Effectively migrate a linux process with C - c

I need to estimate how much it costs to migrate a linux process on another core of the same computer. To migrate the process I'm using the sched_setaffinity system call, but i've noticed that migration does not always happens instantaneously, which is my requirement.
More in depth, I'm creating a C program which makes lots of simple computation two times each, the first without migration and the second with migration. Computing the difference between the two timestamps should give me a rough estimation of the migration overhead. However, I need to figure out how can I migrate the current process and wait until the migration happens
#define _GNU_SOURCE
#define _POSIX_C_SOURCE 199309L
#include <assert.h>
#include <sched.h>
#include <stdbool.h>
#include <stdio.h>
#include <stdlib.h>
#include <unistd.h>
#include <time.h>
#include <stdint.h>
//Migrates the process
int migrate(pid_t pid) {
const int totCPU = 8;
const int nextCPU = (sched_getcpu() +1) % totCPU;
cpu_set_t target;
CPU_SET(nextCPU, &target);
if(sched_setaffinity(pid, sizeof(target), &target) < 0)
perror("Setaffinity");
return nextCPU;
}
int main(void) {
long i =0;
const long iterations = 4;
uint64_t total_sequential_delays = 0;
uint64_t total_migration_delays = 0;
uint64_t delta_us;
for(int i=0; i < iterations; i++) {
struct timespec start, end;
//Migration benchmark only happens in odd iterations
bool do_migration = i % 2 == 1;
//Start timestamp
clock_gettime(CLOCK_MONOTONIC_RAW, &start);
//Target CPU to migrate
int target;
if(do_migration) {
target = migrate(0);
//if current CPU is not the target CPU
if(target != sched_getcpu()) {
do {
clock_gettime(CLOCK_MONOTONIC_RAW, &end);
}
while(target != sched_getcpu());
}
}
//Simple computation
double k = 5;
for(int j = 1; j <= 9999; j++) {
k *= j / (k-3);
}
//End timestamp
clock_gettime(CLOCK_MONOTONIC_RAW, &end);
//Elapsed time
delta_us = (end.tv_sec - start.tv_sec) * 1000000 + (end.tv_nsec - start.tv_nsec) / 1000;
if(do_migration) total_migration_delays += delta_us;
else total_sequential_delays += delta_us;
}
//Compute the averages
double avg_migration = total_migration_delays / iterations;
double avg_sequential = total_sequential_delays / iterations;
//Print them
printf("\navg_migration=%f, avg_sequential=%f",avg_migration,avg_sequential);
return EXIT_SUCCESS;
}
The problem here is that the do-while loop (rows 46-49) sometimes runs forever.

I need to estimate how much it costs to migrate a linux process on another core of the same computer.
OK, the cost can be estimated as:
the time taken to set the new CPU affinity and do a "yield" or "sleep(0)" to force a task switch/reschedule (including task switch overhead, etc).
the cost of a cache miss for every future "was cached on the old CPU but not cached in the new CPU yet" memory access
the cost of a TLB miss for every future "virtual to physical translation was cached on the old CPU but not cached in the new CPU yet" memory access
NUMA penalties
load balancing problems (e.g. migrating from "lightly loaded" CPU or core to "heavily loaded by other processes" CPU or core may cause severe performance problems, including the cost of the kernel deciding to migrate other processes to different CPUs to fix the load balancing, where the costs/overheads paid by other processes probably should be included in the total cost caused by migrating your process).
Note that:
a) there are multiple levels of caches (trace cache, instruction cache, L1 data cache, L2 data cache, ..) and some caches are shared between some CPUs (e.g. L1 might be shared between logical CPUs within the same core, L2 might be shared by 2 cores, L3 might be shared by 8 cores).
b) TLB miss costs depend on a lot of things (e.g. if the kernel is using Meltdown mitigations without the PCID feature and blows away TLBs information every system call anyway).
c) NUMA penalties are latency costs - every access to RAM (e.g. cache miss) that was allocated on the previous CPU (for the previous NUMA node) will have higher latency than accesses to RAM that are allocated on the new/current CPU (correct NUMA node).
d) All of the cache miss costs, TLB miss costs and NUMA penalties depend on memory access patterns. A benchmark that has no memory accesses will be misleading.
e) The cache miss costs, TLB miss costs and NUMA penalties are highly dependent on the hardware involved - e.g. a benchmark on one "slow CPUs with fast RAM and no NUMA" computer will be completely irrelevant for a different "fast CPUs with slow RAM and many NUMA domains" computer. In the same way it's highly dependent on which CPUs (e.g. migrating from CPU #0 to CPU #1 might cost very little, and migrating from CPU #0 to CPU #15 might be very expensive).
To migrate the process I'm using the sched_setaffinity system call, but i've noticed that migration does not always happens instantaneously, which is my requirement.
Put a "sleep(0);" after the "sched_setaffinity();".

