GPUs are suitable for machine learning because of their parallel processing capacity and architectural features. Learn more about their performance here.
Graphics intensive applications like 3D modelling software, virtual desktop infrastructure are often run on GPUs rather than CPUs. This is a standard practice because GPUs better handle high-computational workloads from machine learning and deep learning landscapes. Therefore, it is essential to know what type of data your enterprise or business deals with. Having this knowledge helps determine the more suitable processing unit for your operations.
Machine Learning And GPU
Data contained in a structured or semi-structured form such as database tables, spreadsheets are analyzed well via machine learning algorithms. But, this particular use case may not be ideal for GPUs. CPU based computing effectively carries out structured data analysis using logistic regressions. In such a case, GPU is more a luxury than a requirement. It is best to understand the needs and use case of your data scientists' training and analysis methods.
However, if the data scientists in your enterprise are making use of deep neural networking for data analysis and training of unstructured data, then GPUs become a necessity for your IT infrastructure. In this case, data comprises of images, text, voice or videos.
Let's take a look at some of the current CPU and GPU features that determine their suitability for your business:
Latency vs Throughput
A CPU focusses on processing tasks in a serialized way while keeping latency as low as possible. Whereas, a GPU concentrated on throughput, also, pushing through as many operations as possible simultaneously. Therefore, parallel processing each task is a crucial feature for GPUs. Parallel processing is possible by a large number of cores that GPUs have in comparison to a general CPU.
Architecture
Architecture differences between CPU and GPU make all the difference for your data analysis. A GPU works with fewer, relatively small memory cache layers in comparison to a CPU. This is because it has more components dedicated to computation. Thus, GPU is less concerned with how long it takes to retrieve data from memory as long as GPU has enough computations at hand; the potential memory access “latency” is masked.
Relatively, a GPU device can house much more of cores than CPUs, allowing them to process tasks parallelly.
High-Performing GPUs With VEXXHOST
If your enterprise delves deep into deep learning techniques, then GPUs are meant for you. The training datasets used with neural network models differ in structure from that of more traditional tabular data. Graphics, voice, or text data, often have higher dimensionality. GPUs can accelerate the training phase for such nontabular data by order of magnitude, which is a significant benefit to the data scientist, who can then do more experiments with models in any one period of time.
We at VEXXHOST bring to you the option of NVIDIA accelerators for our enterprise-grade GPUs with our OpenStack based Private Cloud service. Deploy a fully-equipped cloud with us to achieve business success in machine learning and its branches.
