led to sys & idle figures in top, or whether it just showed up using Mike Dunlavey's debugger profiling method (How can I profile C++ code running in Linux?). I suspect it did cause sys & idle problems, but I draw the line at digging through all my old notes to find out :) I do know that I now avoid runtime mallocs as much as possible.
My best guess is that some piece of library code you are using implements locks without your knowledge, is not included in your profiling results, and is not scaling well to high core-count situations. Beware memory allocators!
I'm sure the answer will lie in a consideration of the hardware architecture. You have to think of multi core computers as if they were individual machines connected by a network. In fact that's all that Hypertransport and QPI are.
I find that to solve these scalability problems you have to stop thinking in terms of shared memory and start adopting the philosophy of Communicating Sequential Processes. It means thinking very differently, ie imagine how you would write the software if your hardware was 32 single core machines connected by a network. Modern (and ancient) CPU architectures are not designed to give unfettered scaling of the sort you're after. They are designed to allow many different processes to get on with processing their own data.
Like everything else in computing these things go in fashions. CSP dates back to the 1970s, but the very modern and Java derived Scala is a popular embodiment of the concept. See this section on Scala concurrency on Wikipedia.
What the philosophy of CSP does is force you to design a data distribution scheme that fits your data and the problem you're solving. That's not necessarily easy, but if you manage it then you have a solution that will scale very well indeed. Scala may make it easier to develop.
Personally I do everything in CSP and in C. It's allowed me to develop a signal processing application that scales perfectly linearly from 8 cores to several thousand cores (the limit being how big my room is).
The first thing you're going to have to do is actually use NUMA. It isn't a magic setting that you turn on, you have to exploit it in your software's architecture. I don't know about Java, but in C one would bind a memory allocation to a specific core's memory controller (aka memory affinity), and similarly for threads (core affinity) in cases where the OS doesn't get the hint.
I presume that your data doesn't break down into 32 neat, discrete chunks? It's difficult to give advice without knowing exactly the data flows implicit in your program. But think about it in terms of data flow. Draw it out even; Data Flow Diagrams are useful for this (another ancient graphical formal notation). If your picture shows all your data going through a single object (eg through a single memory buffer) then it's going to be slow...
I assume you have optimized your locks, and synchronization made a minimum. In such a case, it still depends a lot on what libraries you are using to program in parallel.
One issue that can happen even if you have no synchronization issue, is memory bus congestion. This is very nasty and difficult to get rid of.
All I can suggest is somehow make your tasks bigger and create fewer tasks. This depends highly on the nature of your problem. Ideally you want as many tasks as the number of cores/threads, but this is not easy (if possible) to achieve.
Something else that can help is to give more heap to your JVM. This will reduce the need to run Garbage Collector frequently, and speeds up a little.
does 'idle' mean 'blocked on memory controllers'
No. You don't see that in top. I mean if the CPU is waiting for memory access, it will be shown as busy. If you have idle periods, it is either waiting for a lock, or for IO.
I'm the Original Poster. We think we've diagnosed the issue, and it's not locks, not system calls, not memory bus congestion; we think it's level 2/3 CPU cache contention.
To reiterate, our task is embarrassingly parallel so it should scale well. However, one thread has a large amount of CPU cache it can access, but as we add more threads, the amount of CPU cache each process can access gets lower and lower (the same amount of cache divided by more processes). Some levels on some architectures are shared between cores on a die, some are even shared between dies (I think), and it may help to get "down in the weeds" with the specific machine you're using, and optimise your algorithms, but our conclusion is that there's not a lot we can do to achieve the scalability we thought we'd get.
We identified this as the cause by using 2 different algorithms. The one which accesses more level 2/3 cache scales much worse than the one which does more processing with less data. They both make frequent accesses to the main data in main memory.