You aren't clearing the target set:
cpu_set_t target;
CPU_ZERO(&target); /* you need to add this line */
CPU_SET(nextCPU, &target);
so you don't know what you are setting your affinity to.
Another problem is that the cost of migration is not paid up front, so simply measuring the time from set_affinity() until you have settled isn't really a proper measurement. You might be better off running a workload contained to a single cpu, then two, three. Also, consider whether your workload should involve walking largish data structs, possibly with updates, since the loss of your cache investment is also a cost of migration.

Related

How to implement a timer for every second with zero nanosecond with liburing?

I noticed the io_uring kernel side uses CLOCK_MONOTONIC at CLOCK_MONOTONIC, so for the first timer, I get the time with both CLOCK_REALTIME and CLOCK_MONOTONIC and adjust the nanosecond like below and use IORING_TIMEOUT_ABS flag for io_uring_prep_timeout. iorn/clock.c at master · hnakamur/iorn
const long sec_in_nsec = 1000000000;
static int queue_timeout(iorn_queue_t *queue) {
iorn_timeout_op_t *op = calloc(1, sizeof(*op));
if (op == NULL) {
return -ENOMEM;
}
struct timespec rts;
int ret = clock_gettime(CLOCK_REALTIME, &rts);
if (ret < 0) {
fprintf(stderr, "clock_gettime CLOCK_REALTIME error: %s\n", strerror(errno));
return -errno;
}
long nsec_diff = sec_in_nsec - rts.tv_nsec;
ret = clock_gettime(CLOCK_MONOTONIC, &op->ts);
if (ret < 0) {
fprintf(stderr, "clock_gettime CLOCK_MONOTONIC error: %s\n", strerror(errno));
return -errno;
}
op->handler = on_timeout;
op->ts.tv_sec++;
op->ts.tv_nsec += nsec_diff;
if (op->ts.tv_nsec > sec_in_nsec) {
op->ts.tv_sec++;
op->ts.tv_nsec -= sec_in_nsec;
}
op->count = 1;
op->flags = IORING_TIMEOUT_ABS;
ret = iorn_prep_timeout(queue, op);
if (ret < 0) {
return ret;
}
return iorn_submit(queue);
}
From the second time, I just increment the second part tv_sec and use IORING_TIMEOUT_ABS flag for io_uring_prep_timeout.
Here is the output from my example program. The millisecond part is zero but it is about 400 microsecond later than just second.
on_timeout time=2020-05-10T14:49:42.000442
on_timeout time=2020-05-10T14:49:43.000371
on_timeout time=2020-05-10T14:49:44.000368
on_timeout time=2020-05-10T14:49:45.000372
on_timeout time=2020-05-10T14:49:46.000372
on_timeout time=2020-05-10T14:49:47.000373
on_timeout time=2020-05-10T14:49:48.000373
Could you tell me a better way than this?
Thanks for your comments! I'd like to update the current time for logging like ngx_time_update(). I modified my example to use just CLOCK_REALTIME, but still about 400 microseconds late. github.com/hnakamur/iorn/commit/… Does it mean clock_gettime takes about 400 nanoseconds on my machine?
Yes, that sounds about right, sort of. But, if you're on an x86 PC under linux, 400 ns for clock_gettime overhead may be a bit high (order of magnitude higher--see below). If you're on an arm CPU (e.g. Raspberry Pi, nvidia Jetson), it might be okay.
I don't know how you're getting 400 microseconds. But, I've had to do a lot of realtime stuff under linux, and 400 us is similar to what I've measured as the overhead to do a context switch and/or wakeup a process/thread after a syscall suspends it.
I never use gettimeofday anymore. I now just use clock_gettime(CLOCK_REALTIME,...) because it's the same except you get nanoseconds instead of microseconds.
Just so you know, although clock_gettime is a syscall, nowadays, on most systems, it uses the VDSO layer. The kernel injects special code into the userspace app, so that it is able to access the time directly without the overhead of a syscall.
If you're interested, you could run under gdb and disassemble the code to see that it just accesses some special memory locations instead of doing a syscall.
I don't think you need to worry about this too much. Just use clock_gettime(CLOCK_MONOTONIC,...) and set flags to 0. The overhead doesn't factor into this, for the purposes of the ioring call as your iorn layer is using it.
When I do this sort of thing, and I want/need to calculate the overhead of clock_gettime itself, I call clock_gettime in a loop (e.g. 1000 times), and try to keep the total time below a [possible] timeslice. I use the minimum diff between times in each iteration. That compensates for any [possible] timeslicing.
The minimum is the overhead of the call itself [on average].
There are additional tricks that you can do to minimize latency in userspace (e.g. raising process priority, clamping CPU affinity and I/O interrupt affinity), but they can involve a few more things, and, if you're not very careful, they can produce worse results.
Before you start taking extraordinary measures, you should have a solid methodology to measure timing/benchmarking to prove that your results can not meet your timing/throughput/latency requirements. Otherwise, you're doing complicated things for no real/measurable/necessary benefit.
Below is some code I just created, simplified, but based on code I already have/use to calibrate the overhead:
#include <stdio.h>
#include <time.h>
