Convert a FOR loop to parallel processes

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SUMMARY

This discussion focuses on converting a Python for loop into a parallel processing approach to enhance performance. The original code snippet utilizes a for loop to apply a function to independent values in a list, which can be optimized using Numpy's broadcasting or vectorization techniques. Participants emphasize the significant speed improvements achievable through C++ implementations, with one user reporting a 30x speed increase compared to Python. The conversation also highlights the importance of leveraging modern multi-core CPUs for parallel processing.

PREREQUISITES
  • Familiarity with Python programming and its syntax.
  • Understanding of Numpy for numerical operations and broadcasting.
  • Knowledge of parallel processing concepts, particularly in Python.
  • Basic understanding of C++ and its performance advantages over Python.
NEXT STEPS
  • Learn Numpy broadcasting techniques to optimize numerical computations.
  • Explore Python's multiprocessing and threading modules for parallel execution.
  • Investigate C++11 or C++14 features for efficient multi-threading.
  • Study vectorization techniques in MATLAB and their application in Python.
USEFUL FOR

Python developers, data scientists, and anyone looking to enhance the performance of numerical computations through parallel processing and optimization techniques.

  • #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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