![]() ATLAS has been around for a while, and works well in most circumstances. It's one of the fastest libraries around, but if you're looking to squeeze the last drop of performance out of your code, I'd recommend looking into detailed benchmarks for each library. ATLAS stands for Automatically Tuned Linear Algebra Software.I can't go into detail, but here's a quick rundown of what everything is: Now, that we understand how it works, let's look under the hood, for how numpy works: np.config.show()Įxtra_link_args = Įxtra_compile_args = Even a band-aid fix works in a pinch: a = ĬPU times: user 107 ms, sys: 1.28 ms, total: 109 ms Already it's worth noting that any operations you do would be better served on floating point, even without other optimizations. ![]() ![]() It's integers, within a much smaller range than that of real numbers, and yet: CPU times: user 924 ms, sys: 7.74 ms, total: 931 msĪlmost ten times the cost. However, guess how long the following piece of code takes: a = Keep in mind that this is still unoptimized. Here's an example: import numpy as npĪ = ī = Another part of this is the floating point unit found on most general-purpose CPUs, which optimizes floating point operations to the point of insanity. Part of this power comes from the SIMD module, which can perform parallel operations on data sets while using the same instruction. They have become so commonplace that most modern CPUs (forget GPUs for now) have dedicated hardware components that speed up matrix operations. In my particular case, the slowest operations I were doing were matrix operations, which lend themselves well to parallel implementations. Speeding up Numpy on a CPU level requires understand how this speedup is obtained. Eventually I did, and here's a small compilation of the steps it took to achieve this speedup, and the many options available to do so. And while there were a number of guides that would yield results when used together, I found myself lacking the depth of knowledge in machine-level libraries needed to compile, link and use them. There are a wealth of articles and guides online about how to do this, but working from a Mac meant that only 5% of these were useful to me. It was here that I started looking for ways to improve performance. The very first run, using batch gradient descent on a thousand runs took 1 day and 18 hours. ![]() It wasn't until I started working on the MNIST Database that I realised it was painfully slow. For this reason, my first implementation of a perceptron was done in Numpy. When running basic matrix operations in Python, numpy is the most versatile and easily accessible library out there. Part 1 - Looking at CPU Speedup §Background ![]()
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