#define ITERMAX 10000
typedef long long tsc_t;
// tscget -- get time in nanoseconds
static inline tsc_t
tscget(void)
{
struct timespec ts;
tsc_t tsc;
clock_gettime(CLOCK_MONOTONIC,&ts);
tsc = ts.tv_sec;
tsc *= 1000000000;
tsc += ts.tv_nsec;
return tsc;
}
// tscsec -- convert nanoseconds to fractional seconds
double
tscsec(tsc_t tsc)
{
double sec;
sec = tsc;
sec /= 1e9;
return sec;
}
tsc_t
calibrate(void)
{
tsc_t tscbeg;
tsc_t tscold;
tsc_t tscnow;
tsc_t tscdif;
tsc_t tscmin;
int iter;
tscmin = 1LL << 62;
tscbeg = tscget();
tscold = tscbeg;
for (iter = ITERMAX; iter > 0; --iter) {
tscnow = tscget();
tscdif = tscnow - tscold;
if (tscdif < tscmin)
tscmin = tscdif;
tscold = tscnow;
}
tscdif = tscnow - tscbeg;
printf("MIN:%.9f TOT:%.9f AVG:%.9f\n",
tscsec(tscmin),tscsec(tscdif),tscsec(tscnow - tscbeg) / ITERMAX);
return tscmin;
}
int
main(void)
{
calibrate();
return 0;
}
On my system, a 2.67GHz Core i7, the output is:
MIN:0.000000019 TOT:0.000254999 AVG:0.000000025
So, I'm getting 25 ns overhead [and not 400 ns]. But, again, each system can be different to some extent.
UPDATE:
Note that x86 processors have "speed step". The OS can adjust the CPU frequency up or down semi-automatically. Lower speeds conserve power. Higher speeds are maximum performance.
This is done with a heuristic (e.g. if the OS detects that the process is a heavy CPU user, it will up the speed).
To force maximum speed, linux has this directory:
/sys/devices/system/cpu/cpuN/cpufreq
Where N is the cpu number (e.g. 0-7)
Under this directory, there are a number of files of interest. They should be self explanatory.
In particular, look at scaling_governor. It has either ondemand [kernel will adjust as needed] or performance [kernel will force maximum CPU speed].
To force maximum speed, as root, set this [once] to performance (e.g.):
echo "performance" > /sys/devices/system/cpu/cpu0/cpufreq/scaling_governor
Do this for all cpus.
However, I just did this on my system, and it had little effect. So, the kernel's heuristic may have improved.
As to the 400us, when a process has been waiting on something, when it is "woken up", this is a two step process.
The process is marked "runnable".
At some point, the system/CPU does a reschedule. The process will be run, based upon the scheduling policy and the process priority in effect.
For many syscalls, the reschedule [only] occurs on the next system timer/clock tick/interrupt. So, for some, there can be a delay of up to a full clock tick (i.e.) for HZ value of 1000, this can be up to 1ms (1000 us) later.
On average, this is one half of HZ or 500 us.
For some syscalls, when the process is marked runnable, a reschedule is done immediately. If the process has a higher priority, it will be run immediately.
When I first looked at this [circa 2004], I looked at all code paths in the kernel, and the only syscall that did the immediate reschedule was SysV IPC, for msgsnd/msgrcv. That is, when process A did msgsnd, any process B waiting for the given message would be run.
But, others did not (e.g. futex). They would wait for the timer tick. A lot has changed since then, and now, more syscalls will do the immediate reschedule. For example, I recently measured futex [invoked via pthread_mutex_*], and it seemed to do the quick reschedule.
Also, the kernel scheduler has changed. The newer scheduler can wakeup/run some things on a fraction of a clock tick.
So, for you, the 400 us, is [possibly] the alignment to the next clock tick.
But, it could just be the overhead of doing the syscall. To test that, I modified my test program to open /dev/null [and/or /dev/zero], and added read(fd,buf,1) to the test loop.
I got a MIN: value of 529 us. So, the delay you're getting could just be the amount of time it takes to do the task switch.
This is what I would call "good enough for now".
To get "razor's edge" response, you'd probably have to write a custom kernel driver and have the driver do this. This is what embedded systems would do if (e.g.) they had to toggle a GPIO pin on every interval.
But, if all you're doing is printf, the overhead of printf and the underlying write(1,...) tends to swamp the actual delay.
Also, note that when you do printf, it builds the output buffer and when the buffer in FILE *stdout is full, it flushes via write.
For best performance, it's better to do int len = sprintf(buf,"current time is ..."); write(1,buf,len);
Also, when you do this, if the kernel buffers for TTY I/O get filled [which is quite possible given the high frequency of messages you're doing], the process will be suspended until the I/O has been sent to the TTY device.
To do this well, you'd have to watch how much space is available, and skip some messages if there isn't enough space to [wholy] contain them.
You'd need to do: ioctl(1,TIOCOUTQ,...) to get the available space and skip some messages if it is less than the size of the message you want to output (e.g. the len value above).
For your usage, you're probably more interested in the latest time message, rather than outputting all messages [which would eventually produce a lag]

