Why is clock_gettime so erratic? - linux

Intro
Section Old Question contains the initial question (Further Investigation and Conclusion have been added since).
Skip to the section Further Investigation below for a detailed comparison of the different timing methods (rdtsc, clock_gettime and QueryThreadCycleTime).
I believe the erratic behaviour of CGT can be attributed to either a buggy kernel or a buggy CPU (see section Conclusion).
The code used for testing is at the bottom of this question (see section Appendix).
Apologies for the length.
Old Question
In short: I am using clock_gettime to measure the execution time of many code segments. I am experiencing very inconsistent measurements between separate runs. The method has an extremely high standard deviation when compared to other methods (see Explanation below).
Question: Is there a reason why clock_gettime would give so inconsistent measurements when compared to other methods? Is there an alternative method with the same resolution that accounts for thread idle time?
Explanation: I am trying to profile a number of small parts of C code. The execution time of each of the code segments is not more than a couple of microseconds. In a single run, each of the code segments will execute some hundreds of times, which produces runs × hundreds of measurements.
I also have to measure only the time the thread actually spends executing (which is why rdtsc is not suitable). I also need a high resolution (which is why times is not suitable).
I've tried the following methods:
rdtsc (on Linux and Windows),
clock_gettime (with 'CLOCK_THREAD_CPUTIME_ID'; on Linux), and
QueryThreadCycleTime (on Windows).
Methodology: The analysis was performed on 25 runs. In each run, separate code segments repeat a 101 of times. Therefore I have 2525 measurements. Then I look at a histogram of the measurements, and also calculate some basic stuff (like the mean, std.dev., median, mode, min, and max).
I do not present how I measured the 'similarity' of the three methods, but this simply involved a basic comparison of proportion of times spent in each code segment ('proportion' means that the times are normalised). I then look at the pure differences in these proportions. This comparison showed that all 'rdtsc', 'QTCT', and 'CGT' measure the same proportions when averaged over the 25 runs. However, the results below show that 'CGT' has a very large standard deviation. This makes it unusable in my use case.
Results:
A comparison of clock_gettime with rdtsc for the same code segment (25 runs of 101 measurements = 2525 readings):
clock_gettime:
1881 measurements of 11 ns,
595 measurements were (distributed almost normally) between 3369 and 3414 ns,
2 measurements of 11680 ns,
1 measurement of 1506022 ns, and
the rest is between 900 and 5000 ns.
Min: 11 ns
Max: 1506022 ns
Mean: 1471.862 ns
Median: 11 ns
Mode: 11 ns
Stddev: 29991.034
rdtsc (note: no context switches occurred during this run, but if it happens, it usually results in just a single measurement of 30000 ticks or so):
1178 measurements between 274 and 325 ticks,
306 measurements between 326 and 375 ticks,
910 measurements between 376 and 425 ticks,
129 measurements between 426 and 990 ticks,
1 measurement of 1240 ticks, and
1 measurement of 1256 ticks.
Min: 274 ticks
Max: 1256 ticks
Mean: 355.806 ticks
Median: 333 ticks
Mode: 376 ticks
Stddev: 83.896
Discussion:
rdtsc gives very similar results on both Linux and Windows. It has an acceptable standard deviation--it is actually quite consistent/stable. However, it does not account for thread idle time. Therefore, context switches make the measurements erratic (on Windows I have observed this quite often: a code segment with an average of 1000 ticks or so will take ~30000 ticks every now and then--definitely because of pre-emption).
QueryThreadCycleTime gives very consistent measurements--i.e. much lower standard deviation when compared to rdtsc. When no context switches happen, this method is almost identical to rdtsc.
clock_gettime, on the other hand, is producing extremely inconsistent results (not just between runs, but also between measurements). The standard deviations are extreme (when compared to rdtsc).
I hope the statistics are okay. But what could be the reason for such a discrepancy in the measurements between the two methods? Of course, there is caching, CPU/core migration, and other things. But none of this should be responsible for any such differences between 'rdtsc' and 'clock_gettime'. What is going on?
Further Investigation
I have investigated this a bit further. I have done two things:
