I am running vtune -collect memory-access ./main and I receive the output below. The main binary does a lot of random memory accesses on a large virtual and physical memory range.
Memory Bound
LLC Miss: 0.0% of Clockticks
DRAM Bandwidth Bound: 0.0% of Elapsed Time
LLC Miss Count: 0
Average Latency (cycles): 19
Total Thread Count: 2
Paused Time: 0s
The input seems incorrect since there are actually many LLC misses, and the uarch-exploration report shows a 100% LLC replacement percentage (though the 100% result seems incorrect, too). On the other hand, other stats outputted by the uarch-exploration report (e.g., CPI rate) seem reasonable. Is there something I need to do to get vtune to work correctly? Is it possible that maybe vtune just does not fully support my CPU version and so only some of its features work?
Vtune shows this kind of output only when an executable runs in negligible time or if there is some issue with your executable. Please make sure that there are no issues while running your executable.
When it comes to rating the performance of a processor, is calculating the Million Instructions Per Second (MIPS) a practical measure to use?
Or is finding the Execution Time (IC x CPI x 1/CR) the main thing to use?
Imagine you have one CPU that does 100 million tiny little instructions that don't do much on their own per second. Next; imagine you have another CPU where you need a quarter of the instructions to do the same work; which can do 50 million larger instructions per second. The second CPU has half as many MIPs but is twice as fast.
Now.. Imagine you have 2 CPUs that both execute the exact same instructions; where one CPU runs at 1 GHz, can do 5 instructions per cycle, and stalls rarely; and the other CPU runs at 4 GHz, can only do 2 instructions per cycle, and spends a lot more time stalled doing nothing (due to cache misses, branch mispredictions, etc). In this case the 1 GHz CPU might be significantly faster than the 4 GHz CPU.
Finally; imagine you have 2 CPUs that both execute the exact same instructions, both have exactly the same clock frequency, both execute the same number of instructions per cycle, and both spend exactly the same amount of time stalled. One CPU has overheats easily and had to "under-clock" itself to a crawl after 250 milliseconds of not being idle just to avoid melting itself, and the other CPU can go at max. speed continuously without ever overheating.
Execution time is how long it takes to do some work taking everything into account (and can be extremely different for different types of work); while MIPS is like a real estate agent determining how much a building is worth by measuring the weight of a rubber chicken.
I have developed a high performance Cholesky factorization routine, which should have peak performance at around 10.5 GFLOPs on a single CPU (without hyperthreading). But there is some phenomenon which I don't understand when I test its performance. In my experiment, I measured the performance with increasing matrix dimension N, from 250 up to 10000.
In my algorithm I have applied caching (with tuned blocking factor), and data are always accessed with unit stride during computation, so cache performance is optimal; TLB and paging problem are eliminated;
I have 8GB available RAM, and the maximum memory footprint during experiment is under 800MB, so no swapping comes across;
During experiment, no resource demanding process like web browser is running at the same time. Only some really cheap background process is running to record CPU frequency as well as CPU temperature data every 2s.
I would expect the performance (in GFLOPs) should maintain at around 10.5 for whatever N I am testing. But a significant performance drop is observed in the middle of the experiment as shown in the first figure.
CPU frequency and CPU temperature are seen in the 2nd and 3rd figure. The experiment finishes in 400s. Temperature was at 51 degree when experiment started, and quickly rose up to 72 degree when CPU got busy. After that it grew slowly to the highest at 78 degree. CPU frequency is basically stable, and it did not drop when temperature got high.
So, my question is:
since CPU frequency did not drop, why performance suffers?
how exactly does temperature affect CPU performance? Does the increment from 72 degree to 78 degree really make things worse?
CPU info
System: Ubuntu 14.04 LTS
Laptop model: Lenovo-YOGA-3-Pro-1370
Processor: Intel Core M-5Y71 CPU # 1.20 GHz * 2
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Byte Order: Little Endian
CPU(s): 4
On-line CPU(s) list: 0,1
Off-line CPU(s) list: 2,3
Thread(s) per core: 1
Core(s) per socket: 2
Socket(s): 1
NUMA node(s): 1
Vendor ID: GenuineIntel
CPU family: 6
Model: 61
Stepping: 4
CPU MHz: 1474.484
BogoMIPS: 2799.91
Virtualisation: VT-x
L1d cache: 32K
L1i cache: 32K
L2 cache: 256K
L3 cache: 4096K
NUMA node0 CPU(s): 0,1
CPU 0, 1
driver: intel_pstate
CPUs which run at the same hardware frequency: 0, 1
CPUs which need to have their frequency coordinated by software: 0, 1
maximum transition latency: 0.97 ms.
hardware limits: 500 MHz - 2.90 GHz
available cpufreq governors: performance, powersave
current policy: frequency should be within 500 MHz and 2.90 GHz.