Using Time stamp counter to get the time stamp

I have used the below code to get the clock cycle of the processor
unsigned long long rdtsc(void)
{
unsigned hi, lo;
__asm__ __volatile__ ("rdtsc" : "=a"(lo), "=d"(hi));
return ( (unsigned long long)lo)|( ((unsigned long long)hi)<<32 );
}
I get some value say 43, but what is the unit here? Is it in microseconds or nanoseconds.
I used below code to get the frequency of my board.
cat /sys/devices/system/cpu/cpu0/cpufreq/cpuinfo_cur_freq
1700000
I also used below code to find my processor speed
dmidecode -t processor | grep "Speed"
Max Speed: 3700 MHz
Current Speed: 3700 MHz
Now how do I use above frequency and convert it to microseconds or milliseconds?
A simple answer to the stated question, "how do I convert the TSC frequency to microseconds or milliseconds?" is: You do not. What the TSC (Time Stamp Counter) clock frequency actually is, varies depending on the hardware, and may vary during runtime on some. To measure real time, you use clock_gettime(CLOCK_REALTIME) or clock_gettime(CLOCK_MONOTONIC) in Linux.
As Peter Cordes mentioned in a comment (Aug 2018), on most current x86-64 architectures the Time Stamp Counter (accessed by the RDTSC instruction and __rdtsc() function declared in <x86intrin.h>) counts reference clock cycles, not CPU clock cycles. His answer to a similar question in C++ is valid for C also in Linux on x86-64, because the compiler provides the underlying built-in when compiling C or C++, and rest of the answer deals with the hardware details. I recommend reading that one, too.
The rest of this answer assumes the underlying issue is microbenchmarking code, to find out how two implementations of some function compare to each other.
On x86 (Intel 32-bit) and x86-64 (AMD64, Intel and AMD 64-bit) architectures, you can use __rdtsc() from <x86intrin.h> to find out the number of TSC clock cycles elapsed. This can be used to measure and compare the number of cycles used by different implementations of some function, typically a large number of times.
Do note that there are hardware differences as to how the TSC clock is related to CPU clock. The abovementioned more recent answer goes into some detail on that. For practical purposes in Linux, it is sufficient in Linux to use cpufreq-set to disable frequency scaling (to ensure the relationship between the CPU and TSC frequencies does not change during microbenchmarking), and optionally taskset to restrict the microbenchmark to specific CPU core(s). That ensures that the results gathered in that microbenchmark yield results that can be compared to each other.
(As Peter Cordes commented, we also want to add _mm_lfence() from <emmintrin.h> (included by <immintrin.h>). This ensures that the CPU does not internally reorder the RDTSC operation compared to the function to be benchmarked. You can use -DNO_LFENCE at compile time to omit those, if you want.)
Let's say you have functions void foo(void); and void bar(void); that you wish to compare:
#include <stdlib.h>
#include <x86intrin.h>
#include <stdio.h>
#ifdef NO_LFENCE
#define lfence()
#else
#include <emmintrin.h>
#define lfence() _mm_lfence()
#endif
static int cmp_ull(const void *aptr, const void *bptr)
{
const unsigned long long a = *(const unsigned long long *)aptr;
const unsigned long long b = *(const unsigned long long *)bptr;
return (a < b) ? -1 :
(a > b) ? +1 : 0;
}
unsigned long long *measure_cycles(size_t count, void (*func)())
{
unsigned long long *elapsed, started, finished;
size_t i;
elapsed = malloc((count + 2) * sizeof elapsed[0]);
if (!elapsed)
return NULL;
/* Call func() count times, measuring the TSC cycles for each call. */
for (i = 0; i < count; i++) {
/* First, let's ensure our CPU executes everything thus far. */
lfence();
/* Start timing. */
started = __rdtsc();
/* Ensure timing starts before we call the function. */
lfence();
/* Call the function. */
func();
/* Ensure everything has been executed thus far. */
lfence();
/* Stop timing. */
finished = __rdtsc();
/* Ensure we have the counter value before proceeding. */
lfence();
elapsed[i] = finished - started;
}
/* The very first call is likely the cold-cache case,
so in case that measurement might contain useful
information, we put it at the end of the array.
We also terminate the array with a zero. */
elapsed[count] = elapsed[0];
elapsed[count + 1] = 0;
/* Sort the cycle counts. */
qsort(elapsed, count, sizeof elapsed[0], cmp_ull);
/* This function returns all cycle counts, in sorted order,
although the median, elapsed[count/2], is the one
I personally use. */
return elapsed;
}
void benchmark(const size_t count)
{
unsigned long long *foo_cycles, *bar_cycles;
if (count < 1)
return;
printf("Measuring run time in Time Stamp Counter cycles:\n");
fflush(stdout);
foo_cycles = measure_cycles(count, foo);
bar_cycles = measure_cycles(count, bar);
printf("foo(): %llu cycles (median of %zu calls)\n", foo_cycles[count/2], count);
printf("bar(): %llu cycles (median of %zu calls)\n", bar_cycles[count/2], count);
free(bar_cycles);
free(foo_cycles);
}
