Artificial Intelligence and the Growing Need for HPC
The recent development in deep learning is shaping up to be a game changer for artificial intelligence. Neural networks have revolutionized the way we make computers learn and model behaviors, especially for tasks such as speech and image recognition that have been a challenge to tackle with traditional programming paradigms. However, despite their current hype, neural networks as we know them are an old innovation dating back to the 1960’s. Their recent thrust into the limelight has only been as a consequence of the continued development of high-performance computing, or HPC.
What is HPC (High-Performance Computing)?
High-performance computing is the clustering of computers that allows them to execute a task in parallel, radically cutting down execution times. Over the years, innovations in CPU and GPU hardware coupled with breakthroughs in distributed software have allowed HPC to finally make deep learning a feasible endeavor. Simple neural networks that were once impossible to train can now be trained in hours and minutes. This has also elevated HPC, alongside machine learning and data analytics, as an essential asset for modern businesses.
That said, deep learning is still a significant computational challenge, even for modern multi-threaded, multi-core CPUs. Deep learning tends to be a very data hungry practice, with the general rule of thumb being that the larger the training dataset, the better the performance of the resulting model. Moreover, with data types such as sound, images, and text, the dimensionality of the training data also becomes a hurdle. What really makes today’s large-scale deep learning models feasible to train is the use of HPC clusters yielding graphical processing units or GPUs.
Benefits of GPU over CPU
In contrast to a traditional CPU, a modern GPU can contain thousands of processing cores that allows it to execute simple tasks in parallel at much higher speeds. This architecture suits neural networks wonderfully because, in their essence, they are simply a sequence of matrix computations. These computations are quite easy to parallelize, allowing GPUs to execute them up to 50 times faster than traditional CPUs. Moreover, as the complexity of the neural networks and dimensionality of the training data increases, the advantages of using GPUs is compounded, reducing training times from months and weeks to hours and minutes. Training deep learning models also tends to be quite an empirical process, requiring multiple training cycles with different hyperparameters and gauging the ideal set that produces the highest accuracy. Being able to train models in a feasible time can radically improve the performance of the model by giving the user the luxury of experimenting with a large set of hyperparameters and network configurations.
(CPUs photographed above)
Why You Need HPC
As artificial intelligence and data analytics become an important asset for modern businesses and companies to gain an advantage over the competitors, so does the need for in-house HPC clusters to execute these data-intensive tasks. While cloud-based models are excellent for prototyping, having your own HPC GPU cluster allows you unprecedented control over processing and modeling your data. It also tends to be more cost-effective in the long term, especially if continued development of your artificial intelligence tools is a necessity for your business. In some cases, a local HPC cluster is a necessity when the data being manipulated can include your customer’s personal information, as it allows you to keep a strict check on its accessibility.
GPU Solutions — Turnkey Setups from Exxact
As data-driven computing comes into the mainstream, the need for high-performance computing and its continued development becomes ever larger. As we move towards a more connected future with the Internet of Things and Smart cities starting to lay their foundations, data will become a more abundant resource. More of our future systems, such as autonomous cars, smart city infrastructure and its management and energy grids will require the timely and precise processing of data to function effectively. HPC is an essential ingredient in making this vision possible, making it feasible for us to leverage deep learning and artificial intelligence in a data-driven future.
Not sure where to start? The Exxact Deep Learning Development Box is another of our deep learning research signature offerings, powered by state-of-the-art NVIDIA GPU processing power.
Or give our full lists of deep learning GPU solutions and of deep learning software a look–all of our GPU solutions are fully turnkey so you can get going quick.
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