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
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.
- 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).
- 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 information than the function-level profiles returned by most profilers.
- 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.
Scalene is distributed as a
pip package. You can install it as follows:
% 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.
The following command will run Scalene to only perform line-level CPU profiling on a provided example program.
% python -m scalene test/testme.py
To perform both line-level CPU and memory profiling, you first need to build the specialized memory allocator by running
Profiling on a Mac OS X system:
% 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 (seconds)||Slowdown||Line-level?||CPU?||Memory?||Unmodified code?|
||aborted after 30 minutes||>232x||:heavy_check_mark:||:heavy_check_mark:||needs
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 = 88.34% out of 3.46s. Line | CPU % | benchmarks/pystone.py [... lines omitted ...] 137 | 0.65% | def Proc1(PtrParIn): 138 | 1.96% | PtrParIn.PtrComp = NextRecord = PtrGlb.copy() 139 | 0.65% | PtrParIn.IntComp = 5 140 | 2.61% | NextRecord.IntComp = PtrParIn.IntComp 141 | 0.98% | NextRecord.PtrComp = PtrParIn.PtrComp 142 | 2.94% | NextRecord.PtrComp = Proc3(NextRecord.PtrComp) 143 | 0.65% | if NextRecord.Discr == Ident1: 144 | 0.33% | NextRecord.IntComp = 6 145 | 3.27% | NextRecord.EnumComp = Proc6(PtrParIn.EnumComp) 146 | 0.33% | NextRecord.PtrComp = PtrGlb.PtrComp 147 | 2.29% | NextRecord.IntComp = Proc7(NextRecord.IntComp, 10) 148 | | else: 149 | | PtrParIn = NextRecord.copy() 150 | 0.33% | NextRecord.PtrComp = None 151 | | return PtrParIn
And here is an example with memory profiling enabled, running the Julia benchmark.
benchmarks/julia1_nopil.py: % of CPU time = 96.23% out of 9.03s. Line | CPU % | Memory (MB)| benchmarks/julia1_nopil.py 1 | | | # Pasted from Chapter 2, High Performance Python - O'Reilly Media; 2 | | | # minor modifications for Python 3 by Emery Berger 3 | | | 4 | | | """Julia set generator without optional PIL-based image drawing""" 5 | | | import time 6 | | | # area of complex space to investigate 7 | | | x1, x2, y1, y2 = -1.8, 1.8, -1.8, 1.8 8 | | | c_real, c_imag = -0.62772, -.42193 9 | | | 10 | | | #@profile 11 | | 0.12 | def calculate_z_serial_purepython(maxiter, zs, cs): 12 | | | """Calculate output list using Julia update rule""" 13 | | 0.12 | output =  * len(zs) 14 | 0.35% | 25.00 | for i in range(len(zs)): 15 | | | n = 0 16 | 0.23% | -24.75 | z = zs[i] 17 | 0.46% | -27.75 | c = cs[i] 18 | 17.84% | | while abs(z) < 2 and n < maxiter: 19 | 50.86% | 998.12 | z = z * z + c 20 | 23.01% | -968.00 | n += 1 21 | 0.23% | | output[i] = n 22 | | | return output
Positive memory numbers indicate memory consumption in megabytes; negative memory numbers indicate memory reclamation. Note that because of the way Python's memory management works, frequent allocation and de-allocation (as in lines 19-20 above) show up as high positive memory on one line followed by an (approximately) corresponding negative memory on the following line(s).
Logo created by Sophia Berger.
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