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Summary in the conclusion: "The end result is an implementation several orders of magnitude faster than the current reference implementation in Java. ... [Pyth

Summary in the conclusion: "The end result is an implementation several orders of magnitude faster than the current reference implementation in Java. ... [Python] makes the first version easy to implement and provides plenty of powerful tools for optimization later when you understand where and how you need it." [edited to be a statement instead of rhetorical question]

vmarsy 11y ago
All the points the author makes through the post are interesting, and Python is definitely great for protoyping, but I think the initial premise is false: "people don't tend to think of [Python] as a high performance language; for that you would want a compiled language -- ideally C or C++ but Java would do." Java is compiled to bytecode, but it isn't a "compiled language" since that bytecode has to be interpreted by the JVM. All the good libraries the author mention are probably implemented in C or FORTRAN, and so a true implementation in C with the right compiler optimizations would for sure be faster than the python code.
- andylynch 11y ago
  Java(well, Sun's Hotspot) is most definitely compiled, but mostly by the JIT at runtime. The interpreter only runs for the the first warmup, usually 10,000 method calls and the JIT kicks in and translates the method to machine code. The tricky part is it's not simple to control exactly how and when it does its job, although it usually works very well.
- acchow 11y ago
  The major implementations of the JVM are jited, not interpreted. Yes, you get great performance with jitting, tho not necessarily without unpredictable GC pauses and without a loss of energy efficiency from having to run a jit alongside your actual code.
  - xxs 11y ago
    GC pauses have little to nothing to do JIT process itself. You can get JVMs w/ read barriers and soft-realtime guarantees.[0][1] Please note, gc and jit are quite a different thing, even if the gc needs help from the jit. [0]: https://www-01.ibm.com/support/knowledgecenter/SSYKE2_7.0.0/com.ibm.java.aix.71.doc/diag/understanding/metro_introd.html https://www-01.ibm.com/support/knowledgecenter/SSYKE2_7.0.0/... [1]: https://www.azul.com/products/zing/ https://www.azul.com/products/zing/
- olau 11y ago
  "...and so a true implementation in C with the right compiler optimizations would for sure be faster than the python code." True. But this assumes that time is not a constraint. I think you need to think of it this way (as a thought experiment): you start two programmers off, one in C and one in Python, both with a vague understanding of how to solve the problem and approximately the same skill level. Then after X hours, you stop both and see how far they've gotten. I think the argument then is that you might find that the C programmer hasn't yet solved the problem, whereas the Python programmer might have a solution + a deeper understanding of where the slow parts are, and started optimizing those. So in the end the C programmer may win, but perhaps it's not about winning in this sense but more about how fast you can get to a point where you can move on to the next thing.
  - pjc50 11y ago
    Absolutely. For this kind of one-shot scientific computing, the only way the C programmer can win is if the Python programmer is sitting on their hands for weeks waiting for their program to run.
    - JupiterMoon 11y ago
      Unless (1) the relevant library happens to be written in C and there are no python bindings and the problem is simple OR (2) there is an existing solution which is 95% complete in C and one needs to write from scratch in python. I've never come across situation (1) with a new project. Situation (2) is quite common.
      - ovis 11y ago
        But in those cases, the Python programmer could just wrap the C library in Python, which I suspect is often a lot easier than starting from scratch.
  - emcq 11y ago
    I've tried to do a test like this and the results surprised me. While the testing conditions weren't perfect, I tracked how much time it took to port Fortran code to C++, and how much time it took to write the C++/CUDA optimized version. I would have expected writing all the optimized CUDA kernels would add significant time to the project, but in reality it was something like 1.3x for a solution 15x faster. I think as developers we often underestimate the amount of work we need to put into the non coding part of development for larger projects, like testing, debugging, optimization, and design. When these components become important then despite the local productivity gains of using rapid development languages like python or Julia, it would overall be a wash if you're writing the system from scratch in a language like C.
cygnus_a 11y ago
Actually, even the subsection headings in bold give a very succinct summary: - Python has easy development (https://xkcd.com/353/ https://xkcd.com/353/) - Great libraries (ie, free matlab) - Cython for efficiency via C - The algorithms themselves determine speediness (ie numerical methods)
- dagw 11y ago
  The algorithms themselves determine speediness This is so important I wish people would focus more on it. I recently rewrote some Javascript code in (pure) python and got a good 2 orders of magnitude speed up on large inputs just by picking the right data structures and replacing an O(n^3) nested loop with an O(n log n) approach.
  - timClicks 11y ago
    Which data structures did you use? In Python, I tend to rely on dict, list, and set for 90% or more of my code. I wouldn't want to rely on structures written in pure Python.
    - dagw 11y ago
      Nothing exotic. One of the changes for example was replacing a list of list with a set of tuples, which greatly sped up checking if an object was in the collection. Another change was using a generator comprehension and an included itertool function rather than hand rolled nested for loops.
      - echlebek 11y ago
        Once you've exhausted all the low-hanging fruit, like people calling .keys() on dicts, or doing unnecessary linear searches, Cython really starts to shine. I've seen it perform ~40 times better than pure Python in time-consuming loops. We do scientific computing at my company. Numpy does 90% of the work, but there are some algorithms that just aren't easily expressed with arrays. That's where Cython comes in.
anon4 11y ago
I agree with his conclusion that Python has great tools to help performance, but the GIL makes it impossible to effectively make use of separate cores, so I have to wonder just how badly the Java version was written. Properly written high-performance Java (store all data in contiguous arrays, do away with all the OO crap, reuse objects like crazy) should perform as fast, or faster than optimized Python, and you can scale it to as many threads as you like.
- lqdc13 11y ago
  Python has MPI4PY and Multiprocessing. Not sure why you need threads in the first place. Also, many matrix operations autoscale to all cores when compiled against Atlas. Finally, you can release the GIL with Cython: http://docs.cython.org/src/userguide/parallelism.html http://docs.cython.org/src/userguide/parallelism.html

vmarsy 11y ago

All the points the author makes through the post are interesting, and Python is definitely great for protoyping, but I think the initial premise is false: "people don't tend to think of [Python] as a high performance language; for that you would want a compiled language -- ideally C or C++ but Java would do." Java is compiled to bytecode, but it isn't a "compiled language" since that bytecode has to be interpreted by the JVM. All the good libraries the author mention are probably implemented in C or FORTRAN, and so a true implementation in C with the right compiler optimizations would for sure be faster than the python code.