Note that the above results are very specific to the compiler and compiler options used, and of course on the hardware it is run on. The median number of cycles can be interpreted as "the typical number of TSC cycles taken", because the measurement is not completely reliable (may be affected by events outside the process; for example, by context switches, or by migration to another core on some CPUs). For the same reason, I don't trust the minimum, maximum, or average values.
However, the two implementations' (foo() and bar()) cycle counts above can be compared to find out how their performance compares to each other, in a microbenchmark. Just remember that microbenchmark results may not extend to real work tasks, because of how complex tasks' resource use interactions are. One function might be superior in all microbenchmarks, but poorer than others in real world, because it is only efficient when it has lots of CPU cache to use, for example.
In Linux in general, you can use the CLOCK_REALTIME clock to measure real time (wall clock time) used, in the very same manner as above. CLOCK_MONOTONIC is even better, because it is not affected by direct changes to the realtime clock the administrator might make (say, if they noticed the system clock is ahead or behind); only drift adjustments due to NTP etc. are applied. Daylight savings time or changes thereof does not affect the measurements, using either clock. Again, the median of a number of measurements is the result I seek, because events outside the measured code itself can affect the result.
For example:
#define _POSIX_C_SOURCE 200809L
#include <stdlib.h>
#include <stdio.h>
#include <time.h>
#ifdef NO_LFENCE
#define lfence()
#else
#include <emmintrin.h>
#define lfence() _mm_lfence()
#endif
static int cmp_double(const void *aptr, const void *bptr)
{
const double a = *(const double *)aptr;
const double b = *(const double *)bptr;
return (a < b) ? -1 :
(a > b) ? +1 : 0;
}
double median_seconds(const size_t count, void (*func)())
{
struct timespec started, stopped;
double *seconds, median;
size_t i;
seconds = malloc(count * sizeof seconds[0]);
if (!seconds)
return -1.0;
for (i = 0; i < count; i++) {
lfence();
clock_gettime(CLOCK_MONOTONIC, &started);
lfence();
func();
lfence();
clock_gettime(CLOCK_MONOTONIC, &stopped);
lfence();
seconds[i] = (double)(stopped.tv_sec - started.tv_sec)
+ (double)(stopped.tv_nsec - started.tv_nsec) / 1000000000.0;
}
qsort(seconds, count, sizeof seconds[0], cmp_double);
median = seconds[count / 2];
free(seconds);
return median;
}
static double realtime_precision(void)
{
struct timespec t;
if (clock_getres(CLOCK_REALTIME, &t) == 0)
return (double)t.tv_sec
+ (double)t.tv_nsec / 1000000000.0;
return 0.0;
}
void benchmark(const size_t count)
{
double median_foo, median_bar;
if (count < 1)
return;
printf("Median wall clock times over %zu calls:\n", count);
fflush(stdout);
median_foo = median_seconds(count, foo);
median_bar = median_seconds(count, bar);
printf("foo(): %.3f ns\n", median_foo * 1000000000.0);
printf("bar(): %.3f ns\n", median_bar * 1000000000.0);
printf("(Measurement unit is approximately %.3f ns)\n", 1000000000.0 * realtime_precision());
fflush(stdout);
}
In general, I personally prefer to compile the benchmarked function in a separate unit (to a separate object file), and also benchmark a do-nothing function to estimate the function call overhead (although it tends to give an overestimate for the overhead; i.e. yield too large an overhead estimate, because some of the function call overhead is latencies and not actual time taken, and some operations are possible during those latencies in the actual functions).
It is important to remember that the above measurements should only be used as indications, because in a real world application, things like cache locality (especially on current machines, with multi-level caching, and lots of memory) hugely affect the time used by different implementations.
For example, you might compare the speeds of a quicksort and a radix sort. Depending on the size of the keys, the radix sort requires rather large extra arrays (and uses a lot of cache). If the real application the sort routine is used in does not simultaneously use a lot of other memory (and thus the sorted data is basically what is cached), then a radix sort will be faster if there is enough data (and the implementation is sane). However, if the application is multithreaded, and the other threads shuffle (copy or transfer) a lot of memory around, then the radix sort using a lot of cache will evict other data also cached; even though the radix sort function itself does not show any serious slowdown, it may slow down the other threads and therefore the overall program, because the other threads have to wait for their data to be re-cached.
This means that the only "benchmarks" you should trust, are wall clock measurements used on the actual hardware, running actual work tasks with actual work data. Everything else is subject to many conditions, and are more or less suspect: indications, yes, but not very reliable.

