Has anyone timed the impact of the Spectre/Meltdown patches for OS X or macOS?
I just updated my El Capitan based Mac Pro (#1), and noticed a 6% slowdown (#2) on my major benchmark (compiling 500 C files (about 1/2 million lines) from the Terminal).
The update is the "Security Update 2018-001", which addresses Meltdown and Spectre according to https://support.apple.com/en-us/HT208465
Stan
early 2009, 16 cores, 20 GB RAM
Tests repeated 3 times, files brought into memory first, best of the three tests compared showing 6% slowdown. If average of times used instead, still shows 6%. No other "apps" running.
I implemented a program which uses different CUDA streams from different CPU threads. Memory copying is implemented via cudaMemcpyAsync using those streams. Kernel launches are also using those streams. The program is doing double-precision computations (and I suspect this is the culprit, however, cuBlas reaches 75-85% CPU usage for multiplication of matrices of doubles). There are also reduction operations, however they are implemented via if(threadIdx.x < s) with s decreasing 2 times in each iteration, so stalled warps should be available to other blocks. The application is GPU and CPU intensive, it starts with another piece of work as soon as the previous has finished. So I expect it to reach 100% of either CPU or GPU.
The problem is that my program generates 30-40% of GPU load (and about 50% of CPU load), if trusting GPU-Z 1.9.0. Memory Controller Load is 9-10%, Bus Interface Load is 6%. This is for the number of CPU threads equal to the number of CPU cores. If I double the number of CPU threads, the loads stay about the same (including the CPU load).
So why is that? Where is the bottleneck?
I am using GeForce GTX 560 Ti, CUDA 8RC, MSVC++2013, Windows 10.
One my guess is that Windows 10 applies some aggressive power saving, even though GPU and CPU temperatures are low, the power plan is set to "High performance" and the power supply is 700W while power consumption with max CPU and GPU TDP is about 550W.
Another guess is that double-precision speed is 1/12 of the single-precision speed because there are 1 double-precision CUDA core per 12 single-precision CUDA cores on my card, and GPU-Z takes as 100% the situation when all single-precision and double-precision cores are used. However, the numbers do not quite match.
Apparently the reason was low occupancy due to CUDA threads using too many registers by default. To tell the compiler the limit on the number of registers per thread, __launch_bounds__ can be used, as described here. So to be able to launch all 1536 threads in 560 Ti, for block size 256 the following can be specified:
_global__ void __launch_bounds__(256, 6) MyKernel(...) { ... }
After limiting the number of registers per CUDA thread, the GPU usage has raised to 60% for me.
By the way, 5xx series cards are still supported by NSight v5.1 for Visual Studio. It can be downloaded from the archive.
EDIT: the following flags have further increased GPU usage to 70% in an application that uses multiple GPU streams from multiple CPU threads:
cudaSetDeviceFlags(cudaDeviceScheduleYield | cudaDeviceMapHost | cudaDeviceLmemResizeToMax);
cudaDeviceScheduleYield lets other threads execute when a CPU
thread is waiting on GPU operation, rather than spinning GPU for the
result.
cudaDeviceLmemResizeToMax, as I understood it, makes kernel
launches themselves asynchronous and avoids excessive local memory
allocations&deallocations.
Recently I profiled some MATLAB code and I was shocked to see the following in a heavily used function:
5.76 198694 58 persistent CONSTANTS;
3.44 198694 59 if isempty(CONSTANTS) % initialize CONSTANTS
In other words, MATLAB spent about 9 seconds, over 198694 function calls, declaring the persistent CONSTANTS and checking if it has been initialized. That represents 13% of the total time spent in that function.
Do persistent variables really carry that much of a performance penalty in MATLAB? Or are we doing something terribly wrong here?
UPDATE
#Andrew I tried your sample script and I am very, very perplexed by the output:
time calls line
6 function has_persistent
6.48 200000 7 persistent CONSTANTS
1.91 200000 8 if isempty(CONSTANTS)
9 CONSTANTS = 42;
10 end
I tried the bench() command and it showed my machine in the middle range of the sample machines. Running Ubuntu 64 bits on a Intel(R) Core(TM) i7 CPU, 4GB RAM.
That's the standard way of using persistent variables in Matlab. You're doing what you're supposed to. There will be noticable overhead for it, but your timings do seem kind of surprisingly high.
Here's a similar test I ran in 32-bit Matlab R2009b on a 3.0 GHz Intel Core 2 QX9650 machine under Windows XP x64. Similar results on other machines and versions. About 5x faster than your timings.
Test:
function call_has_persistent
for i = 1:200000
has_persistent();
end
function has_persistent
persistent CONSTANTS
if isempty(CONSTANTS)
CONSTANTS = 42;
end
Results:
0.89 200000 7 persistent CONSTANTS
0.25 200000 8 if isempty(CONSTANTS)
What Matlab version, OS, and CPU are you running on? What does CONSTANTS get initialized with? Does Matlab's bench() output seem reasonable for your machine?
Your timings do seem high. There may be a bug or config issue there to fix. But if you really want to get Matlab code fast, the standard advice is to "vectorize" it: restructure the code so that it makes fewer function calls on larger input arrays, and makes use of Matlab's built in vectorized functions instead of loops or control structures, to avoid having 200,000 calls to the function in the first place. If possible. Matlab has relatively high overhead per function or method call (see Is MATLAB OOP slow or am I doing something wrong? for some numbers), so you can often get more mileage by refactoring to eliminate function calls instead of making the individual function calls faster.
