[Python-ideas] Why not picoseconds?
victor.stinner at gmail.com
Sun Oct 15 14:28:40 EDT 2017
I proposed to use nanoseconds because UNIX has 1 ns resolution in timespec,
the most recent API, and Windows has 100 ns.
Using picoseconds would confuse users who may expect sub-nanosecond
resolution, whereas no OS support them currently.
Moreover, nanoseconds as int already landed in os.stat and os.utime.
Last but not least, I already strugle in pytime.c to prevent integer
overflow with 1 ns resolution. It can quickly become much more complex if
there is no native C int type supporting a range large enough to more 1
picosecond resolution usable. I really like using int64_t for _PyTime_t,
it's well supported, very easy to use (ex: "t = t2 - t1"). 64-bit int
supports year after 2200 for delta since 1970.
Note: I only know Ruby which chose picoseconds.
Le 15 oct. 2017 19:18, "Antoine Pitrou" <solipsis at pitrou.net> a écrit :
> Since new APIs are expensive and we'd like to be future-proof, why not
> move to picoseconds? That would be safe until clocks reach the THz
> barrier, which is quite far away from us.
> On Fri, 13 Oct 2017 16:12:39 +0200
> Victor Stinner <victor.stinner at gmail.com>
> > Hi,
> > I would like to add new functions to return time as a number of
> > nanosecond (Python int), especially time.time_ns().
> > It would enhance the time.time() clock resolution. In my experience,
> > it decreases the minimum non-zero delta between two clock by 3 times,
> > new "ns" clock versus current clock: 84 ns (2.8x better) vs 239 ns on
> > Linux, and 318 us (2.8x better) vs 894 us on Windows, measured in
> > Python.
> > The question of this long email is if it's worth it to add more "_ns"
> > time functions than just time.time_ns()?
> > I would like to add:
> > * time.time_ns()
> > * time.monotonic_ns()
> > * time.perf_counter_ns()
> > * time.clock_gettime_ns()
> > * time.clock_settime_ns()
> > time(), monotonic() and perf_counter() clocks are the 3 most common
> > clocks and users use them to get the best available clock resolution.
> > clock_gettime/settime() are the generic UNIX API to access these
> > clocks and so should also be enhanced to get nanosecond resolution.
> > == Nanosecond resolution ==
> > More and more clocks have a frequency in MHz, up to GHz for the "TSC"
> > CPU clock, and so the clocks resolution is getting closer to 1
> > nanosecond (or even better than 1 ns for the TSC clock!).
> > The problem is that Python returns time as a floatting point number
> > which is usually a 64-bit binary floatting number (in the IEEE 754
> > format). This type starts to loose nanoseconds after 104 days.
> > Conversion from nanoseconds (int) to seconds (float) and then back to
> > nanoseconds (int) to check if conversions loose precision:
> > # no precision loss
> > >>> x=2**52+1; int(float(x * 1e-9) * 1e9) - x
> > 0
> > # precision loss! (1 nanosecond)
> > >>> x=2**53+1; int(float(x * 1e-9) * 1e9) - x
> > -1
> > >>> print(datetime.timedelta(seconds=2**53 / 1e9))
> > 104 days, 5:59:59.254741
> > While a system administrator can be proud to have an uptime longer
> > than 104 days, the problem also exists for the time.time() clock which
> > returns the number of seconds since the UNIX epoch (1970-01-01). This
> > clock started to loose nanoseconds since mid-May 1970 (47 years ago):
> > >>> import datetime
> > >>> print(datetime.datetime(1970, 1, 1) + datetime.timedelta(seconds=2**53
> / 1e9))
> > 1970-04-15 05:59:59.254741
> > == PEP 410 ==
> > Five years ago, I proposed a large and complex change in all Python
> > functions returning time to support nanosecond resolution using the
> > decimal.Decimal type:
> > https://www.python.org/dev/peps/pep-0410/
> > The PEP was rejected for different reasons:
> > * it wasn't clear if hardware clocks really had a resolution of 1
> > nanosecond, especially when the clock is read from Python, since
> > reading a clock in Python also takes time...
> > * Guido van Rossum rejected the idea of adding a new optional
> > parameter to change the result type: it's an uncommon programming
> > practice (bad design in Python)
> > * decimal.Decimal is not widely used, it might be surprised to get such
> > == CPython enhancements of the last 5 years ==
> > Since this PEP was rejected:
> > * the os.stat_result got 3 fields for timestamps as nanoseconds
> > (Python int): st_atime_ns, st_ctime_ns, st_mtime_ns
> > * Python 3.3 got 3 new clocks: time.monotonic(), time.perf_counter()
> > and time.process_time()
> > * I enhanced the private C API of Python handling time (API called
> > "pytime") to store all timings as the new _PyTime_t type which is a
> > simple 64-bit signed integer. The unit of _PyTime_t is not part of the
> > API, it's an implementation detail. The unit is currently 1
> > nanosecond.