OpenMP for beginners

I just got started with openMP; I wrote a little C code in order to check if what I have studied is correct. However I found some troubles; here is the main.c code
#include "stdio.h"
#include "stdlib.h"
#include "omp.h"
#include "time.h"
int main(){
float msec_kernel;
const int N = 1000000;
int i, a[N];
clock_t start = clock(), diff;
#pragma omp parallel for private(i)
for (i = 1; i <= N; i++){
a[i] = 2 * i;
}
diff = clock() - start;
msec_kernel = diff * 1000 / CLOCKS_PER_SEC;
printf("Kernel Time: %e s\n",msec_kernel*1e-03);
printf("a[N] = %d\n",a[N]);
return 0;
}
My goal is to see how long it takes to the PC to do such operation using 1 and 2 CPUs; in order to to compile the program I type the following line in the terminal:
gcc -fopenmp main.c -o main
And then I select the number of CPUs like so:
export OMP_NUM_THREADS=N
where N is either 1 or 2; however I don't get the right execution time; my results in fact are:
Kernel Time: 5.000000e-03 s
a[N] = 2000000
and
Kernel Time: 6.000000e-03 s
a[N] = 2000000
Both corresponding to N=1 and N=2. as you can see when I use 2 CPUs it takes slightly more time than using just one! What am I doing wrong? How can I fix this problem?
First of all, using multiple cores doesn't implicitly mean, that you're going to get better performance.
OpenMP has to manage the data distribution among you're cores which is going to take time as well. Especially for very basic operations such as only a single multiplication you are doing, performance of a sequential (single core) program will be better.
Second, by going through every element of you're array only once and not doing anything else, you make no use of cache memory and most certainly not of shared cache between cpu's.
So you should start reading some things about general algorithm performance. To make use of multiple cores using shared cache is in my opinion the essence.
Todays computers have come to a stage where the CPU is so much faster than a memory allocation, read or write. This means when using multiple cores, you'll only have a benefit if you use things like shared cache, because the data distribution,initialization of the threads and managing them will use time as well. To really see a performance speedup (See the link, essential term in parallel computing) you should program an algorithm which has a heavy accent on computation not on memory; this has to do with locality (another important term).
So if you wanna experience a big performance boost by using multiple cores test it on a matrix-matrix-multiplication on big matrices such as 10'000*10'000. And plot some graphs with inputsize(matrix-size) to time and matrix-size to gflops and compare the multicore with the sequential version.
Also make yourself comfortable with the complexity analysis (Big O notation).
Matrix-matrix-multiplication has a locality of O(n).
Hope this helps :-)
I suggest setting the numbers of cores/threads within the code itself either directly at the #pragma line #pragma omp parallel for num_threads(2) or using the omp_set_num_threads function omp_set_num_threads(2);
Further, when doing time/performance analysis it is really important to always run the program multiple times and then take the mean of all the runtimes or something like that. Running the respective programs only once will not give you a meaningful reading of used time. Always call multiple times in a row. Not to forget to also alternate the quality of data.
I suggest writing a test.c file, which takes your actual program function within a loop and then calculates the time per execution of the function:
int executiontimes = 20;
clock_t initial_time = clock();
for(int i = 0; i < executiontimes; i++){
function_multiplication(values);
}
clock_t final_time = clock();
clock_t passed_time = final_time - initial_time;
clock_t time_per_exec = passed_time / executiontimes;
Improve this test algorithm, add some rand() for your values etc. seed them with srand() etc. If you have more questions on the subject or to my answer leave a comment and I'll try to explain further by adding more explanations.
The function clock() returns elapsed CPU time, which includes ticks from all cores. Since there is some overhead to using multiple threads, when you sum the execution time of all threads the total cpu time will always be longer than the serial time.
If you want the real time (wall clock time), try to use the OMP Runtime Library function omp_get_wtime() defined in omp.h. It is cross platform portable and should be the preferred way to do wall timing.
You can also use the POSIX functions defined in time.h:
struct timespec start, stop;
clock_gettime(CLOCK_REALTIME, &start);
// action
clock_gettime(CLOCK_REALTIME, &stop);
double elapsed_time = (stop.tv_sec - start.tv_sec) +
1e-9 * (stop.tv_nsec - start.tv_nsec);

Do virtual cores contribute to performance when parallelizing a matrix multiplication?