andylynch 11y ago
Java(well, Sun's Hotspot) is most definitely compiled, but mostly by the JIT at runtime. The interpreter only runs for the the first warmup, usually 10,000 method calls and the JIT kicks in and translates the method to machine code. The tricky part is it's not simple to control exactly how and when it does its job, although it usually works very well.
acchow 11y ago
The major implementations of the JVM are jited, not interpreted. Yes, you get great performance with jitting, tho not necessarily without unpredictable GC pauses and without a loss of energy efficiency from having to run a jit alongside your actual code.
- xxs 11y ago
  GC pauses have little to nothing to do JIT process itself. You can get JVMs w/ read barriers and soft-realtime guarantees.[0][1] Please note, gc and jit are quite a different thing, even if the gc needs help from the jit. [0]: https://www-01.ibm.com/support/knowledgecenter/SSYKE2_7.0.0/com.ibm.java.aix.71.doc/diag/understanding/metro_introd.html https://www-01.ibm.com/support/knowledgecenter/SSYKE2_7.0.0/... [1]: https://www.azul.com/products/zing/ https://www.azul.com/products/zing/
olau 11y ago
"...and so a true implementation in C with the right compiler optimizations would for sure be faster than the python code." True. But this assumes that time is not a constraint. I think you need to think of it this way (as a thought experiment): you start two programmers off, one in C and one in Python, both with a vague understanding of how to solve the problem and approximately the same skill level. Then after X hours, you stop both and see how far they've gotten. I think the argument then is that you might find that the C programmer hasn't yet solved the problem, whereas the Python programmer might have a solution + a deeper understanding of where the slow parts are, and started optimizing those. So in the end the C programmer may win, but perhaps it's not about winning in this sense but more about how fast you can get to a point where you can move on to the next thing.
- pjc50 11y ago
  Absolutely. For this kind of one-shot scientific computing, the only way the C programmer can win is if the Python programmer is sitting on their hands for weeks waiting for their program to run.
  - JupiterMoon 11y ago
    Unless (1) the relevant library happens to be written in C and there are no python bindings and the problem is simple OR (2) there is an existing solution which is 95% complete in C and one needs to write from scratch in python. I've never come across situation (1) with a new project. Situation (2) is quite common.
    - ovis 11y ago
      But in those cases, the Python programmer could just wrap the C library in Python, which I suspect is often a lot easier than starting from scratch.
- emcq 11y ago
  I've tried to do a test like this and the results surprised me. While the testing conditions weren't perfect, I tracked how much time it took to port Fortran code to C++, and how much time it took to write the C++/CUDA optimized version. I would have expected writing all the optimized CUDA kernels would add significant time to the project, but in reality it was something like 1.3x for a solution 15x faster. I think as developers we often underestimate the amount of work we need to put into the non coding part of development for larger projects, like testing, debugging, optimization, and design. When these components become important then despite the local productivity gains of using rapid development languages like python or Julia, it would overall be a wash if you're writing the system from scratch in a language like C.

cygnus_a 11y ago

Actually, even the subsection headings in bold give a very succinct summary: - Python has easy development (https://xkcd.com/353/ https://xkcd.com/353/) - Great libraries (ie, free matlab) - Cython for efficiency via C - The algorithms themselves determine speediness (ie numerical methods)

dagw 11y ago
The algorithms themselves determine speediness This is so important I wish people would focus more on it. I recently rewrote some Javascript code in (pure) python and got a good 2 orders of magnitude speed up on large inputs just by picking the right data structures and replacing an O(n^3) nested loop with an O(n log n) approach.
- timClicks 11y ago
  Which data structures did you use? In Python, I tend to rely on dict, list, and set for 90% or more of my code. I wouldn't want to rely on structures written in pure Python.
  - dagw 11y ago
    Nothing exotic. One of the changes for example was replacing a list of list with a set of tuples, which greatly sped up checking if an object was in the collection. Another change was using a generator comprehension and an included itertool function rather than hand rolled nested for loops.
    - echlebek 11y ago
      Once you've exhausted all the low-hanging fruit, like people calling .keys() on dicts, or doing unnecessary linear searches, Cython really starts to shine. I've seen it perform ~40 times better than pure Python in time-consuming loops. We do scientific computing at my company. Numpy does 90% of the work, but there are some algorithms that just aren't easily expressed with arrays. That's where Cython comes in.

anon4 11y ago

I agree with his conclusion that Python has great tools to help performance, but the GIL makes it impossible to effectively make use of separate cores, so I have to wonder just how badly the Java version was written. Properly written high-performance Java (store all data in contiguous arrays, do away with all the OO crap, reuse objects like crazy) should perform as fast, or faster than optimized Python, and you can scale it to as many threads as you like.

lqdc13 11y ago
Python has MPI4PY and Multiprocessing. Not sure why you need threads in the first place. Also, many matrix operations autoscale to all cores when compiled against Atlas. Finally, you can release the GIL with Cython: http://docs.cython.org/src/userguide/parallelism.html http://docs.cython.org/src/userguide/parallelism.html