高性能Python

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• The main properties of interest in a computing unit are the number of operations it can do in one cycle and how many cycles it can do in one second. The first value is measured by its instructions per cycle (IPC),1 while the latter value is measured by its clock speed. These two measures are always competing with each other when new computing units are being made. ... Furthermore, while increasing clock speed almost immediately speeds up all programs running on that computational unit (because they are able to do more calculations per second), having a higher IPC can also drastically affect computing by changing the level of vectorization that is possible. Vectorization is when a CPU is provided with multiple pieces of data at a time and is able to operate on all of them at once. This s... (查看原文)

  我很严肃的 2015-03-12 10:48:07

• Simply adding more cores to a CPU does not always speed up a program’s execution time. This is because of something known as Amdahl’s law. Simply stated, Amdahl’s law says that if a program designed to run on multiple cores has some routines that must run on one core, this will be the bottleneck for the final speedup that can be achieved by allocating more cores. ... Furthermore, a major hurdle with utilizing multiple cores in Python is Python’s use of a global interpreter lock (GIL). The GIL makes sure that a Python process can only run one instruction at a time, regardless of the number of cores it is currently using. This means that even though some Python code has access to multiple cores at a time, only one core is running a Python instruction at any given time. ... While this may... (查看原文)

  我很严肃的 2015-03-12 11:16:29

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