scalene 0.7.7

Scalene: A high-resolution, low-overhead CPU and memory profiler for Python

scalene: a high-performance CPU and memory profiler for Python

by Emery Berger

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About Scalene

Scalene is a high-performance CPU and memory profiler for Python that does a few things that other Python profilers do not and cannot do. It runs orders of magnitude faster than other profilers while delivering far more detailed information.

1. Scalene is fast. It uses sampling instead of instrumentation or relying on Python's tracing facilities. Its overhead is typically no more than 10-20% (and often less).
2. Scalene is precise. Unlike most other Python profilers, Scalene performs CPU profiling at the line level, pointing to the specific lines of code that are responsible for the execution time in your program. This level of detail can be much more useful than the function-level profiles returned by most profilers.
3. Scalene separates out time spent running in Python from time spent in native code (including libraries). Most Python programmers aren't going to optimize the performance of native code (which is usually either in the Python implementation or external libraries), so this helps developers focus their optimization efforts on the code they can actually improve.
4. Scalene profiles memory usage. In addition to tracking CPU usage, Scalene also points to the specific lines of code responsible for memory growth. It accomplishes this via an included specialized memory allocator.

Installation

Scalene is distributed as a pip package and works on Linux and Mac OS X platforms. You can install it as follows:

  % pip install scalene

or

  % python -m pip install scalene

NOTE: Currently, installing Scalene in this way does not install its memory profiling library, so you will only be able to use it to perform CPU profiling. To take advantage of its memory profiling capability, you will need to download this repository.

NEW: You can now install the memory profiling part on Mac OS X using Homebrew.

  % brew tap emeryberger/scalene
  % brew install --head libscalene

This will install a scalene script you can use (see below).

Usage

The following command will run Scalene to only perform line-level CPU profiling on a provided example program.

  % python -m scalene test/testme.py

If you have installed the Scalene library with Homebrew, you can just invoke scalene to perform both line-level CPU and memory profiling:

  % scalene test/testme.py

Otherwise, you first need to build the specialized memory allocator by running make:

  % make

Profiling on a Mac OS X system (without using Homebrew):

  % DYLD_INSERT_LIBRARIES=$PWD/libscalene.dylib PYTHONMALLOC=malloc python -m scalene test/testme.py

Profiling on a Linux system:

  % LD_PRELOAD=$PWD/libscalene.so PYTHONMALLOC=malloc python -m scalene test/testme.py

Comparison to Other Profilers

Performance and Features

Below is a table comparing various profilers to scalene, running on an example Python program (benchmarks/julia1_nopil.py) from the book High Performance Python, by Gorelick and Ozsvald. All of these were run on a 2016 MacBook Pro.

	Time	Slowdown	Line-level?	CPU?	Wall clock vs. CPU time?	Separates Python from native?	Memory?	Unmodified code?
original program	6.71s	1.0x
cProfile	11.04s	1.65x	function-level	✔	wall clock			✔
Profile	202.26s	30.14x	function-level	✔	CPU time			✔
pyinstrument	9.83s	1.46x	function-level	✔	wall clock			✔
line_profiler	78.0s	11.62x	✔	✔	wall clock			needs @profile decorators
pprofile (deterministic)	403.67s	60.16x	✔	✔	wall clock		✔
pprofile (statistical)	7.47s	1.11x	✔	✔	wall clock			✔
yappi (CPU)	127.53s	19.01x	function-level	✔	CPU time			✔
yappi (wallclock)	21.45s	3.2x	function-level	✔	wall clock			✔
py-spy	7.25s	1.08x	✔	✔	both			✔
memory_profiler	aborted after 2 hours	>1000x	✔				✔	needs @profile decorators
scalene (CPU only)	6.98s	1.04x	✔	✔	both	✔		✔
scalene (CPU + memory)	7.68s	1.14x	✔	✔	both	✔	✔	✔

Output

Scalene prints annotated source code for the program being profiled and any modules it uses in the same directory or subdirectories. Here is a snippet from pystone.py, just using CPU profiling:

    benchmarks/pystone.py: % of CPU time = 100.00% out of   3.66s.
          	 |     CPU % |     CPU % |   
      Line	 |  (Python) |  (native) |  [benchmarks/pystone.py]
    --------------------------------------------------------------------------------
    [... lines omitted ...]
       137	 |     0.27% |     0.14% | def Proc1(PtrParIn):
       138	 |     1.37% |     0.11% |     PtrParIn.PtrComp = NextRecord = PtrGlb.copy()
       139	 |     0.27% |     0.22% |     PtrParIn.IntComp = 5
       140	 |     1.37% |     0.77% |     NextRecord.IntComp = PtrParIn.IntComp
       141	 |     2.47% |     0.93% |     NextRecord.PtrComp = PtrParIn.PtrComp
       142	 |     1.92% |     0.78% |     NextRecord.PtrComp = Proc3(NextRecord.PtrComp)
       143	 |     0.27% |     0.17% |     if NextRecord.Discr == Ident1:
       144	 |     0.82% |     0.30% |         NextRecord.IntComp = 6
       145	 |     2.19% |     0.79% |         NextRecord.EnumComp = Proc6(PtrParIn.EnumComp)
       146	 |     1.10% |     0.39% |         NextRecord.PtrComp = PtrGlb.PtrComp
       147	 |     0.82% |     0.06% |         NextRecord.IntComp = Proc7(NextRecord.IntComp, 10)
       148	 |           |           |     else:
       149	 |           |           |         PtrParIn = NextRecord.copy()
       150	 |     0.82% |     0.32% |     NextRecord.PtrComp = None
       151	 |           |           |     return PtrParIn

And here is an example with memory profiling enabled, running the Julia benchmark. The top line is a "spark line" summarizing memory consumption over time.

    Memory usage: ▁▁▄▇█▇▇▇█▇█▇█▇█▇█▇▇▇▇█▇▇█▇█▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇█ (max: 105.73MB)
    benchmarks/julia1_nopil.py: % of CPU time = 100.00% out of   9.11s.
          	 |     CPU % |     CPU % | Avg memory  | Memory      | 
      Line	 |  (Python) |  (native) | growth (MB) | usage (%)   | [benchmarks/julia1_nopil.py]
    --------------------------------------------------------------------------------
         1	 |           |           |             |             | import sys
    [... lines omitted ...]
        30	 |           |           |             |             | def calculate_z_serial_purepython(maxiter, zs, cs):
        31	 |           |           |             |             |     """Calculate output list using Julia update rule"""
        32	 |           |           |          18 |       0.74% |     output = [0] * len(zs)
        33	 |     0.44% |     0.06% |          16 |       1.32% |     for i in range(len(zs)):
        34	 |           |           |             |             |         n = 0
        35	 |     0.22% |     0.04% |         -16 |             |         z = zs[i]
        36	 |     0.22% |     0.07% |             |             |         c = cs[i]
        37	 |    26.12% |     5.57% |             |             |         while abs(z) < 2 and n < maxiter:
        38	 |    36.04% |     7.74% |          16 |      85.09% |             z = z * z + c
        39	 |    12.01% |     2.70% |         -16 |       3.96% |             n += 1
        40	 |     0.33% |     0.10% |             |             |         output[i] = n
        41	 |           |           |             |             |     return output
        42	 |           |           |             |             | 

Positive memory numbers indicate total memory allocation in megabytes; negative memory numbers indicate memory reclamation. Memory usage means how much of the total memory allocation activity a particular line represents.

Acknowledgements

Logo created by Sophia Berger.