Measured the overhead of just calling clock_gettime(CLOCK_THREAD_CPUTIME_ID, &t) (see code 1 in Appendix), and
in a plain loop called clock_gettime and stored the readings into an array (see code 2 in Appendix). I measure the delta times (difference in successive measurement times, which should correspond a bit to the overhead of the call of clock_gettime).
I have measured it on two different computers with two different Linux Kernel versions:
CGT:
CPU: Core 2 Duo L9400 # 1.86GHz
Kernel: Linux 2.6.40-4.fc15.i686 #1 SMP Fri Jul 29 18:54:39 UTC 2011 i686 i686 i386
Results:
Estimated clock_gettime overhead: between 690-710 ns
Delta times:
Average: 815.22 ns
Median: 713 ns
Mode: 709 ns
Min: 698 ns
Max: 23359 ns
Histogram (left-out ranges have frequencies of 0):
Range | Frequency
------------------+-----------
697 < x ≤ 800 -> 78111 <-- cached?
800 < x ≤ 1000 -> 16412
1000 < x ≤ 1500 -> 3
1500 < x ≤ 2000 -> 4836 <-- uncached?
2000 < x ≤ 3000 -> 305
3000 < x ≤ 5000 -> 161
5000 < x ≤ 10000 -> 105
10000 < x ≤ 15000 -> 53
15000 < x ≤ 20000 -> 8
20000 < x -> 5
CPU: 4 × Dual Core AMD Opteron Processor 275
Kernel: Linux 2.6.26-2-amd64 #1 SMP Sun Jun 20 20:16:30 UTC 2010 x86_64 GNU/Linux
Results:
Estimated clock_gettime overhead: between 279-283 ns
Delta times:
Average: 320.00
Median: 1
Mode: 1
Min: 1
Max: 3495529
Histogram (left-out ranges have frequencies of 0):
Range | Frequency
--------------------+-----------
x ≤ 1 -> 86738 <-- cached?
282 < x ≤ 300 -> 13118 <-- uncached?
300 < x ≤ 440 -> 78
2000 < x ≤ 5000 -> 52
5000 < x ≤ 30000 -> 5
3000000 < x -> 8
RDTSC:
Related code rdtsc_delta.c and rdtsc_overhead.c.
CPU: Core 2 Duo L9400 # 1.86GHz
Kernel: Linux 2.6.40-4.fc15.i686 #1 SMP Fri Jul 29 18:54:39 UTC 2011 i686 i686 i386
Results:
Estimated overhead: between 39-42 ticks
Delta times:
Average: 52.46 ticks
Median: 42 ticks
Mode: 42 ticks
Min: 35 ticks
Max: 28700 ticks
Histogram (left-out ranges have frequencies of 0):
Range | Frequency
------------------+-----------
34 < x ≤ 35 -> 16240 <-- cached?
41 < x ≤ 42 -> 63585 <-- uncached? (small difference)
48 < x ≤ 49 -> 19779 <-- uncached?
49 < x ≤ 120 -> 195
3125 < x ≤ 5000 -> 144
5000 < x ≤ 10000 -> 45
10000 < x ≤ 20000 -> 9
20000 < x -> 2
CPU: 4 × Dual Core AMD Opteron Processor 275
Kernel: Linux 2.6.26-2-amd64 #1 SMP Sun Jun 20 20:16:30 UTC 2010 x86_64 GNU/Linux
Results:
Estimated overhead: between 13.7-17.0 ticks
Delta times:
Average: 35.44 ticks
Median: 16 ticks
Mode: 16 ticks
Min: 14 ticks
Max: 16372 ticks
Histogram (left-out ranges have frequencies of 0):
Range | Frequency
------------------+-----------
13 < x ≤ 14 -> 192
14 < x ≤ 21 -> 78172 <-- cached?
21 < x ≤ 50 -> 10818
50 < x ≤ 103 -> 10624 <-- uncached?
5825 < x ≤ 6500 -> 88
6500 < x ≤ 8000 -> 88
8000 < x ≤ 10000 -> 11
10000 < x ≤ 15000 -> 4
15000 < x ≤ 16372 -> 2
QTCT:
Related code qtct_delta.c and qtct_overhead.c.
CPU: Core 2 6700 # 2.66GHz
Kernel: Windows 7 64-bit
Results:
Estimated overhead: between 890-940 ticks
Delta times:
Average: 1057.30 ticks
Median: 890 ticks
Mode: 890 ticks
Min: 880 ticks
Max: 29400 ticks
Histogram (left-out ranges have frequencies of 0):
Range | Frequency
------------------+-----------
879 < x ≤ 890 -> 71347 <-- cached?
895 < x ≤ 1469 -> 844
1469 < x ≤ 1600 -> 27613 <-- uncached?
1600 < x ≤ 2000 -> 55
2000 < x ≤ 4000 -> 86
4000 < x ≤ 8000 -> 43
8000 < x ≤ 16000 -> 10
16000 < x -> 1
Conclusion
I believe the answer to my question would be a buggy implementation on my machine (the one with AMD CPUs with an old Linux kernel).
The CGT results of the AMD machine with the old kernel show some extreme readings. If we look at the delta times, we'll see that the most frequent delta is 1 ns. This means that the call to clock_gettime took less than a nanosecond! Moreover, it also produced a number of extraordinary large deltas (of more than 3000000 ns)! This seems to be erroneous behaviour. (Maybe unaccounted core migrations?)
Remarks:
The overhead of CGT and QTCT is quite big.
It is also difficult to account for their overhead, because CPU caching seems to make quite a big difference.
Maybe sticking to RDTSC, locking the process to one core, and assigning real-time priority is the most accurate way to tell how many cycles a piece of code used...
Appendix
Code 1: clock_gettime_overhead.c
#include <time.h>
#include <stdio.h>
#include <stdint.h>
/* Compiled & executed with:
gcc clock_gettime_overhead.c -O0 -lrt -o clock_gettime_overhead
./clock_gettime_overhead 100000
*/
int main(int argc, char **args) {
struct timespec tstart, tend, dummy;
int n, N;
N = atoi(args[1]);
clock_gettime(CLOCK_THREAD_CPUTIME_ID, &tstart);
