GNU make: should the number of jobs equal the numb

Question

There seems to be some controversy on whether the number of jobs in GNU make is supposed to be equal to the number of cores, or if you can optimize the build time by adding one extra job that can be queued up while the others "work".

Is it better to use -j4 or -j5 on a quad core system?

Have you seen (or done) any benchmarking that supports one or the other?

Answer 1

I just got an Athlon II X2 Regor proc with a Foxconn M/B and 4GB of G-Skill memory.

I put my 'cat /proc/cpuinfo' and 'free' at the end of this so others can see my specs. It's a dual core Athlon II x2 with 4GB of RAM.

uname -a on default slackware 14.0 kernel is 3.2.45.

I downloaded the next step kernel source (linux-3.2.46) to /archive4;

extracted it (tar -xjvf linux-3.2.46.tar.bz2);

cd'd into the directory (cd linux-3.2.46);

and copied the default kernel's config over (cp /usr/src/linux/.config .);

used make oldconfig to prepare the 3.2.46 kernel config;

then ran make with various incantations of -jX.

I tested the timings of each run by issuing make after the time command, e.g., 'time make -j2'. Between each run I 'rm -rf' the linux-3.2.46 tree and reextracted it, copied the default /usr/src/linux/.config into the directory, ran make oldconfig and then did my 'make -jX' test again.

plain "make":

real    51m47.510s
user    47m52.228s
sys     3m44.985s
bob@Moses:/archive4/linux-3.2.46$

as above but with make -j2

real    27m3.194s
user    48m5.135s
sys     3m39.431s
bob@Moses:/archive4/linux-3.2.46$

as above but with make -j3

real    27m30.203s
user    48m43.821s
sys     3m42.309s
bob@Moses:/archive4/linux-3.2.46$

as above but with make -j4

real    27m32.023s
user    49m18.328s
sys     3m43.765s
bob@Moses:/archive4/linux-3.2.46$

as above but with make -j8

real    28m28.112s
user    50m34.445s
sys     3m49.877s
bob@Moses:/archive4/linux-3.2.46$

'cat /proc/cpuinfo' yields:

bob@Moses:/archive4$ cat /proc/cpuinfo
processor       : 0
vendor_id       : AuthenticAMD
cpu family      : 16
model           : 6
model name      : AMD Athlon(tm) II X2 270 Processor
stepping        : 3
microcode       : 0x10000c8
cpu MHz         : 3399.957
cache size      : 1024 KB
physical id     : 0
siblings        : 2
core id         : 0
cpu cores       : 2
apicid          : 0
initial apicid  : 0
fdiv_bug        : no
hlt_bug         : no
f00f_bug        : no
coma_bug        : no
fpu             : yes
fpu_exception   : yes
cpuid level     : 5
wp              : yes
flags           : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmo
v pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rd
tscp lm 3dnowext 3dnow constant_tsc nonstop_tsc extd_apicid pni monitor cx16 p
opcnt lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowpre
fetch osvw ibs skinit wdt npt lbrv svm_lock nrip_save
bogomips        : 6799.91
clflush size    : 64
cache_alignment : 64
address sizes   : 48 bits physical, 48 bits virtual
power management: ts ttp tm stc 100mhzsteps hwpstate

processor       : 1
vendor_id       : AuthenticAMD
cpu family      : 16
model           : 6
model name      : AMD Athlon(tm) II X2 270 Processor
stepping        : 3
microcode       : 0x10000c8
cpu MHz         : 3399.957
cache size      : 1024 KB
physical id     : 0
siblings        : 2
core id         : 1
cpu cores       : 2
apicid          : 1
initial apicid  : 1
fdiv_bug        : no
hlt_bug         : no
f00f_bug        : no
coma_bug        : no
fpu             : yes
fpu_exception   : yes
cpuid level     : 5
wp              : yes
flags           : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmo
v pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rd
tscp lm 3dnowext 3dnow constant_tsc nonstop_tsc extd_apicid pni monitor cx16 p
opcnt lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowpre
fetch osvw ibs skinit wdt npt lbrv svm_lock nrip_save
bogomips        : 6799.94
clflush size    : 64
cache_alignment : 64
address sizes   : 48 bits physical, 48 bits virtual
power management: ts ttp tm stc 100mhzsteps hwpstate

'free' yields:

bob@Moses:/archive4$ free
             total       used       free     shared    buffers     cached
Mem:       3991304    3834564     156740          0     519220    2515308

Answer 2

I've run my home project on my 4-core with hyperthreading laptop and recorded the results. This is a fairly compiler-heavy project but it includes a unit test of 17.7 seconds at the end. The compiles are not very IO intensive; there is very much memory available and if not the rest is on a fast SSD.