I have an O(n^3) matrix multiplication function in C.
void matrixMultiplication(int N, double **A, double **B, double **C, int threadCount) {
int i = 0, j = 0, k = 0, tid;
pragma omp parallel num_threads(4) shared(N, A, B, C, threadCount) private(i, j, k, tid) {
tid = omp_get_thread_num();
pragma omp for
for (i = 1; i < N; i++)
{
printf("Thread %d starting row %d\n", tid, i);
for (j = 0; j < N; j++)
{
for (k = 0; k < N; k++)
{
C[i][j] = C[i][j] + A[i][k] * B[k][j];
}
}
}
}
return;
}
I am using OpenMP to parallelize this function by splitting up the multiplications. I am performing this computation on square matrices of size N = 3000 with a 1.8 GHz Intel Core i5 processor.
This processor has two physical cores and two virtual cores. I noticed the following performances for my computation
1 thread: 526.06s
2 threads: 264.531
3 threads: 285.195
4 threads: 279.914
I had expected my gains to continue until the setting the number of threads equal to four. However, this obviously did not occur.
Why did this happen? Is it because the performance of a core is equal to the sum of its physical and virtual cores?
Using more than one hardware thread per core can help or hurt, depending on circumstances.
It can help if one hardware thread stalls because of a cache miss, and the other hardware thread can keep going and keep the ALU busy.
It can hurt if each hardware thread forces evictions of data needed by the other thread. That is the threads destructively interfere with each other.
One way to address the problem is to write the kernel in a way such that each thread needs only half the cache. For example, blocked matrix multiplication can be used to minimize the cache footprint of a matrix multiplication.
Another way is to write the algorithm in a way such that both threads operate on the same data at the same time, so they help each other bring data into cache (constructive interference). This approach is admittedly hard to do with OpenMP unless the implementation has good support for nested parallelism.
I guess that the bottleneck is the memory (or L3 CPU cache) bandwidth. Arithmetic is quite cheap these days.
If you can afford it, try to benchmark the same code with the same data on some more powerful processor (e.g. some socket 2013 i7)
Remember that on today's processors, a cache miss lasts as long as several hundred instructions (or cycles): RAM is very slow w.r.t. cache or CPU.
BTW, if you have a GPGPU you could play with OpenCL.
Also, it is probable that linear software packages like LAPACK (or some other numerical libraries) are more efficient than your naive matrix multiplication.
You could also consider using __builtin_prefetch (see this)
BTW, numerical computation is hard. I am not expert at all, but I met people who worked dozens of years in it (often after a PhD in the field).

Why doesn't this code scale linearly?