for (n = 0; n < N; ++n) {
clock_gettime(CLOCK_THREAD_CPUTIME_ID, &dummy);
clock_gettime(CLOCK_THREAD_CPUTIME_ID, &dummy);
clock_gettime(CLOCK_THREAD_CPUTIME_ID, &dummy);
clock_gettime(CLOCK_THREAD_CPUTIME_ID, &dummy);
clock_gettime(CLOCK_THREAD_CPUTIME_ID, &dummy);
clock_gettime(CLOCK_THREAD_CPUTIME_ID, &dummy);
clock_gettime(CLOCK_THREAD_CPUTIME_ID, &dummy);
clock_gettime(CLOCK_THREAD_CPUTIME_ID, &dummy);
clock_gettime(CLOCK_THREAD_CPUTIME_ID, &dummy);
clock_gettime(CLOCK_THREAD_CPUTIME_ID, &dummy);
}
clock_gettime(CLOCK_THREAD_CPUTIME_ID, &tend);
printf("Estimated overhead: %lld ns\n",
((int64_t) tend.tv_sec * 1000000000 + (int64_t) tend.tv_nsec
- ((int64_t) tstart.tv_sec * 1000000000
+ (int64_t) tstart.tv_nsec)) / N / 10);
return 0;
}
Code 2: clock_gettime_delta.c
#include <time.h>
#include <stdio.h>
#include <stdint.h>
/* Compiled & executed with:
gcc clock_gettime_delta.c -O0 -lrt -o clock_gettime_delta
./clock_gettime_delta > results
*/
#define N 100000
int main(int argc, char **args) {
struct timespec sample, results[N];
int n;
for (n = 0; n < N; ++n) {
clock_gettime(CLOCK_THREAD_CPUTIME_ID, &sample);
results[n] = sample;
}
printf("%s\t%s\n", "Absolute time", "Delta");
for (n = 1; n < N; ++n) {
printf("%lld\t%lld\n",
(int64_t) results[n].tv_sec * 1000000000 +
(int64_t)results[n].tv_nsec,
(int64_t) results[n].tv_sec * 1000000000 +
(int64_t) results[n].tv_nsec -
((int64_t) results[n-1].tv_sec * 1000000000 +
(int64_t)results[n-1].tv_nsec));
}
return 0;
}
Code 3: rdtsc.h
static uint64_t rdtsc() {
#if defined(__GNUC__)
# if defined(__i386__)
uint64_t x;
__asm__ volatile (".byte 0x0f, 0x31" : "=A" (x));
return x;
# elif defined(__x86_64__)
uint32_t hi, lo;
__asm__ __volatile__ ("rdtsc" : "=a"(lo), "=d"(hi));
return ((uint64_t)lo) | ((uint64_t)hi << 32);
# else
# error Unsupported architecture.
# endif
#elif defined(_MSC_VER)
return __rdtsc();
#else
# error Other compilers not supported...
#endif
}
Code 4: rdtsc_delta.c
#include <stdio.h>
#include <stdint.h>
#include "rdtsc.h"
/* Compiled & executed with:
gcc rdtsc_delta.c -O0 -o rdtsc_delta
./rdtsc_delta > rdtsc_delta_results
Windows:
cl -Od rdtsc_delta.c
rdtsc_delta.exe > windows_rdtsc_delta_results
*/
#define N 100000
int main(int argc, char **args) {
uint64_t results[N];
int n;
for (n = 0; n < N; ++n) {
results[n] = rdtsc();
}
printf("%s\t%s\n", "Absolute time", "Delta");
for (n = 1; n < N; ++n) {
printf("%lld\t%lld\n", results[n], results[n] - results[n-1]);
}
return 0;
}
Code 5: rdtsc_overhead.c
#include <time.h>
#include <stdio.h>
#include <stdint.h>
#include "rdtsc.h"
/* Compiled & executed with:
gcc rdtsc_overhead.c -O0 -lrt -o rdtsc_overhead
./rdtsc_overhead 1000000 > rdtsc_overhead_results
Windows:
cl -Od rdtsc_overhead.c
rdtsc_overhead.exe 1000000 > windows_rdtsc_overhead_results
*/
int main(int argc, char **args) {
uint64_t tstart, tend, dummy;
int n, N;
N = atoi(args[1]);
tstart = rdtsc();
for (n = 0; n < N; ++n) {
dummy = rdtsc();
dummy = rdtsc();
dummy = rdtsc();
dummy = rdtsc();
dummy = rdtsc();
dummy = rdtsc();
dummy = rdtsc();
dummy = rdtsc();
dummy = rdtsc();
dummy = rdtsc();
}
tend = rdtsc();
printf("%G\n", (double)(tend - tstart)/N/10);
return 0;
}
Code 6: qtct_delta.c
#include <stdio.h>
#include <stdint.h>
#include <Windows.h>
/* Compiled & executed with:
cl -Od qtct_delta.c
qtct_delta.exe > windows_qtct_delta_results
*/
#define N 100000
int main(int argc, char **args) {
uint64_t ticks, results[N];
int n;
for (n = 0; n < N; ++n) {
QueryThreadCycleTime(GetCurrentThread(), &ticks);
results[n] = ticks;
}
printf("%s\t%s\n", "Absolute time", "Delta");
for (n = 1; n < N; ++n) {
printf("%lld\t%lld\n", results[n], results[n] - results[n-1]);
}
return 0;
}
Code 7: qtct_overhead.c
#include <stdio.h>
#include <stdint.h>
#include <Windows.h>
/* Compiled & executed with:
cl -Od qtct_overhead.c
qtct_overhead.exe 1000000
*/
int main(int argc, char **args) {
uint64_t tstart, tend, ticks;
int n, N;
N = atoi(args[1]);
QueryThreadCycleTime(GetCurrentThread(), &tstart);
for (n = 0; n < N; ++n) {
QueryThreadCycleTime(GetCurrentThread(), &ticks);
QueryThreadCycleTime(GetCurrentThread(), &ticks);
QueryThreadCycleTime(GetCurrentThread(), &ticks);
QueryThreadCycleTime(GetCurrentThread(), &ticks);
QueryThreadCycleTime(GetCurrentThread(), &ticks);
QueryThreadCycleTime(GetCurrentThread(), &ticks);
QueryThreadCycleTime(GetCurrentThread(), &ticks);
QueryThreadCycleTime(GetCurrentThread(), &ticks);
QueryThreadCycleTime(GetCurrentThread(), &ticks);
QueryThreadCycleTime(GetCurrentThread(), &ticks);
}
QueryThreadCycleTime(GetCurrentThread(), &tend);
printf("%G\n", (double)(tend - tstart)/N/10);
return 0;
}