It may be worth benchmarking some other basic Matlab operations on your machine, to see if it's just "persistent" that seems slow. Also try profiling just this little call_has_persistent test script in isolation to see if the context of your function makes a difference.
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?
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.
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)
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.
Both are not wrong. To be at peace with yourself and with author of software you're compiling (different multi-thread/single-thread restrictions apply at software level itself), I suggest you use:
make -j`nproc`
Notes: nproc is linux command that will return number of cores/threads(modern CPU) available on system. Placing it under ticks ` like above will pass the number to the make command.
Additional info: As someone mentioned, using all cores/threads to compile software can literally choke your box to near death (being unresponsive) and might even take longer than using less cores. As I seen one Slackware user here posted he had dual core CPU but still provided testing up to j 8, which stopped being different at j 2 (only 2 hardware cores that CPU can utilize). So, to avoid unresponsive box i suggest you run it like this:
make -j`nproc --ignore=2`
This will pass the output of nproc to make and subtract 2 cores from its result.
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. :)
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
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
From my experience, there must be some performance benefits when adding extra jobs.
It is simply because disk I/O is one of the bottle necks besides CPU. However it is not easy to decide on the number of extra jobs as it is highly inter-connected with the number of cores and types of the disk being used.
Many years later, the majority of these answers are still correct. However, there has been a bit of a change: Using more jobs than you have physical cores now gives a genuinely significant speedup. As an addendum to Dascandy's table, here's my times for compiling a project on a AMD Ryzen 5 3600X on linux. (The Powder Toy, commit c6f653ac3cef03acfbc44e8f29f11e1b301f1ca2)
I recommend checking yourself, but I've found with input from others that using your logical core count for job count works well on Zen. Alongside that, the system does not seem to lose responsiveness. I imagine this applies to recent Intel CPUs as well. Do note I have an SSD, as well, so it may be worth it to test your CPU yourself.
scons -j1 --release --native 120.68s user 9.78s system 99% cpu 2:10.60 total
scons -j2 --release --native 122.96s user 9.59s system 197% cpu 1:07.15 total
scons -j3 --release --native 125.62s user 9.75s system 292% cpu 46.291 total
scons -j4 --release --native 128.26s user 10.41s system 385% cpu 35.971 total
scons -j5 --release --native 133.73s user 10.33s system 476% cpu 30.241 total
scons -j6 --release --native 144.10s user 11.24s system 564% cpu 27.510 total
scons -j7 --release --native 153.64s user 11.61s system 653% cpu 25.297 total
scons -j8 --release --native 161.91s user 12.04s system 742% cpu 23.440 total
scons -j9 --release --native 169.09s user 12.38s system 827% cpu 21.923 total
scons -j10 --release --native 176.63s user 12.70s system 910% cpu 20.788 total
scons -j11 --release --native 184.57s user 13.18s system 989% cpu 19.976 total
scons -j12 --release --native 192.13s user 14.33s system 1055% cpu 19.553 total
scons -j13 --release --native 193.27s user 14.01s system 1052% cpu 19.698 total
scons -j14 --release --native 193.62s user 13.85s system 1076% cpu 19.270 total
scons -j15 --release --native 195.20s user 13.53s system 1056% cpu 19.755 total
scons -j16 --release --native 195.11s user 13.81s system 1060% cpu 19.692 total
( -jinf test not included, as it is not supported by scons.)
Tests done on Ubuntu 19.10 w/ a Ryzen 5 3600X, Samsung 860 Evo SSD (SATA), and 32GB RAM
Final note: Other people with a 3600X may get better times than me. When doing this test, I had Eco mode enabled, reducing the CPU's speed a little.
YES! On my 3950x, I run -j32 and it saves hours of compile time! I can still watch youtube, browse the web, etc. during compile without any difference. The processor isn't always pegged even with a 1TB 970 PRO nvme or 1TB Auros Gen4 nvme and 64GB of 3200C14. Even when it is, I don't notice UI wise. I plan on testing with -j48 in the near future on some big upcoming projects. I expect, as you probably do, to see some impressive improvement. Those still with a quad-core might not get the same gains....
Linus himself just upgraded to a 3970x and you can bet your bottom dollar, he is at least running -j64.
It seems like people are compiling MRI Ruby (1.8.7) for 64-bit platforms. I've been searching and reading for a while now without really getting the answers I want. What I want to know is if any of you guys actually used more than 4GB of memory in Ruby? Is Ruby truly 64-bits if you compile it that way?
I've found comments in the source code indicating that it's not tested on 64-bits. For instance it says "BigDecimal has not yet been compiled and tested on 64 bit integer system." in the comments for BigDecimal.
It would also be interesting to know how the other implementations of Ruby do in 64-bits.
MRI (both the 1.8.x and 1.9.x line) can be compiled as 64 bits.
For example, Snow Leopard comes bundled with 1.8.7 compiled as 64 bits. This can be seen in the activity monitor, or from irb by asking, for example 42.size. You'll get 8 (bytes) if it is compiled in 64 bits, 4 (bytes) otherwise.
Ruby will be able to access over 4G of ram. For example:
$ irb
>> n = (1 << 29) + 8
=> 536870920
>> x = Array.new(n, 42); x.size
=> 536870921 # one greater because it holds elements from 0 to n inclusive
Getting the last line will take a while if you don't have more than 4 G or ram because the OS will swap a lot, but even on my machine with 4 GB it works. Virtual ram size for the process was 4.02 G.
I updated the comment in the bigdecimal html file which was outdated (from march 2003...)