For example, first I run a benchmark program when the load average is 0.00,
then, I run some cpu-consuming task to generate some load to 10.00, then kill it.
next, now cpu usage is 0 but load average is 10.00, if I run the benchmark program again, will the load average affect the result?
No, but that doesn't mean your benchmark will run the same.
The answer to your question is no. The load average is a reported value. It is meant to give you an idea of the state of the system, averaged over several periods of time. Since it is averaged, it takes time for it to go back to 0 after a heavy load was placed on the system.
This is just a report, however. Your system isn't really loaded, and the CPU isn't currently taken. A new benchmark you'll run is unaffected by the system's state 5 minutes ago.
With that said, what is true for CPU may not be true for memory. If your loader uses a lot of memory, the kernel might push less used memory into the swap. It will also reduce the amount of memory it has for file cache. Depending on your benchmark, that might affect the benchmark's performance.
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I have quad core ubuntu system. say If I see the load average as 60 in last 15 mins during peak time. Load average goes to 150 as well.
This loads happens generally only during peak time. Basically I want to know if there is any standard formula to derive the number of cores ideally required to handle the given load ?
Objective :-
If consider the load as 60 then it means 60 task were in queue on an average at any point of time in last 15 mins ? Adding cpu can help me to server the
request faster or save system from hang or crashing .
Linux load average (as printed by uptime or top) includes tasks in I/O wait, so it can have very little to do with CPU time that could potentially be used in parallel.
If all the tasks were purely CPU bound, then yes 150 sustained load average would mean that potentially 150 cores could be useful. (But if it's not sustained, then it might just be a temporary long queue that wouldn't get that long if you had better CPU throughput.)
If you're getting crashes, that's a huge problem that isn't explainable by high loads. (Unless it's from the out-of-memory killer kicking in.)
It might help to use vmstat or dstat to see how much CPU time is spent in user/kernel space when your load avg. is building up, or if it's probably mostly I/O.
Or of course you probably know what tasks are running on your machine, and whether one single task is I/O bound or CPU bound on an otherwise-idle machine. I/O throughput usually scales a bit positively with queue depth, except on magnetic hard drives when that turns sequential read/write into seek-heavy workloads.
For my research I need a CPU benchmark to do some experiments on my Ubuntu laptop (Ubuntu 15.10, Memory 7.7 GiB, Intel Core i7-4500U CPU # 1.80HGz x 4, 64bit). In an ideal world, I would like to have a benchmark satisfying the following:
The CPU should be an official benchmark rather than created by my own for transparency purposes.
The time needed to execute the benchmark on my laptop should be at least 5 minutes (the more the better).
The benchmark should result in different levels of CPU throughout execution. For example, I don't want a benchmark which permanently keeps the CPU utilization level at around 100% - so I want a benchmark which will make the CPU utilization vary over time.
Especially points 2 and 3 are really key for my research. However, I couldn't find any suitable benchmarks so far. Benchmarks I found so far include: sysbench, CPU Fibonacci, CPU Blowfish, CPU Cryptofish, CPU N-Queens. However, all of them just need a couple of seconds to complete and the utilization level on my laptop is at 100% constantly.
Question: Does anyone know about a suitable benchmark for me? I am also happy to hear any other comments/questions you have. Thank you!
To choose a benchmark, you need to know exactly what you're trying to measure. Your question doesn't include that, so there's not much anyone can tell you without taking a wild guess.
If you're trying to measure how well Turbo clock speed works to make a power-limited CPU like your laptop run faster for bursty workloads (e.g. to compare Haswell against Skylake's new and improved power management), you could just run something trivial that's 1 second on, 2 seconds off, and count how many loop iterations it manages.
The duty cycle and cycle length should be benchmark parameters, so you can make plots. e.g. with very fast on/off cycles, Skylake's faster-reacting Turbo will ramp up faster and drop down to min power faster (leaving more headroom in the bank for the next burst).
The speaker in that talk (the lead architect for power management on Intel CPUs) says that Javascript benchmarks are actually bursty enough for Skylake's power management to give a measurable speedup, unlike most other benchmarks which just peg the CPU at 100% the whole time. So maybe have a look at Javascript benchmarks, if you want to use well-known off-the-shelf benchmarks.