If you haven't tried that yet: Look at hardware-level profilers like Oracle Studio has (for CentOS, Redhat, and Oracle Linux) or if you are stuck with Windows: Intel VTune. Then start looking at operations with suspiciously high clocks per instruction metrics. Suspiciously high mean a lot higher than the same code on a single-numa, single-L3-cache machine (like current Intel desktop CPUs).
I am doing research about dedicated I/O software that would run on consumer hardware. Essentially it boils down to saving huge data streams for later processing. Right now I am looking for a model to estimate performance factors on x86.
Take for example the new Macbook Pro:
high-speed Thunderbolt I/O (input/output) technology delivers
an amazing 10 gigabits per second of transfer speeds in both
directions
1.25 GB/s sounds nice but most processors of the day are clocked around 2 Ghz. Multiple cores make little difference as long as only one can be assigned per network channel.
So even if the software acts as a miniature operating system and limits itself to network/disk operations, the amount of data flowing to storage can't be greater than P / (2 * N)[1] chunks per second. Although this hints the rough performance limit, I feel it's far from adequate.
What other considerations should one take estimating I/O performance in regards to processor frequency and other hardware specifics? For simplicity's sake, assume here that storage performs instantly under all circumstances.
[1] P - processor frequency; N - algorithm overhead
The hardware limiting factors are probably the I/O bus performance, say PCIe, and more recently, the FSB clock-rates, since memory controllers are moving from northbridge to the CPUs themselves.
Then, of course, you have to figure out what sort of processing you need to do on the input, and how much work it is to produce the output. These, at least for conventional software running on a CPU, are dependent on the processor clock, but not only. Writing your code to take advantage of the hardware facilities like caches, instruction-level parallelism, etc. is still a black art but can give you an order of magnitude performance boost.
Basically what I'm ranting about is that not all software is created equal, and you probably want to take that into account.
Likely, harddisk controllers will decide the harddisk I/O performance, graphics cards will decide maximum resolution and refresh I/O performance, and so on. Don't really understand the question, the CPU is becoming less and less involved in these kinds of things (well, has been for the last 10 years).
I doubt the question will even have bearing on CPUs with integrated GPUs, since the buffer to be output to screen is in external memory sharing a bus with (again) a controller on the motherboard.
It's all buffered, so I can only see CPUs affecting file performance if you somehow force the hardware buffer size to something insanely puny. Edit: and I'm pretty sure Apple will prevent you from doing such things. ;)
For Thunderbolt specifically, it's more about what the minimum CPU model is, that supports the kinds of bus speeds required by the Thunderbolt chip set version that is in the machine in question.
Thunderbolt is a raw data traffic system and performance specs are potential maximums, hence all the asterisks in the Apple specs. I believe it will indeed alleviate bottlenecks and in general give lag-free intelligent data shuffling doing many things simultaneously.
The CPU will idle-wait a shorter time for needed data, but the processing speed of the data is the same. When playing or creating a movie, codec processing time will be the same, but you will still feel a boost/lack of lag because the data is there when it needs it. For the I/O, the bottleneck will become the read/write speed of your harddisk instead, and the CPU bottleneck (for file copy operations, likely at least some code in Finder) will stay the same.
In other words, only CPU-intensive tasks such as for example movie encoding will benefit significantly from a faster CPU, while the benefits of Thunderbolt vs. a mix of interfaces will boost machines with both slow and fast CPUs.
I have a highly threaded program but I believe it is not able to scale well across multiple cores because it is already saturating all the memory bandwidth.
Is there any tool out there which allows to measure how much of the memory bandwidth is being used?
Edit: Please note that typical profilers show things like memory leaks and memory allocation, which I am not interested in.
I am only whether the memory bandwidth is being saturated or not.
If you have a recent Intel processor, you might try to use Intel(r) Performance Counter Monitor: http://software.intel.com/en-us/articles/intel-performance-counter-monitor/ It can directly measure consumed memory bandwidth from the memory controllers.