Well as CLOCK_THREAD_CPUTIME_ID is implemented using rdtsc it will likely suffer from the same problems as it. The manual page for clock_gettime says:
The CLOCK_PROCESS_CPUTIME_ID and CLOCK_THREAD_CPUTIME_ID clocks
are realized on many platforms using timers from the CPUs (TSC on
i386, AR.ITC on Itanium). These registers may differ between CPUs and
as a consequence these clocks may return bogus results if a
process is migrated to another CPU.
Which sounds like it might explain your problems? Maybe you should lock your process to one CPU to get stable results?

When you have a highly skewed distribution that cannot go negative, you're going to see large discrepancies between mean, median, and mode.
The standard deviation is fairly meaningless for such a distribution.
It's usually a good idea to log-transform it.
That will make it "more normal".

Related

C/C++ MPI speedup is not as expected

I am trying to write an MPI application to speedup a math algorithm with a computer cluster. But before this I am doing some kind of benchmarking. But the first results are not as much as expected.
The test application has linear speedup with 4 cores but 5,6 cores are not speeding up the application. I am doing a test with Odroid N2 platform. It has 6 cores. Nproc says there are 6 cores available.
Am I missing some kind of configuration? Or is my code not prepared well enought ( it is based on one of the base example of mpi)?
Is there any response time or syncronization time which shall be considered ?
Here are some measures from my MPI based application. I measured a total calculation time for a function.
1 core 0.838052sec
2 core 0.438483sec
3 core 0.405501sec
4 core 0.416391sec
5 core 0.514472sec
6 core 0.435128sec
12 core (4 core from 3 N2 boards) 0.06867sec
18 core (6 core from 3 N2 boards) 0.152759sec
I did a benchmark with raspberry pi4 with 4 core:
1 core 1.51 sec
2 core 0.75 sec
3 core 0.69 sec
4 core 0.67 sec
And this is my benchmark application:
int MyFun(int *array, int num_elements, int j)
{
int result_overall = 0;
for (int i = 0; i < num_elements; i++)
{
result_overall += array[i] / 1000;
}
return result_overall;
}
int compute_sum(int* sub_sums,int num_of_cpu)
{
int sum = 0;
for(int i = 0; i<num_of_cpu; i++)
{
sum += sub_sums[i];
}
return sum;
}
//measuring performance from main(): num_elements_per_proc is equal to 604800
if (world_rank == 0)
{
startTime = std::chrono::high_resolution_clock::now();
}
// Compute the sum of your subset
int sub_sum = 0;
for(int j=0;j<1000;j++)
{
sub_sum += MyFun(sub_intArray, num_elements_per_proc, world_rank);
}
MPI_Allgather(&sub_sum, 1, MPI_INT, sub_sums, 1, MPI_INT, MPI_COMM_WORLD);
int total_sum = compute_sum(sub_sums, num_of_cpu);
if (world_rank == 0)
{
elapsedTime = std::chrono::high_resolution_clock::now() - startTime;
timer = elapsedTime.count();
}
I build it with -O3 optimization level.
UPDATE:
new measures:
60480 sample, MyFun called 100000 times:
1.47 -> 0.74 -> 0.48 -> 0.36
6048 samples, MyFun called 1000000 times:
1.43 -> 0.7 -> 0.47 -> 0.35
6048 samples, MyFun called 10000000 times:
14.43 -> 7.08 -> 4.72 -> 3.59
UPDATE2:
By the way when I list the CPU info in linux I got this:
Is this normal?
The quad-core A73 core is not present. And it says there are two sockets with 3-3 cores.
And here is the CPU utilization with sar:
Seems like all of the cores are utilized.
I create some plots from speedup:
Seems like calculation on float instead of int helps a bit but the core 5-6 do not help much. And I think memory bandwidth is okay. Is this a normal behavior when utilizing all CPU equally with little.BIG architecture?