If rolling your own, put a loop-carried dependency chain in the loop, preferably with something that's not too variable in latency across microarchitectures. A long chain of integer adds would work, and Fibonacci is a good way to stop the compiler from optimizing it away. Either pick a fixed iteration count that works well for current CPU speeds, or check the clock every 10M iterations.
Or set a timer that will fire after some time, and have it set a flag that you check inside the loop. (e.g. from a signal handler). Specifically, alarm(2) may be a good choice. Record how many iterations you did in this burst of work.
I have a linux dedicated server machine(8cores 8gbRAM) where i run some crawler php scripts. The load on the system ends up being arround 200, which sounds a lot. Since i am not using the machine to host content, what could be the sideeffects of such high level of load for the purposes stated above.
Machines were made to work so there are no issues with high load average, per se. But, a high load average can be an indicator of a performance issue, often. Such investigation is usually application specific, but here is a very general guideline:
Since load average is a combined metric of (CPU, IO .. etc) you want to examine all separately. I would start with making sure the machine is not thrashing, by checking swap usage (vmstat comes in handy), and disk performance (using iostat). You may also check if your operations are CPU intensive.
You should read your load average value as a 3 component value (1 minute load, 5 minutes load and 15 minutes load respectively).
Take a look at the example taken from Wiki:
For example, one can interpret a load average of "1.73 0.60 7.98" on a single-CPU system as:
during the last minute, the system was overloaded by 73% on average (1.73 runnable processes, so that 0.73 processes had to wait for a turn for a single CPU system on average).
during the last 5 minutes, the CPU was idling 40% of the time on average.
during the last 15 minutes, the system was overloaded 698% on average (7.98 runnable processes, so that 6.98 processes had to wait for a turn for a single CPU system on average).
Full article
Please note that this value depends on the resources of your machine.
Cheers!
I read some articles about the CPU load average. They were talking about the definition, the differences between the CPU usage, and the optimal value (roughly equals to the number of cores). They also mentioned that if the number is high, you will be in trouble (waking up at mid-night etc.), but what would actually be happening if the number is high?
For example, I have been running 4, 6 and 8 sessions on a 4 core Linux server. Although the time it took to finish the task were different (4 fasted, 8 slowest), the results seem OK. The CPU load averages were roughly 4, 8 and 10. I understand that 10 might not be a good number, but then what?
It's just that: if you run absurdly high load averages, the overall efficiency will suffer: the CPU processing power will go to waste.
This is caused by several factors; the most immediate being more CPU time needed for scheduling the competing tasks. One not at all insignificant factor is that several competing processes will also overutlize the CPU cache; each task switch effectively throwing out the cache contents and replacing them with new ones. Further choke points come in forms of bottlenecks in memory and storage bandwidths.
We are testing our software for the first time on a machine with > 12 cores for scalability and we are encountering a nasty drop in performance after the 12th thread is added. After spending a couple days on this, we are stumped regarding what to try next.
The test system is a dual Opteron 6174 (2x12 cores) with 16 GB of memory, Windows Server 2008 R2.
Basically, performance peaks from 10 - 12 threads, then drops off a cliff and is soon performing work at about the same rate it was with about 4 threads. The drop-off is fairly steep and by 16 - 20 threads it reaches bottom in terms of throughput. We have tested both with a single process running multiple threads and as multiple processes running single threads-- the results are pretty much the same. The processing is fairly memory intensive and somewhat disk intensive.
We are fairly certain this is a memory bottleneck, but we don't believe it a cache issue. The evidence is as follows:
CPU usages continues to climb from 50 to 100% when scaling from 12 to 24 threads. If we were having synchronization/deadlock issues, we would have expected CPU usage to top out before reaching 100%.
Testing while copying a large amount of files in the background had very little impact on the processing rates. We think this rules out disk i/o as the bottleneck.
The commit charge is only about 4 GBs, so we should be well below the threshold in which paging would become an issue.