I wrote this SOR solver code. Don't bother too much what this algorithm does, it is not the concern here. But just for the sake of completeness: it may solve a linear system of equations, depending on how well conditioned the system is.
I run it with an ill conditioned 2097152 rows sparce matrix (that never converges), with at most 7 non-zero columns per row.
Translating: the outer do-while loop will perform 10000 iterations (the value I pass as max_iters), the middle for will perform 2097152 iterations, split in chunks of work_line, divided among the OpenMP threads. The innermost for loop will have 7 iterations, except in very few cases (less than 1%) where it can be less.
There is data dependency among the threads in the values of sol array. Each iteration of the middle for updates one element but reads up to 6 other elements of the array. Since SOR is not an exact algorithm, when reading, it can have any of the previous or the current value on that position (if you are familiar with solvers, this is a Gauss-Siedel that tolerates Jacobi behavior on some places for the sake of parallelism).
typedef struct{
size_t size;
unsigned int *col_buffer;
unsigned int *row_jumper;
real *elements;
} Mat;
int work_line;
// Assumes there are no null elements on main diagonal
unsigned int solve(const Mat* matrix, const real *rhs, real *sol, real sor_omega, unsigned int max_iters, real tolerance)
{
real *coefs = matrix->elements;
unsigned int *cols = matrix->col_buffer;
unsigned int *rows = matrix->row_jumper;
int size = matrix->size;
real compl_omega = 1.0 - sor_omega;
unsigned int count = 0;
bool done;
do {
done = true;
#pragma omp parallel shared(done)
{
bool tdone = true;
#pragma omp for nowait schedule(dynamic, work_line)
for(int i = 0; i < size; ++i) {
real new_val = rhs[i];
real diagonal;
real residual;
unsigned int end = rows[i+1];
for(int j = rows[i]; j < end; ++j) {
unsigned int col = cols[j];
if(col != i) {
real tmp;
#pragma omp atomic read
tmp = sol[col];
new_val -= coefs[j] * tmp;
} else {
diagonal = coefs[j];
}
}
residual = fabs(new_val - diagonal * sol[i]);
if(residual > tolerance) {
tdone = false;
}
new_val = sor_omega * new_val / diagonal + compl_omega * sol[i];
#pragma omp atomic write
sol[i] = new_val;
}
#pragma omp atomic update
done &= tdone;
}
} while(++count < max_iters && !done);
return count;
}
As you can see, there is no lock inside the parallel region, so, for what they always teach us, it is the kind of 100% parallel problem. That is not what I see in practice.
All my tests were run on a Intel(R) Xeon(R) CPU E5-2670 v2 # 2.50GHz, 2 processors, 10 cores each, hyper-thread enabled, summing up to 40 logical cores.
On my first set runs, work_line was fixed on 2048, and the number of threads varied from 1 to 40 (40 runs in total). This is the graph with the execution time of each run (seconds x number of threads):
The surprise was the logarithmic curve, so I thought that since the work line was so large, the shared caches were not very well used, so I dug up this virtual file /sys/devices/system/cpu/cpu0/cache/index0/coherency_line_size that told me this processor's L1 cache synchronizes updates in groups of 64 bytes (8 doubles in the array sol). So I set the work_line to 8:
Then I thought 8 was too low to avoid NUMA stalls and set work_line to 16:
While running the above, I thought "Who am I to predict what work_line is good? Lets just see...", and scheduled to run every work_line from 8 to 2048, steps of 8 (i.e. every multiple of the cache line, from 1 to 256). The results for 20 and 40 threads (seconds x size of the split of the middle for loop, divided among the threads):
I believe the cases with low work_line suffers badly from cache synchronization, while bigger work_line offers no benefit beyond a certain number of threads (I assume because the memory pathway is the bottleneck). It is very sad that a problem that seems 100% parallel presents such bad behavior on a real machine. So, before I am convinced multi-core systems are a very well sold lie, I am asking you here first:
How can I make this code scale linearly to the number of cores? What am I missing? Is there something in the problem that makes it not as good as it seems at first?
Update
Following suggestions, I tested both with static and dynamic scheduling, but removing the atomics read/write on the array sol. For reference, the blue and orange lines are the same from the previous graph (just up to work_line = 248;). The yellow and green lines are the new ones. For what I could see: static makes a significant difference for low work_line, but after 96 the benefits of dynamic outweighs its overhead, making it faster. The atomic operations makes no difference at all.
The sparse matrix vector multiplication is memory bound (see here) and it could be shown with a simple roofline model. Memory bound problems benefit from higher memory bandwidth of multisocket NUMA systems but only if the data initialisation is done in such a way that the data is distributed among the two NUMA domains. I have some reasons to believe that you are loading the matrix in serial and therefore all its memory is allocated on a single NUMA node. In that case you won't benefit from the double memory bandwidth available on a dual-socket system and it really doesn't matter if you use schedule(dynamic) or schedule(static). What you could do is enable memory interleaving NUMA policy in order to have the memory allocation spread among both NUMA nodes. Thus each thread would end up with 50% local memory access and 50% remote memory access instead of having all threads on the second CPU being hit by 100% remote memory access. The easiest way to enable the policy is by using numactl:
$ OMP_NUM_THREADS=... OMP_PROC_BIND=1 numactl --interleave=all ./program ...
OMP_PROC_BIND=1 enables thread pinning and should improve the performance a bit.
I would also like to point out that this:
done = true;
#pragma omp parallel shared(done)
{
bool tdone = true;
// ...
#pragma omp atomic update
done &= tdone;
}
is a probably a not very efficient re-implementation of:
done = true;
#pragma omp parallel reduction(&:done)
{
// ...
if(residual > tolerance) {
done = false;
}
// ...
}
It won't have a notable performance difference between the two implementations because of the amount of work done in the inner loop, but still it is not a good idea to reimplement existing OpenMP primitives for the sake of portability and readability.
Try running the IPCM (Intel Performance Counter Monitor). You can watch memory bandwidth, and see if it maxes out with more cores. My gut feeling is that you are memory bandwidth limited.
As a quick back of the envelope calculation, I find that uncached read bandwidth is about 10 GB/s on a Xeon. If your clock is 2.5 GHz, that's one 32 bit word per clock cycle. Your inner loop is basically just a multiple-add operation whose cycles you can count on one hand, plus a few cycles for the loop overhead. It doesn't surprise me that after 10 threads, you don't get any performance gain.
Your inner loop has an omp atomic read, and your middle loop has an omp atomic write to a location that could be the same one read by one of the reads. OpenMP is obligated to ensure that atomic writes and reads of the same location are serialized, so in fact it probably does need to introduce a lock, even though there isn't any explicit one.
It might even need to lock the whole sol array unless it can somehow figure out which reads might conflict with which writes, and really, OpenMP processors aren't necessarily all that smart.
No code scales absolutely linearly, but rest assured that there are many codes that do scale much closer to linearly than yours does.
I suspect you are having caching issues. When one thread updates a value in the sol array, it invalids the caches on other CPUs that are storing that same cache line. This forces the caches to be updated, which then leads to the CPUs stalling.
Even if you don't have an explicit mutex lock in your code, you have one shared resource between your processes: the memory and its bus. You don't see this in your code because it is the hardware that takes care of handling all the different requests from the CPUs, but nevertheless, it is a shared resource.
So, whenever one of your processes writes to memory, that memory location will have to be reloaded from main memory by all other processes that use it, and they all have to use the same memory bus to do so. The memory bus saturates, and you have no more performance gain from additional CPU cores that only serve to worsen the situation.

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