OpenMP worst performance with more threads (following openMP tutorials)

I'm starting to work with OpenMP and I follow these tutorials:
OpenMP Tutorials
I'm coding exactly what appears on the video, but instead of a better performance with more threads I get worse. I don't understand why.
Here's my code:
#include <iostream>
#include <time.h>
#include <omp.h>
using namespace std;
static long num_steps = 100000000;
double step;
#define NUM_THREADS 2
int main()
{
clock_t t;
t = clock();
int i, nthreads; double pi, sum[NUM_THREADS];
step = 1.0/(double)num_steps;
omp_set_num_threads(NUM_THREADS);
#pragma omp parallel
{
int i, id, nthrds;
double x;
id = omp_get_thread_num();
nthrds = omp_get_num_threads();
if(id == 0) nthreads = nthrds;
for(i=id, sum[id]=0.0; i < num_steps; i = i + nthrds)
{
x = (i+0.5)*step;
sum[id] += 4.0/(1.0+x*x);
}
}
for(i = 0, pi=0.0; i<nthreads; i++) pi += sum[i] * step;
t = clock() - t;
cout << "time: " << t << " miliseconds" << endl;
}
As you can see, it's exactly the same as in the video, I only added a code to measure an elapsed time.
On the tutorial, the more threads we use the better a performance.
In my case, that doesn't happen. Here are the timing I got:
1 thread: 433590 miliseconds
2 threads: 1705704 miliseconds
3 threads: 2689001 miliseconds
4 threads: 4221881 miliseconds
Why do I get this behavior?
-- EDIT --
gcc version: gcc 5.5.0
result of lscpu:
Architechure: x86_64
CPU op-mode(s): 32-bit, 64-bit
Byte Order: Little Endian
CPU(s): 8
On-line CPU(s) list: 0-7
Thread(s) per core: 2
Core(s) per socket: 4
Socket(s): 1
NUMA node(s): 1
Vendor ID: GenuineIntel
CPU family: 6
Model: 60
Model name: Intel(R) Core(TM) i7-4720HQ CPU # 2.60Ghz
Stepping: 3
CPU Mhz: 2594.436
CPU max MHz: 3600,0000
CPU min Mhz: 800,0000
BogoMIPS: 5188.41
Virtualization: VT-x
L1d cache: 32K
L1i cache: 32K
L2 cache: 256K
L3 cache: 6144K
NUMA node0 CPU(s): 0-7
-- EDIT --
I've tried using omp_get_wtime() instead, like this:
#include <iostream>
#include <time.h>
#include <omp.h>
using namespace std;
static long num_steps = 100000000;
double step;
#define NUM_THREADS 8
int main()
{
int i, nthreads; double pi, sum[NUM_THREADS];
step = 1.0/(double)num_steps;
double start_time = omp_get_wtime();
omp_set_num_threads(NUM_THREADS);
#pragma omp parallel
{
int i, id, nthrds;
double x;
id = omp_get_thread_num();
nthrds = omp_get_num_threads();
if(id == 0) nthreads = nthrds;
for(i=id, sum[id]=0.0; i < num_steps; i = i + nthrds)
{
x = (i+0.5)*step;
sum[id] += 4.0/(1.0+x*x);
}
}
for(i = 0, pi=0.0; i<nthreads; i++) pi += sum[i] * step;
double time = omp_get_wtime() - start_time;
cout << "time: " << time << " seconds" << endl;
}
The behavior is different, although I have some questions.
Now, if I increase the number of threads by 1, for example, 1 thread, 2 threads, 3, 4, ..., the results are basically the same as previous, the performance gets worse, although if I increase to 64 threads, or 128 threads I get indeed better performance, the timing decreases from 0.44 [s] (for 1 thread) to 0.13 [s] ( for 128 threads ).
My question is: Why I don't have the same behaviour as in the tutorial?
2 threads get better performance than 1,
3 threads get better performance than 2, etc.
Why do I only get better performance with much bigger amount of threads?
instead of better performances with more threads I get worse ... I don't understand why.
Well,let's make the testing a bit more systematic and repeatable to see if :
// time: 1535120 milliseconds 1 thread
// time: 200679 milliseconds 1 thread -O2
// time: 191205 milliseconds 1 thread -O3
// time: 184502 milliseconds 2 threads -O3
// time: 189947 milliseconds 3 threads -O3
// time: 202277 milliseconds 4 threads -O3
// time: 182628 milliseconds 5 threads -O3
// time: 192032 milliseconds 6 threads -O3
// time: 185771 milliseconds 7 threads -O3
// time: 187606 milliseconds 16 threads -O3
// time: 187231 milliseconds 32 threads -O3
// time: 186131 milliseconds 64 threads -O3
ref.: a few sample runs on a TiO.RUN platform fast mock-up ... where limited resources apply a certain glass-ceiling to hit...
This did show more the effects of { -O2 |-O3 }-compilation-mode optimisation effects, than the above proposed principal degradation for growing number of threads.
Next comes the "background" noise from non-managed code-execution ecosystem, where O/S will easily skew the simplistic performance benchmarking
If indeed interested in further details, feel free to read about a Law of diminishing returns ( about real world compositions of [SERIAL], resp. [PARALLEL] parts of the process-scheduling ), where Dr. Gene AMDAHL has initiated the principal rules,
why more threads do not get way better performance ( and where a bit more contemporary re-formulation of this law explains, why more threads may even get negative improvement ( get more expensive add-on overheads ), than a right-tuned peak performance.
#include <time.h>
#include <omp.h>
#include <stdio.h>
#include <stdlib.h>
using namespace std;
static long num_steps = 100000000;
double step;
#define NUM_THREADS 7
int main()
{
clock_t t;
t = clock();
int i, nthreads; double pi, sum[NUM_THREADS];
step = 1.0 / ( double )num_steps;
omp_set_num_threads( NUM_THREADS );
// struct timespec start;
// t = clock(); // _________________________________________ BEST START HERE
// clock_gettime( CLOCK_MONOTONIC, &start ); // ____________ USING MONOTONIC CLOCK
#pragma omp parallel
{
int i,
nthrds = omp_get_num_threads(),
id = omp_get_thread_num();;
double x;
if ( id == 0 ) nthreads = nthrds;
for ( i = id, sum[id] = 0.0;
i < num_steps;
i += nthrds
)
{
x = ( i + 0.5 ) * step;
sum[id] += 4.0 / ( 1.0 + x * x );
}
}
// t = clock() - t; // _____________________________________ BEST STOP HERE
// clock_gettime( CLOCK_MONOTONIC, &end ); // ______________ USING MONOTONIC CLOCK
for ( i = 0, pi = 0.0;
i < nthreads;
i++
) pi += sum[i] * step;
t = clock() - t;
// // time: 1535120 milliseconds 1 thread
// // time: 200679 milliseconds 1 thread -O2
// // time: 191205 milliseconds 1 thread -O3
printf( "time: %d milliseconds %d threads\n", // time: 184502 milliseconds 2 threads -O3
t, // time: 189947 milliseconds 3 threads -O3
NUM_THREADS // time: 202277 milliseconds 4 threads -O3
); // time: 182628 milliseconds 5 threads -O3
} // time: 192032 milliseconds 6 threads -O3
// time: 185771 milliseconds 7 threads -O3
The major problem in that version is false sharing. This is explained later in the video you started to watch. You get this when many threads are accessing data that is adjacent in memory (the sum array). The video also explains how to use padding to manually avoid this issue.
That said, the idiomatic solution is to use a reduction and not even bother with the manual work sharing:
double sum = 0;
#pragma omp parallel for reduction(+:sum)
for(int i=0; i < num_steps; i++)
{
double x = (i+0.5)*step;
sum += 4.0/(1.0+x*x);
}
This is also explained in a later video of the series. It is much simpler than what you started with and most likely the most efficient way.
Although the presenter is certainly competent, the style of these OpenMP tutorial videos is very much bottom up. I'm not sure that is a good educational approach. In any case you should probably watch all of the videos to know how to best use OpenMP it in practice.
Why do I only get better performance with much bigger amount of threads?
This is a bit counterintuitive, you very rarely get better performance from using more OpenMP threads than hardware threads - unless this is indirectly fixing another issue. In your case the large amount of threads means that the sum array is spread out over a larger region in memory and false-sharing is less likely.