The governor "performance" may decide which speed to use
within this range.
current CPU frequency is 1.40 GHz.
boost state support:
Supported: yes
Active: yes
update 1 (control experiment)
In my original experiment, CPU is kept busy working from N = 250 to N = 10000. Many people (primarily those whose saw this post before re-editing) suspected that the overheating of CPU is the major reason for performance hit. Then I went back and installed lm-sensors linux package to track such information, and indeed, CPU temperature rose up.
But to complete the picture, I did another control experiment. This time, I give CPU a cooling time between each N. This is achieved by asking the program to pause for a number of seconds at the start of iteration of the loop through N.
for N between 250 and 2500, the cooling time is 5s;
for N between 2750 and 5000, the cooling time is 20s;
for N between 5250 and 7500, the cooling time is 40s;
finally for N between 7750 and 10000, the cooling time is 60s.
Note that the cooling time is much larger than the time spent for computation. For N = 10000, only 30s are needed for Cholesky factorization at peak performance, but I ask for a 60s cooling time.
This is certainly a very uninteresting setting in high performance computing: we want our machine to work all the time at peak performance, until a very large task is completed. So this kind of halt makes no sense. But it helps to better know the effect of temperature on performance.
This time, we see that peak performance is achieved for all N, just as theory supports! The periodic feature of CPU frequency and temperature is the result of cooling and boost. Temperature still has an increasing trend, simply because as N increases, the work load is getting bigger. This also justifies more cooling time for a sufficient cooling down, as I have done.
The achievement of peak performance seems to rule out all effects other than temperature. But this is really annoying. Basically it says that computer will get tired in HPC, so we can't get expected performance gain. Then what is the point of developing HPC algorithm?
OK, here are the new set of plots:
I don't know why I could not upload the 6th figure. SO simply does not allow me to submit the edit when adding the 6th figure. So I am sorry I can't attach the figure for CPU frequency.
update 2 (how I measure CPU frequency and temperature)
Thanks to Zboson for adding the x86 tag. The following bash commands are what I used for measurement:
while true
do
cat /sys/devices/system/cpu/cpu0/cpufreq/scaling_cur_freq >> cpu0_freq.txt ## parameter "freq0"
cat sys/devices/system/cpu/cpu1/cpufreq/scaling_cur_freq >> cpu1_freq.txt ## parameter "freq1"
sensors | grep "Core 0" >> cpu0_temp.txt ## parameter "temp0"
sensors | grep "Core 1" >> cpu1_temp.txt ## parameter "temp1"
sleep 2
done
Since I did not pin the computation to 1 core, the operating system will alternately use two different cores. It makes more sense to take
freq[i] <- max (freq0[i], freq1[i])
temp[i] <- max (temp0[i], temp1[i])
as the overall measurement.
TL:DR: Your conclusion is correct. Your CPU's sustained performance is nowhere near its peak. This is normal: the peak perf is only available as a short term "bonus" for bursty interactive workloads, above its rated sustained performance, given the light-weight heat-sink, fans, and power-delivery.
You can develop / test on this machine, but benchmarking will be hard. You'll want to run on a cluster, server, or desktop, or at least a gaming / workstation laptop.
From the CPU info you posted, you have a dual-core-with-hyperthreading Intel Core M with a rated sustainable frequency of 1.20 GHz, Broadwell generation. Its max turbo is 2.9GHz, and it's TDP-up sustainable frequency is 1.4GHz (at 6W).
For short bursts, it can run much faster and make much more heat than it requires its cooling system to handle. This is what Intel's "turbo" feature is all about. It lets low-power ultraportable laptops like yours have snappy UI performance in stuff like web browsers, because the CPU load from interactive is almost always bursty.
Desktop/server CPUs (Xeon and i5/i7, but not i3) do still have turbo, but the sustained frequency is much closer to the max turbo. e.g. a Haswell i7-4790k has a sustained "rated" frequency of 4.0GHz. At that frequency and below, it won't use (and convert to heat) more than its rated TDP of 88W. Thus, it needs a cooling system that can handle 88W. When power/current/temperature allow, it can clock up to 4.4GHz and use more than 88W of power. (The sliding window for calculating the power history to keep the sustained power with 88W is sometimes configurable in the BIOS, e.g. 20sec or 5sec. Depending on what code is running, 4.4GHz might not increase the electrical current demand to anywhere near peak. e.g. code with lots of branch mispredicts that's still limited by CPU frequency, but that doesn't come anywhere near saturating the 256b AVX FP units like Prime95 would.)
Your laptop's max turbo is a factor of 2.4x higher than rated frequency. That high-end Haswell desktop CPU can only upclock by 1.1x. The max sustained frequency is already pretty close to the max peak limits, because it's rated to need a good cooling system that can keep up with that kind of heat production. And a solid power supply that can supply that much current.