I'd recommend the Visual Studio Sample Profiler which can collect sample events on specific hardware counters. For example, you can choose to sample on cache misses. Here's an article explaining how to choose the CPU counter, though there are other counters you can play with as well.
it would be hard to find a tool that measured memory bandwidth utilization for your application.
But since the issue you face is a suspected memory bandwidth problem, you could try and measure if your application is generating a lot of page faults / sec, which would definitely mean that you are no where near the theoretical memory bandwidth.
You should also measure how cache friendly your algorithms are. If they are thrashing the cache, your memory bandwidth utilization will be severely hampered. Google "measuring cache misses" on good sources that tells you how to do this.
It isn't possible to properly measure memory bus utilisation with any kind of software-only solution. (it used to be, back in the 80's or so. But then we got piplining, cache, out-of-order execution, multiple cores, non-uniform memory architectues with multiple busses, etc etc etc).
You absolutely have to have hardware monitoring the memory bus, to determine how 'busy' it is.
Fortunately, most PC platforms do have some, so you just need the drivers and other software to talk to it:
wenjianhn comments that there is a project specficially for intel hardware (which they call the Processor Counter Monitor) at https://github.com/opcm/pcm
For other architectures on Windows, I am not sure. But there is a project (for linux) which has a grab-bag of support for different architectures at https://github.com/RRZE-HPC/likwid
In principle, a computer engineer could attach a suitable oscilloscope to almost any PC and do the monitoring 'directly', although this is likely to require both a suitably-trained computer engineer as well as quite high performance test instruments (read: both very costly).
If you try this yourself, know that you'll likely need instruments or at least analysis which is aware of the protocol of the bus you're intending to monitor for utilisation.
This can sometimes be really easy, with some busses - eg old parallel FIFO hardware, which usually has a separate wire for 'fifo full' and another for 'fifo empty'.
Such chips are used usually between a faster bus and a slower one, on a one-way link. The 'fifo full' signal, even it it normally occasionally triggers, can be monitored for excessively 'long' levels: For the example of a USB 2.0 Hi-Speed link, this happens when the OS isn't polling the USB fifo hardware on time. Measuring the frequency and duration of these 'holdups' then lets you measure bus utilisation, but only for this USB 2.0 bus.
For a PC memory bus, I guess you could also try just monitoring how much power your RAM interface is using - which perhaps may scale with use. This might be quite difficult to do, but you may 'get lucky'. You want the current of the supply which feeds VccIO for the bus. This should actually work much better for newer PC hardware than those ancient 80's systems (which always just ran at full power when on).
A fairly ordinary oscilloscope is enough for either of those examples - you just need one that can trigger only on 'pulses longer than a given width', and leave it running until it does, which is a good way to do 'soak testing' over long periods.
You monitor utiliation either way by looking for the change in 'idle' time.
But modern PC memory busses are quite a bit more complex, and also much faster.
To do it directly by tapping the bus, you'll need at least an oscilloscope (and active probes) designed explicitly for monitoring the generation of DDR bus your PC has, along with the software analysis option (usually sold separately) to decode the protocol enough to figure out the kind of activity which is occuring on it, from which you can figure out what kind of activity you want to measure as 'idle'.
You may even need a motherboard designed to allow you to make those measurements also.
This isn't so staightfoward as just looking for periods of no activity - all DRAM needs regular refresh cycles at the very least, which may or may not happen along with obvious bus activity (some DRAM's do it automatically, some need a specific command to trigger it, some can continue to address and transfer data from banks not in refresh, some can't, etc).
So the instrument needs to be able to analyse the data deeply enough for you extract how busy it is.
Your best, and simplest bet is to find a PC hardware (CPU) vendor who has tools which do what you want, and buy that hardware so you can use those tools.
This might even involve running your application in a VM, so you can benefit from better tools in a different OS hosting it.
To this end, you'll likely want to try Linux KVM (yes, even for Windows - there are windows guest drivers for it), and also pin down your VM to specific CPUs, whilst you also configure linux to avoid putting other jobs on those same CPUs.
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.