What is the precision of the bash builtin time command?

I have a script that measures the execution time of a program using the bash builtin command time.
I am trying to understand the precision of this command: as far as I know it returns the precision in ms, however it use the getrusage() function which returns a value in microseconds. But reading this paper, the real precision is only of 10ms because getrusage samples the time relying on ticks (= 100Hz). The paper is really old (it mentions Linux 2.2.14 running on a Pentium 166Mhz with 96Mb of ram).
Is time still using getrusage() and 100 Hz ticks or is more precise on modern systems?
The testing machine is running Linux 2.6.32.
EDIT: This is a slightly modified version (which should compile also on old version of GCC) of muru's code: modify the value of variable 'v' change also the delay between measures in order to spot the minimum granularity. A value around 500,000 should trigger changes of 1ms on a relatively recent cpu (first versions of i5/i7 #~2.5Ghz)
#include <sys/time.h>
#include <sys/resource.h>
#include <stdio.h>
void dosomething(){
long v = 1000000;
while (v > 0)
v--;
}
int main()
{
struct rusage r1, r2;
long t1, t2, min, max;
int i;
printf("t1\tt2\tdiff\n");
for (i = 0; i<5; i++){
getrusage(RUSAGE_SELF, &r1);
dosomething();
getrusage(RUSAGE_SELF, &r2);
t1 = r1.ru_stime.tv_usec + r1.ru_stime.tv_sec*1000000 + r1.ru_utime.tv_usec + r1.ru_utime.tv_sec*1000000;
t2 = r2.ru_stime.tv_usec + r2.ru_stime.tv_sec*1000000 + r2.ru_utime.tv_usec + r2.ru_utime.tv_sec*1000000;
printf("%ld\t%ld\t%ld\n",t1,t2,t2-t1);
if ((t2-t1 < min) | (i == 0))
min = t2-t1;
if ((t2-t1 > max) | (i == 0))
max = t2-t1;
dosomething();
}
printf("Min = %ldus Max = %ldus\n",min,max);
return 0;
}
However the precision is bound to linux version: with Linux 3 and above the precision is in the order of us, while on linux 2.6.32 could be around 1ms, probably depending also on the specific distro. I suppose that this difference is related to the usage of HRT isntead of Tick on recent linux versions.
In any case the maximum precision of time is 1ms on all recent and not-so-recent machines.
The bash builtin time still uses getrusage(2). On an Ubuntu 14.04 system:
$ bash --version
GNU bash, version 4.3.11(1)-release (x86_64-pc-linux-gnu)
Copyright (C) 2013 Free Software Foundation, Inc.
License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>
This is free software; you are free to change and redistribute it.
There is NO WARRANTY, to the extent permitted by law.
$ strace -o log bash -c 'time sleep 1'
real 0m1.018s
user 0m0.000s
sys 0m0.001s
$ tail log
getrusage(RUSAGE_SELF, {ru_utime={0, 0}, ru_stime={0, 3242}, ...}) = 0
getrusage(RUSAGE_CHILDREN, {ru_utime={0, 0}, ru_stime={0, 530}, ...}) = 0
write(2, "\n", 1) = 1
write(2, "real\t0m1.018s\n", 14) = 14
write(2, "user\t0m0.000s\n", 14) = 14
write(2, "sys\t0m0.001s\n", 13) = 13
rt_sigprocmask(SIG_BLOCK, [CHLD], [], 8) = 0
rt_sigprocmask(SIG_SETMASK, [], NULL, 8) = 0
exit_group(0) = ?
+++ exited with 0 +++
As the strace output shows, it calls getrusage.
As for precision, the rusage struct that getrusage uses includes timeval objects, and timeval has microsecond precision. From the manpage of getrusage:
ru_utime
This is the total amount of time spent executing in user mode,
expressed in a timeval structure (seconds plus microseconds).
ru_stime
This is the total amount of time spent executing in kernel
mode, expressed in a timeval structure (seconds plus
microseconds).
I think it does better than 10 ms. Take the following example file:
#include <sys/time.h>
#include <sys/resource.h>
#include <stdio.h>
int main()
{
struct rusage now, then;
getrusage(RUSAGE_SELF, &then);
getrusage(RUSAGE_SELF, &now);
printf("%ld %ld\n",
then.ru_stime.tv_usec + then.ru_stime.tv_sec*1000000 + then.ru_utime.tv_usec + then.ru_utime.tv_sec*1000000
now.ru_stime.tv_usec + now.ru_stime.tv_sec*1000000 + now.ru_utime.tv_usec + now.ru_utime.tv_sec*1000000);
}
Now:
$ make test
cc test.c -o test
$ for ((i=0; i < 5; i++)); do ./test; done
447 448
356 356
348 348
347 347
349 350
The reported differences between successive calls to getrusage are 1 µs and 0 (the minimum). Since it does show a 1 µs gap, the tick must be at most 1 µs.
If it had a 10 ms tick, the differences would be zero, or at least 10000.