The purpose of Core M is to have a CPU that can limit itself to ultra low power levels (rated TDP of 4.5 W at 1.2GHz, 6W at 1.4GHz). So the laptop manufacturer can safely design a cooling and power delivery system that's small and light, and only handles that much power. The "Scenario Design Power" is only 3.5W, and that's supposed to represent the thermal requirements for real-world code, not max-power stuff like Prime95.
Even a "normal" ULV laptop CPU is rated for 15W sustained, and high power gaming/workstation laptop CPUs at 45W. And of course laptop vendors put those CPUs into machines with beefier heat-sinks and fans. See a table on wikipedia, and compare desktop / server CPUs (also on the same page).
The achievement of peak performance seems to rule out all effects
other than temperature. But this is really annoying. Basically it says
that computer will get tired in HPC, so we can't get expected
performance gain. Then what is the point of developing HPC algorithm?
The point is to run them on hardware that's not so badly thermally limited! An ultra-low-power CPU like a Core M makes a decent dev platform, but not a good HPC compute platform.
Even a laptop with an xxxxM CPU, rather than a xxxxU CPU, will do ok. (e.g. a "gaming" or "workstation" laptop that's designed to run CPU-intensive stuff for sustained periods). Or in Skylake-family, "xxxxH" or "HK" are the 45W mobile CPUs, at least quad-core.
Further reading:
Modern Microprocessors
A 90-Minute Guide!
[Power Delivery in a Modern Processor] - general background, including the "power wall" that Pentium 4 ran into.
(https://www.realworldtech.com/power-delivery/) - really deep technical dive into CPU / motherboard design and the challenges of delivering stable low-voltage to very bursty demands, and reacting quickly to the CPU requesting more / less voltage as it changes frequency.
simple problem from Wilkinson and Allen's Parallel Programming: Techniques and Applications Using Networked Workstations and Parallel Computers. Working through the exercises at the end of the first chapter and want to make sure that I'm on the right track. The full question is:
1-11 A multiprocessor consists of 10 processors, each capable of a peak execution rate of 200 MFLOPs (millions of floating point operations per second). What is the performance of the system as measured in MFLOPs when 10% of the code is sequential and 90% is parallelizable?
I assume the question wants me to find the number of operations per second of a serial processor which would take the same amount of time to run the program as the multiprocessor.
I think I'm right in thinking that 10% of the program is run at 200 MFLOPs, and 90% is run at 2,000 MFLOPs, and that I can average these speeds to find the performance of the multiprocessor in MFLOPs:
1/10 * 200 + 9/10 * 2000 = 1820 MFLOPs
So when running a program which is 10% serial and 90% parallelizable the performance of the multiprocessor is 1820 MFLOPs.
Is my approach correct?
ps: I understand that this isn't exactly how this would work in reality because it's far more complex, but I would like to know if I'm grasping the concepts.
Your calculation would be fine if 90% of the time, all 10 processors were fully utilized, and 10% of the time, just 1 processor was in use. However, I don't think that is a reasonable interpretation of the problem. I think it is more reasonable to assume that if a single processor were used, 10% of its computations would be on the sequential part, and 90% of its computations would be on the parallelizable part.
One possibility is that the sequential part and parallelizable parts can be run in parallel. Then one processor could run the sequential part, and the other 9 processors could do the parallelizable part. All processors would be fully used, and the result would be 2000 MFLOPS.
Another possibility is that the sequential part needs to be run first, and then the parallelizable part. If a single processor needed 1 hour to do the first part, and 9 hours to do the second, then it would take 10 processors 1 + 0.9 = 1.9 hours total, for an average of about (1*200 + 0.9*2000)/1.9 ~ 1053 MFLOPS.
When I write a simple application, running for 10 minutes, that starts 10 threads once (pthreads), each sleeping for 1 ms in a loop (not doing anything else) the CPU is used ca. 44% (top reports that). It is a ARM9 CPU with 450 MHz, Linux 2.6.37 is used as OS. There is no other program running, it tried out different kernel configs (Dynamic Ticks, Soft/Hard IRQ, High Resolution Timer, ..., ..., ...), different priorities (up to 99) but the numbers stay the same. /usr/bin/time -v shows ca. 5'200'000 voluntary context switches and ca. 3 minutes are spent in Kernel space. Sleepin in each thread for ca. 5 ms and the CPU utilization goes down to ca. 9% which is IMO still crazy (40'500'000 cycles to safe some registers). clock_nanosleep was used for sleeping (CLOCK_REALTIME/CLOCK_MONOTONIC did not change anything).
I'm aware that a full context switch is expensive on ARM9 because caches have to be cleared. But a simple thread switch, or switch to the OS shouldn't be that expensive IMHO (address space remains the same, no cache/TLB flushing required). Is this common or should I try to find the bottleneck in the kernel?
You're busily waking up and going back to sleep at 100uS intervals -- 10 threads, 1ms, that's 100uS on average. And keep in mind that you have two context switches for each of those 100uS intervals, so you have a context switch every 50uS on average, or 20,000 times per second.
Perhaps that's the answer you're looking for?