1 job        real   2m27.929s    user   2m11.352s    sys    0m11.964s    
2 jobs       real   1m22.901s    user   2m13.800s    sys    0m9.532s
3 jobs       real   1m6.434s     user   2m29.024s    sys    0m10.532s
4 jobs       real   0m59.847s    user   2m50.336s    sys    0m12.656s
5 jobs       real   0m58.657s    user   3m24.384s    sys    0m14.112s
6 jobs       real   0m57.100s    user   3m51.776s    sys    0m16.128s
7 jobs       real   0m56.304s    user   4m15.500s    sys    0m16.992s
8 jobs       real   0m53.513s    user   4m38.456s    sys    0m17.724s
9 jobs       real   0m53.371s    user   4m37.344s    sys    0m17.676s
10 jobs      real   0m53.350s    user   4m37.384s    sys    0m17.752s
11 jobs      real   0m53.834s    user   4m43.644s    sys    0m18.568s
12 jobs      real   0m52.187s    user   4m32.400s    sys    0m17.476s
13 jobs      real   0m53.834s    user   4m40.900s    sys    0m17.660s
14 jobs      real   0m53.901s    user   4m37.076s    sys    0m17.408s
15 jobs      real   0m55.975s    user   4m43.588s    sys    0m18.504s
16 jobs      real   0m53.764s    user   4m40.856s    sys    0m18.244s
inf jobs     real   0m51.812s    user   4m21.200s    sys    0m16.812s

Basic results:

• Scaling to the core count increases the performance nearly linearly. The real time went down from 2.5 minutes to 1.0 minute (2.5x as fast), but the time taken during compile went up from 2.11 to 2.50 minutes. The system noticed barely any additional load in this bit.
• Scaling from the core count to the thread count increased the user load immensely, from 2.50 minutes to 4.38 minutes. This near doubling is most likely because the other compiler instances wanted to use the same CPU resources at the same time. The system is getting a bit more loaded with requests and task switching, causing it to go to 17.7 seconds of time used. The advantage is about 6.5 seconds on a compile time of 53.5 seconds, making for a 12% speedup.
• Scaling from thread count to double thread count gave no significant speedup. The times at 12 and 15 are most likely statistical anomalies that you can disregard. The total time taken increases ever so slightly, as does the system time. Both are most likely due to increased task switching. There is no benefit to this.

My guess right now: If you do something else on your computer, use the core count. If you do not, use the thread count. Exceeding it shows no benefit. At some point they will become memory limited and collapse due to that, making the compiling much slower. The "inf" line was added at a much later date, giving me the suspicion that there was some thermal throttling for the 8+ jobs. This does show that for this project size there's no memory or throughput limit in effect. It's a small project though, given 8GB of memory to compile in.

Answer 3

Just as a ref:

From Spawning Multiple Build Jobs section in LKD:

where n is the number of jobs to spawn. Usual practice is to spawn one or two jobs per processor. For example, on a dual processor machine, one might do

$ make j4

Answer 4

Ultimately, you'll have to do some benchmarks to determine the best number to use for your build, but remember that the CPU isn't the only resource that matters!

If you've got a build that relies heavily on the disk, for example, then spawning lots of jobs on a multicore system might actually be slower, as the disk will have to do extra work moving the disk head back and forth to serve all the different jobs (depending on lots of factors, like how well the OS handles the disk-cache, native command queuing support by the disk, etc.).

And then you've got "real" cores versus hyper-threading. You may or may not benefit from spawning jobs for each hyper-thread. Again, you'll have to benchmark to find out.

I can't say I've specifically tried #cores + 1, but on our systems (Intel i7 940, 4 hyperthreaded cores, lots of RAM, and VelociRaptor drives) and our build (large-scale C++ build that's alternately CPU and I/O bound) there is very little difference between -j4 and -j8. (It's maybe 15% better... but nowhere near twice as good.)

If I'm going away for lunch, I'll use -j8, but if I want to use my system for anything else while it's building, I'll use a lower number. :)

Answer 5

I would say the best thing to do is benchmark it yourself on your particular environment and workload. Seems like there are too many variables (size/number of source files, available memory, disk caching, whether your source directory & system headers are located on different disks, etc.) for a one-size-fits-all answer.

My personal experience (on a 2-core MacBook Pro) is that -j2 is significantly faster than -j1, but beyond that (-j3, -j4 etc.) there's no measurable speedup. So for my environment "jobs == number of cores" seems to be a good answer. (YMMV)

Answer 6

I, personally, use make -j n where n is "number of cores" + 1.

I can't, however, give a scientific explanation: I've seen a lot of people using the same settings and they gave me pretty good results so far.

Anyway, you have to be careful because some make-chains simply aren't compatible with the --jobs option, and can lead to unexpected results. If you're experiencing strange dependency errors, just try to make without --jobs.