CUDA performance test

I'm writing a simple CUDA program for performance test.
This is not related to vector calculation, but just for a simple (parallel) string conversion.
#include <stdio.h>
#include <string.h>
#include <cuda_runtime.h>
#define UCHAR unsigned char
#define UINT32 unsigned long int
#define CTX_SIZE sizeof(aes_context)
#define DOCU_SIZE 4096
#define TOTAL 100000
#define BBLOCK_SIZE 500
UCHAR pH_TXT[DOCU_SIZE * TOTAL];
UCHAR pH_ENC[DOCU_SIZE * TOTAL];
UCHAR* pD_TXT;
UCHAR* pD_ENC;
__global__
void TEST_Encode( UCHAR *a_input, UCHAR *a_output )
{
UCHAR *input;
UCHAR *output;
input = &(a_input[threadIdx.x * DOCU_SIZE]);
output = &(a_output[threadIdx.x * DOCU_SIZE]);
for ( int i = 0 ; i < 30 ; i++ ) {
if ( (input[i] >= 'a') && (input[i] <= 'z') ) {
output[i] = input[i] - 'a' + 'A';
}
else {
output[i] = input[i];
}
}
}
int main(int argc, char** argv)
{
struct cudaDeviceProp xCUDEV;
cudaGetDeviceProperties(&xCUDEV, 0);
// Prepare Source
memset(pH_TXT, 0x00, DOCU_SIZE * TOTAL);
for ( int i = 0 ; i < TOTAL ; i++ ) {
strcpy((char*)pH_TXT + (i * DOCU_SIZE), "hello world, i need an apple.");
}
// Allocate vectors in device memory
cudaMalloc((void**)&pD_TXT, DOCU_SIZE * TOTAL);
cudaMalloc((void**)&pD_ENC, DOCU_SIZE * TOTAL);
// Copy vectors from host memory to device memory
cudaMemcpy(pD_TXT, pH_TXT, DOCU_SIZE * TOTAL, cudaMemcpyHostToDevice);
// Invoke kernel
int threadsPerBlock = BLOCK_SIZE;
int blocksPerGrid = (TOTAL + threadsPerBlock - 1) / threadsPerBlock;
printf("Total Task is %d\n", TOTAL);
printf("block size is %d\n", threadsPerBlock);
printf("repeat cnt is %d\n", blocksPerGrid);
TEST_Encode<<<blocksPerGrid, threadsPerBlock>>>(pD_TXT, pD_ENC);
cudaMemcpy(pH_ENC, pD_ENC, DOCU_SIZE * TOTAL, cudaMemcpyDeviceToHost);
// Free device memory
if (pD_TXT) cudaFree(pD_TXT);
if (pD_ENC) cudaFree(pD_ENC);
cudaDeviceReset();
}
And when i change BLOCK_SIZE value from 2 to 1000, I got a following duration time (from NVIDIA Visual Profiler)
TOTAL BLOCKS BLOCK_SIZE Duration(ms)
100000 50000 2 28.22
100000 10000 10 22.223
100000 2000 50 12.3
100000 1000 100 9.624
100000 500 200 10.755
100000 250 400 29.824
100000 200 500 39.67
100000 100 1000 81.268
My GPU is GeForce GT520 and max threadsPerBlock value is 1024, so I predicted that I would get best performance when BLOCK is 1000, but the above table shows different result.
I can't understand why Duration time is not linear, and how can I fix this problem. (or how can I find optimized Block value (mimimum Duration time)
It seems 2, 10, 50 threads doesn't utilize the capabilities of the gpu since its design is to start much more threads.
Your card has compute capability 2.1.
Maximum number of resident threads per multiprocessor = 1536
Maximum number of threads per block = 1024
Maximum number of resident blocks per multiprocessor = 8
Warp size = 32
There are two issues:
1.
You try to occupy so much register memory per thread that it will definetly is outsourced to slow local memory space if your block sizes increases.
2.
Perform your tests with multiple of 32 since this is the warp size of your card and many memory operations are optimized for thread sizes with multiple of the warp size.
So if you use only around 1024 (1000 in your case) threads per block 33% of your gpu is idle since only 1 block can be assigned per SM.
What happens if you use the following 100% occupancy sizes?
128 = 12 blocks -> since only 8 can be resident per sm the block execution is serialized
192 = 8 resident blocks per sm
256 = 6 resident blocks per sm
512 = 3 resident blocks per sm