The best data comes from using AMD's CodeAnalyst tool. CodeAnalyst shows the windows kernel goes from taking about 6% of the cpu time with 12 threads to 80-90% of CPU time when using 24 threads. A vast majority of that time is spent in the ExAcquireResourceSharedLite (50%) and KeAcquireInStackQueuedSpinLockAtDpcLevel (46%) functions. Here are the highlights of the kernel's factor change when going from running with 12 threads to running with 24:
Instructions: 5.56 (times more)
Clock cycles: 10.39
Memory operations: 4.58
Cache miss ratio: 0.25 (actual cache miss ratio is 0.1, 4 times smaller than with 12 threads)
Avg cache miss latency: 8.92
Total cache miss latency: 6.69
Mem bank load conflict: 11.32
Mem bank store conflict: 2.73
Mem forwarded: 7.42
We thought this might be evidence of the problem described in this paper, however we found that pinning each worker thread/process to a particular core didn't improve the results at all (if anything, performance got a little worse).
So that's where we're at. Any ideas on the precise cause of this bottleneck or how we might avoid it?
I'm not sure that I understand the issues completely such that I can offer you a solution but from what you've explained I may have some alternative view points which may be of help.
I program in C so what works for me may not be applicable in your case.
Your processors have 12MB of L3 and 6MB of L2 which is big but in my view they're seldom big enough!
You're probably using rdtsc for timing individual sections. When I use it I have a statistics structure into which I send the measurement results from different parts of the executing code. Average, minimum, maximum and number of observations are obvious but also standard deviation has its place in that it can help you decide whether a large maximum value should be researched or not. Standard deviation only needs to be calculated when it needs to be read out: until then it can be stored in its components (n, sum x, sum x^2). Unless you're timing very short sequences you can omit the preceding synchronizing instruction. Make sure you quantifiy the timing overhead, if only to be able to rule it out as insignificant.
When I program multi-threaded I try to make each core's/thread's task as "memory limited" as possible. By memory limited I mean not doing things which requires unnecessary memory access. Unnecessary memory access usually means as much inline code as possible and as litte OS access as possible. To me the OS is a great unknown in terms of how much memory work a call to it will generate so I try to keep calls to it to a minimum. In the same manner but usually to a lesser performance impacting extent I try to avoid calling application functions: if they must be called I'd rather they didn't call a lot of other stuff.
In the same manner I minimize memory allocations: if I need several I add them together into one and then subdivide that one big allocation into smaller ones. This will help later allocations in that they will need to loop through fewer blocks before finding the block returned. I only block initialize when absolutely necessary.
I also try to reduce code size by inlining. When moving/setting small blocks of memory I prefer using intrinsics based on rep movsb and rep stosb rather than calling memcopy/memset which are usually both optimized for larger blocks and not especially limited in size.
I've only recently begun using spinlocks but I implement them such that they become inline (anything is better than calling the OS!). I guess the OS alternative is critical sections and though they are fast local spinlocks are faster. Since they perform additional processing it means that they prevent application processing from being performed during that time. This is the implementation:
inline void spinlock_init (SPINLOCK *slp)
{
slp->lock_part=0;
}
inline char spinlock_failed (SPINLOCK *slp)
{
return (char) __xchg (&slp->lock_part,1);
}
Or more elaborate (but not overly so):
inline char spinlock_failed (SPINLOCK *slp)
{
if (__xchg (&slp->lock_part,1)==1) return 1;
slp->count_part=1;
return 0;
}
And to release
inline void spinlock_leave (SPINLOCK *slp)
{
slp->lock_part=0;
}
Or
inline void spinlock_leave (SPINLOCK *slp)
{
if (slp->count_part==0) __breakpoint ();
if (--slp->count_part==0) slp->lock_part=0;
}
The count part is something I've brought along from embedded (and other programming) where it is used for handling nested interrupts.
I'm also a big fan of IOCPs for their efficiency in handling IO events and threads but your description does not indicate whether your application could use them. In any case you appear to economize on them, which is good.
To address your bullet points:
1) If you have 12 cores at 100% usage and 12 cores idle, then your total CPU usage would be 50%. If your synchronization is spinlock-esque, then your threads would still be saturating their CPUs even while not accomplishing useful work.
2) skipped
3) I agree with your conclusion. In the future, you should know that Perfmon has a counter: Process\Page Faults/sec that can verify this.
4) If you don't have the private symbols for ntoskrnl, CodeAnalyst may not be able to tell you the correct function names in its profile. Rather, it can only point to the nearest function for which it has symbols. Can you get stack traces with the profiles using CodeAnalyst? This could help you determine what operation your threads perform that drives the kernel usage.
Also, my former team at Microsoft has provided a number of tools and guidelines for performance analysis here, including taking stack traces on CPU profiles.