> > This week, I converted one of the last clock to new _PyTime_t format:
> > time.perf_counter() now has internally a resolution of 1 nanosecond,
> > instead of using the C double type.
> > XXX technically https://github.com/python/cpython/pull/3983 is not
> > merged yet :-)
> > == Clocks resolution in Python ==
> > I implemented time.time_ns(), time.monotonic_ns() and
> > time.perf_counter_ns() which are similar of the functions without the
> > "_ns" suffix, but return time as nanoseconds (Python int).
> > I computed the smallest difference between two clock reads (ignoring a
> > differences of zero):
> > Linux:
> > * time_ns(): 84 ns <=== !!!
> > * time(): 239 ns <=== !!!
> > * perf_counter_ns(): 84 ns
> > * perf_counter(): 82 ns
> > * monotonic_ns(): 84 ns
> > * monotonic(): 81 ns
> > Windows:
> > * time_ns(): 318000 ns <=== !!!
> > * time(): 894070 ns <=== !!!
> > * perf_counter_ns(): 100 ns
> > * perf_counter(): 100 ns
> > * monotonic_ns(): 15000000 ns
> > * monotonic(): 15000000 ns
> > The difference on time.time() is significant: 84 ns (2.8x better) vs
> > 239 ns on Linux and 318 us (2.8x better) vs 894 us on Windows. The
> > difference will be larger next years since every day adds
> > 864,00,000,000,000 nanoseconds to the system clock :-) (please don't
> > bug me with leap seconds! you got my point)
> > The difference on perf_counter and monotonic clocks are not visible in
> > this quick script since my script runs less than 1 minute, my computer
> > uptime is smaller than 1 weak, ... and Python internally starts these
> > clocks at zero *to reduce the precision loss*! Using an uptime larger
> > than 104 days, you would probably see a significant difference (at
> > least +/- 1 nanosecond) between the regular (seconds as double) and
> > the "_ns" (nanoseconds as int) clocks.
> > == How many new nanosecond clocks? ==
> > The PEP 410 proposed to modify the following functions:
> > * os module: fstat(), fstatat(), lstat(), stat() (st_atime, st_ctime
> > and st_mtime fields of the stat structure), sched_rr_get_interval(),
> > times(), wait3() and wait4()
> > * resource module: ru_utime and ru_stime fields of getrusage()
> > * signal module: getitimer(), setitimer()
> > * time module: clock(), clock_gettime(), clock_getres(), monotonic(),
> > time() and wallclock() ("wallclock()" was finally called "monotonic",
> > see PEP 418)
> > According to my tests of the previous section, the precision loss
> > starts after 104 days (stored in nanoseconds). I don't know if it's
> > worth it to modify functions which return "CPU time" or "process time"
> > of processes, since most processes live shorter than 104 days. Do you
> > care of a resolution of 1 nanosecond for the CPU and process time?
> > Maybe we need 1 nanosecond resolution for profiling and benchmarks.
> > But in that case, you might want to implement your profiler in C
> > rather in Python, like the hotshot module, no? The "pytime" private
> > API of CPython gives you clocks with a resolution of 1 nanosecond.
> > == Annex: clock performance ==
> > To have an idea of the cost of reading the clock on the clock
> > resolution in Python, I also ran a microbenchmark on *reading* a
> > clock. Example:
> > $ ./python -m perf timeit --duplicate 1024 -s 'import time; t=time.time'
> > Linux (Mean +- std dev):
> > * time.time(): 45.4 ns +- 0.5 ns
> > * time.time_ns(): 47.8 ns +- 0.8 ns
> > * time.perf_counter(): 46.8 ns +- 0.7 ns
> > * time.perf_counter_ns(): 46.0 ns +- 0.6 ns
> > Windows (Mean +- std dev):
> > * time.time(): 42.2 ns +- 0.8 ns
> > * time.time_ns(): 49.3 ns +- 0.8 ns
> > * time.perf_counter(): 136 ns +- 2 ns <===
> > * time.perf_counter_ns(): 143 ns +- 4 ns <===
> > * time.monotonic(): 38.3 ns +- 0.9 ns
> > * time.monotonic_ns(): 48.8 ns +- 1.2 ns
> > Most clocks have the same performance except of perf_counter on
> > Windows: around 140 ns whereas other clocks are around 45 ns (on Linux
> > and Windows): 3x slower. Maybe the "bad" perf_counter performance can
> > be explained by the fact that I'm running Windows in a VM, which is
> > not ideal for benchmarking. Or maybe my C implementation of
> > time.perf_counter() is slow?
> > Note: I expect that a significant part of the numbers are the cost of
> > Python function calls. Reading these clocks using the Python C
> > functions are likely faster.
> > Victor
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