Accurately Calculating CPU Utilization in Linux using /proc/stat

There are a number of posts and references on how to get CPU Utilization using statistics in /proc/stat. However, most of them use only four of the 7+ CPU stats (user, nice, system, and idle), ignoring the remaining jiffie CPU counts present in Linux 2.6 (iowait, irq, softirq).
As an example, see Determining CPU utilization.
My question is this: Are the iowait/irq/softirq numbers also counted in one of the first four numbers (user/nice/system/idle)? In other words, does the total jiffie count equal the sum of the first four stats? Or, is the total jiffie count equal to the sum of all 7 stats? If the latter is true, then a CPU utilization formula should take all of the numbers into account, like this:
#include <stdio.h>
#include <stdlib.h>
int main(void)
{
long double a[7],b[7],loadavg;
FILE *fp;
for(;;)
{
fp = fopen("/proc/stat","r");
fscanf(fp,"%*s %Lf %Lf %Lf %Lf",&a[0],&a[1],&a[2],&a[3],&a[4],&a[5],&a[6]);
fclose(fp);
sleep(1);
fp = fopen("/proc/stat","r");
fscanf(fp,"%*s %Lf %Lf %Lf %Lf",&b[0],&b[1],&b[2],&b[3],&b[4],&b[5],&b[6]);
fclose(fp);
loadavg = ((b[0]+b[1]+b[2]+b[4]+b[5]+b[6]) - (a[0]+a[1]+a[2]+a[4]+a[5]+a[6]))
/ ((b[0]+b[1]+b[2]+b[3]+b[4]+b[5]+b[6]) - (a[0]+a[1]+a[2]+a[3]+a[4]+a[5]+a[6]));
printf("The current CPU utilization is : %Lf\n",loadavg);
}
return(0);
}
I think iowait/irq/softirq are not counted in one of the first 4 numbers. You can see the comment of irqtime_account_process_tick in kernel code for more detail:
(for Linux kernel 4.1.1)
2815 * Tick demultiplexing follows the order
2816 * - pending hardirq update <-- this is irq
2817 * - pending softirq update <-- this is softirq
2818 * - user_time
2819 * - idle_time <-- iowait is included in here, discuss below
2820 * - system time
2821 * - check for guest_time
2822 * - else account as system_time
For the idle time handling, see account_idle_time function:
2772 /*
2773 * Account for idle time.
2774 * #cputime: the cpu time spent in idle wait
2775 */
2776 void account_idle_time(cputime_t cputime)
2777 {
2778 u64 *cpustat = kcpustat_this_cpu->cpustat;
2779 struct rq *rq = this_rq();
2780
2781 if (atomic_read(&rq->nr_iowait) > 0)
2782 cpustat[CPUTIME_IOWAIT] += (__force u64) cputime;
2783 else
2784 cpustat[CPUTIME_IDLE] += (__force u64) cputime;
2785 }
If the cpu is idle AND there is some IO pending, it will count the time in CPUTIME_IOWAIT. Otherwise, it is count in CPUTIME_IDLE.
To conclude, I think the jiffies in irq/softirq should be counted as "busy" for cpu because it was actually handling some IRQ or soft IRQ. On the other hand, the jiffies in "iowait" should be counted as "idle" for cpu because it was not doing something but waiting for a pending IO to happen.
from busybox, its top magic is:
static const char fmt[] ALIGN1 = "cp%*s %llu %llu %llu %llu %llu %llu %llu %llu";
int ret;
if (!fgets(line_buf, LINE_BUF_SIZE, fp) || line_buf[0] != 'c' /* not "cpu" */)
return 0;
ret = sscanf(line_buf, fmt,
&p_jif->usr, &p_jif->nic, &p_jif->sys, &p_jif->idle,
&p_jif->iowait, &p_jif->irq, &p_jif->softirq,
&p_jif->steal);
if (ret >= 4) {
p_jif->total = p_jif->usr + p_jif->nic + p_jif->sys + p_jif->idle
+ p_jif->iowait + p_jif->irq + p_jif->softirq + p_jif->steal;
/* procps 2.x does not count iowait as busy time */
p_jif->busy = p_jif->total - p_jif->idle - p_jif->iowait;
}

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