Convert a FOR loop to parallel processes

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Discussion Overview

The discussion revolves around converting a Python for loop into a parallel processing approach to improve performance, particularly in the context of computational tasks that take a long time to execute. Participants explore various methods, including vectorization, multithreading, and the use of alternative programming languages like C++ and Julia.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant presents a basic for loop in Python and seeks guidance on converting it to a parallel processing model.
  • Another participant suggests that using broadcasting in NumPy could significantly speed up the code, potentially by a factor of 100, and emphasizes the advantages of vectorization.
  • Some participants discuss the challenges of parallel processing in Python, noting that it may not be straightforward and suggesting alternatives like Cython or Numba for performance improvements.
  • There is mention of distributed computing schemes, with one participant referencing Apache Spark as an example.
  • Several participants express interest in the performance differences between Python and C++, with one reporting a significant speedup when running the same code in C++ compared to Python.
  • One participant raises a question about the efficiency of multithreading, suggesting that it may only be effective when threads have I/O operations to perform.
  • Another participant counters that modern processors with multiple cores can benefit from parallelizing operations across threads, regardless of I/O.

Areas of Agreement / Disagreement

Participants express a range of views on the best approach to improve performance, with some advocating for vectorization and others for multithreading or switching to C++. There is no consensus on a single best method, and the discussion includes both support for and skepticism about various techniques.

Contextual Notes

Some participants highlight the need for familiarity with vectorization techniques and the potential complexity involved in implementing them effectively. The discussion also touches on the limitations of Python's performance compared to compiled languages like C++.

Who May Find This Useful

This discussion may be useful for programmers and researchers looking to optimize computational tasks in Python, those interested in parallel processing techniques, and individuals considering switching to more performant programming languages for numerical computations.

  • #31
willem2 said:
The cpu can have dozens of instructions waiting for other instructions or memory in the pipeline at the same time. The cpu will have no problems starting a multiply, a subtraction, a load (2 loads for the newest types) and a store in one clock cycle, even if these belong to different iterations of the loop.
EngWiPy said:
OK, I see. But we have no control over this. I mean this is a hardware design architecture how the CPU executes different instructions, because they are done at different units. But what if you are executing the same function on independent data, but using the same instructions?
We have some control over this, which is what optimizations using loop unrolling or loop unwinding are about.
The processor executes instructions in one or more pipelines, and tries to guess what the next instruction will be. If it guesses correctly, everything is fine, since that instruction is in the pipeline. If it guesses wrong, it has to flush the pipeline, which takes several clock cycles to refill.

Loops such as for or while loops can be problematic, as are branches such as if and if ... else.

Here's a simple example from this wiki article: https://en.wikipedia.org/wiki/Loop_unrolling
C:
int x;
for (x = 0; x < 100; x++)
{
     delete(x);
}

The same loop, after unrolling:
C:
int x;
for (x = 0; x < 100; x += 5 )
{
     delete(x);
     delete(x + 1);
     delete(x + 2);
     delete(x + 3);
     delete(x + 4);
}
 
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