Is Deep Learning the Next Big Challenge for High Performance Computing?

Today, artificial intelligence (AI) is not just a buzzword but a reality that is changing the world. From online shopping suggestions to voice-enabled assistants — AI-based solutions are all around us. And the computing industry is working towards inventing more efficient computers that can run complex algorithms to support tasks performed by AI.

Deep learning is currently the most effective AI technology, as it allows computers to learn in a more human-like manner. High volumes of data are used to train deep neural networks, with each layer of the network learning a different data feature. The main challenge so far has been the calculation cost: vast volumes of data and computations needed to run the algorithms.

High performance computing (HPC) systems are the natural choice for deep learning, as large-scale HPC clusters can handle huge workloads. However, there are still a few unanswered questions. For instance, many people wonder whether the HPC market is ready for this new challenge and the steep increase in memory and storage capacity demand. Continue reading to learn more about the state of deep learning in HPC and how it is used!

Deep Learning Is Revolutionizing AI

Since its conception, deep learning has been the most promising application of neural networks. What is it? Simply put, it is a machine learning method where researchers use a set of algorithms to allow the machine to “learn” through interactions with data.

Deep learning is founded on the idea of learning multiple levels of representation, starting from simple features and combining them to form more complex ones. It is so powerful because it can create a hierarchical representation of the input and learn features that are useful for classification or clustering in a way similar to that of humans. This kind of learning is often unsupervised when the algorithm learns patterns from untagged data.

Deep learning is used in various applications, such as:

  • Natural language processing — Algorithms used in this way can do tasks such as recognizing the topic of a document or the sentiment expressed in a piece of text.
  • Image recognition — Deep neural networks are used to recognize objects within images, allowing computers to “see” and understand the world around them.
  • Speech recognition — Trained algorithms excel at speech recognition. As a result, many deep learning algorithms have replaced speech recognition software based on hidden Markov models.
  • Video analysis — Deep learning is becoming increasingly important in the video industry because it allows computers to automatically identify, tag, and organize video clips.
  • Machine translation — Deep learning has been used in Google Translate to improve translation quality significantly.
  • Drug discovery — Trained algorithms can speed up finding new drugs by identifying promising molecules from vast amounts of experimental data.
  • Recommendation systems — Deep learning algorithms can provide better recommendations for products and services than traditional recommendation systems.
  • Fraud detection — The use of deep learning allows banks to detect fraudulent transactions based on complex patterns.
  • Cancer diagnosis — Algorithms can be used to analyze large amounts of medical data, such as images, to detect cancerous cells or tumors in patients.

The most significant advantage of applying deep learning is that it becomes easier to design novel solutions to problems that have not been addressed before. Algorithms might even come up with answers that have not been explicitly programmed and thus can match human creativity.

The main challenge with deep learning is that unlike traditional machine learning, which uses labeled data, unlabelled data is needed to train these deep neural networks. The amount of unlabelled data required to train deep neural networks is enormous, as this approach requires thousands of hours of training to work well. The good news is that high performance computing clusters can handle this kind of workload. HPC clusters are also well suited for running deep learning algorithms because they can take full advantage of their parallelism and high memory capacities.

Can Deep Learning Reach Its Full Potential?

Deep learning is computationally intensive and requires lots of memory, which usually is challenging for computing systems — HPC included. HPC clusters typically use many-core processors, which efficiently handle parallel workloads and memory-intensive applications. However, many-core architectures often end up to be not powerful enough to handle these huge workloads efficiently.

An enormous amount of computational power required to run deep neural networks and calculate large volumes of data is the next big challenge for HPC. To make deep learning more efficient on HPC clusters, we need to improve both hardware technologies and software algorithms.

Deep learning requires high performance interconnect technologies capable of delivering high bandwidth between the nodes within the cluster while maintaining low latency and high utilization rates. New inventions are needed to deal with current limitations, including creating more learning-ready hardware that can handle high amounts of calculations per second.

To Sum Up

Deep learning is changing how AI systems are trained and how they interact with humans. It is also changing the computing industry, as more and more companies opt to purchase high performance computers to run and train their own algorithms.

Overall, deep learning models are already revolutionizing many applications and will lead to even more innovations in the near future. However, HPC systems need to be improved and adapted to meet the increasing demand for these workloads from commercial and academic users. It is up to the computer industry to deliver more powerful systems able to run deep learning algorithms while keeping costs competitive.

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