Overview

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Ingredients

SpiNNaker is just one example of the myriad of ways that scientists have tried to tie computing and human intelligence together. SpiNNaker is a supercomputer, activated in 2018, with a million processing cores that are connected in a way that mimics the brain. However, even with all this processing power, SpiNNaker can only simulate about 1% of a human brain.


Supercomputers, like SpiNNaker, are just one form of High-Performance Computing (HPC) system. Researchers have not yet been able to create HPC systems with the efficiency of the human brain. However, these systems are still incredibly powerful, particularly when combined with Artificial Intelligence (AI). In this article, you’ll learn what HPC is and how it can be used with AI.

Step-by-step

  1. What Is HPC?

    HPC is computing with aggregated processing power. HPC systems are clusters of computers (nodes) that work together on one distributed workload with parallel processing instead of sequential. You can use HPC to solve complex issues and perform analyses that could not be accomplished with traditional workstations. 

    Traditional computers have only one Central Processing Unit (CPU) with up to 18 cores. Cores are the processors in a CPU. HPC systems typically have two or more CPUs per node, and between 16 to 64 nodes. Each node also has its own memory and storage, adding to the amount of data that can be processed.

    Many HPC systems make use of Graphics Processing Units (GPUs). GPUs are used as co-processors and work in tandem with CPUs. The use of GPUs and CPUs together is called hybrid computing. GPUs are like CPUs but they have processors designed for a single task, rather than being multipurpose. This specialization enables GPUs to accelerate portions of an application and further speed data processing.

    Benefits of HPC include:

    • Speed — more numerous and powerful processors take less time to run experiments or process data sets.
    • Volume — increased memory and storage enables you to process larger amounts of data and run longer analyses.
    • Efficiency — you can distribute pooled resources across various workloads, enabling near 100% active use.
    • Cost — bulk purchasing and cloud availability can reduce costs. Faster speed and greater efficiency produce increased productivity and greater ROI.

     

    HPC systems were designed to allow organizations with fewer resources access to a semblance of the computing power that can be accomplished with supercomputers. Supercomputers can include tens of thousands of processors and cost upwards of $20 million. These computers are only feasible for the largest organizations and are inaccessible to standard enterprise users.

    HPC systems are typically used by:

    • Research labs — to find cures for diseases, interpret astronomical data, predict and track weather patterns, or train AI algorithms.
    • Media and entertainment — to edit films, render special effects, or live stream events.
    • Oil and gas companies — to identify oil and gas sources and increase the productivity of existing wells.
    • Financial services — to track stock trends, automate trading, or identify fraud.

     

    Currently, Linux is primarily used for HPC although it can also be achieved with Windows systems. There are also HPC systems being integrated with cloud infrastructures. All three cloud vendors support HPC and include services for orchestrating and managing workloads. For example, Azure provides an integration for the migration of HPC workloads.

  2. How Can HPC and AI Work Together?

    AI requires enormous amounts of data and real-time processing. This sort of workload is what HPC was designed for. It should be no surprise then that AI and HPC can be used efficiently together.

    Both AI and HPC systems require large volumes of storage, computing power, and high-speed interconnections. Both also require orchestration to manage; HPC often employs Slurm while AI often employs containerization and Kubernetes. Additionally, combining HPC and AI requires a few integrations. You need communication libraries that can distribute workloads and aggregate results, like Open MPI and distributed TensorFlow.

    Benefits of combining AI and HPC include:

    • Higher quality simulations — can be created due to greater stability/accuracy
    • Larger/finer models — can be created from lower precision data requirements
    • Easier to scan code — due to reduced code optimization requirements
    • More money towards research — and less towards maintenance costs over time
    • Bigger and longer simulation — at a lower cost due to increased speed

     

    Current Applications of AI and HPC

    Currently, researchers are using AI for automation of big data analysis and visualization tasks. You can use AI to process data more efficiently and transform it into a more human accessible format. You can also use AI in simulations instead of computationally intensive models. Combined with HPC, you can further reduce processing time

    With HPC, realistic, highly-detailed datasets can be generated in a functional amount of time. You can then use these datasets in an AI training feedback loop to refine data interpretations. This makes it possible to train on problems that previously would take too long to process. It also reduces the time between learning cycles and the time cost of failed experiments. 

    Another application is processing Internet of Things (IoT) device data. A huge influx of data comes from IoT devices that require automatic processing to be useful. You can use AI to summarize data and provide initial processing which you can then refine manually. You can combine with HPC to process and analyze streams in real-time. This enables faster detection and response from IoT devices.

  3. Considerations for the Future

    The drive for increasingly accurate and fast AI tools makes HPC indispensable. At the same time, AI can be used to refine and enhance HPC processes. This mutual benefit seems to indicate that AI and HPC are unlikely to part ways any time soon. 

    In the future, speed will continue to be a key factor. The amount of data to be ingested and processed will likely continue to increase, requiring faster systems for timely analysis. The complexity of analysis should also increase as models become more detailed and exact. This will require even more processing power. HPC is a good candidate to fill this need until quantum computing is fully realized.

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