Magical World of Parallel Computing

This is part-2 of the "EnchantingProgram" post, read this post first: http://magical-parallel-computing.blogspot.in/2017/04/a-simple-python-program-using.html So, let's see the actual reason of the speedup in the C and C++ programs. Lo and Behold, it is the effect of Branch Prediction ! Surprised? Well, at least my comments in the programs should have given you some direction!!! The if condition leads to a branch in the control flow. We know that branch predictions lead to pipeline flushes and create a delay in the piped execution scheme. Modern microprocessors utilize complex run-time systems for throughput, execution speed and memory efficiency. One example of such a technique is dynamic branch prediction. A long time ago, microprocessors used only a basic technique called static branch prediction, with two general rules: A forward branch is presumed to be not taken A backward branch is presumed to be taken Now, static branch p...

I would like to show you a very simple, yet subtle example on how programs can seem to produce unreasonable outputs. Recently, I was glancing through certain programs in Python, searching for places to optimize code and induce parallelism. I started thinking of threads immediately, and how independent contexts of computation can speed up code. Although I program frequently with Python, I hadn't been using any kind of explicit parallelism in my code. So, using my C knowledge, I went towards the Threading library of Python. Long story short, that was a mistake! Turns out that the Python implementation which is distributed by default (CPython) and Pypy, both have a process-wide mutex called the Global Interpreter Lock. This lock is necessary mainly because CPython's memory management is not thread-safe. The GIL locks up any kind of concurrent access to any objects in the Python run-time to prevent any race conditions or corruption of states. This is effectively a synchroniz...