Parallel Computing — Upgrade Your Data Science with a GPU

Parallel Computing — Upgrade Your Data Science with a GPU

Are GPUs faster than CPUs? It’s a very loaded question, but the short answer is no, not always. In fact, for most general purpose computing, a CPU performs much better than a GPU.

Are GPUs faster than CPUs? It’s a very loaded question, but the short answer is no, not always. In fact, for most general purpose computing, a CPU performs much better than a GPU. That’s because CPUs are designed with fewer processor cores that have higher clock speeds than the ones found on GPUs, allowing them to complete series of tasks very quickly. GPUs, on the other hand, have much greater number of cores and are designed for a different purpose. At inception, GPU was originally designed to accelerate the performance of graphics rendering. It did so by allowing the CPU to offload burdensome calculations and free up processing power. GPUs render images more quickly than a CPU because of its parallel processing architecture, which allows it to perform multiple calculations across streams of data simultaneously. The CPU is the brain of the operation, responsible for giving instructions to the rest of the system, including the GPU(s). Today, with the help of additional software, GPUs’ capabilities have expanded to significantly reduce the time it takes to complete certain types of computations required at the different stages of Data Science. It’s important to highlight that GPUs do not replace CPUs but rather act as a co-processors to accelerate scientific and engineering computing.

Comparison between CPU and GPU processing:

Image for post

GPU vs. CPU

*If you’d like to see a fun illustration, here’s a [video_](https://www.youtube.com/watch?time_continue=93&v=-P28LKWTzrI&feature=emb_title) from 2009_

GPU Parallel Processing for Data Science

GPUs offer significant speed boost at a time when CPU performance increase has slowed down over the past few years (and sadly breaking Moore’s Law). Thus, it’s expected that the adoption to GPU computing will increase in the coming years. This feature is important in the field of Data Science, which involves processing very large datasets (in the form of matrices/vectors) efficiently. The SIMD design, or Single Instruction/Multiple Data, means that GPU computing can process multiple data with a single instruction, as is the case for matrix multiplication. As an example, Deep Learning can take advantage of parallel computing to reduce time spent in the training cycle since many of the convolution operations are repetitive. In fact, many of the steps in the Data Science workflow can be processed through the GPU (e.g. data preprocessing, feature engineering, and visualization), but they do require additional functions to implement. Since traditional computer programming isn’t written for parallel processing, they need to be converted to be GPU-enabled. GPU manufacturers are very keen on providing software support for developers to accelerate adoption.

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