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    Editor's Pick (1 - 4 of 8)
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    Technology Trends in High-Performance Computing

    Apac CIO Outlook | Tuesday, January 11, 2022
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    Customization of HPC offerings is becoming more common, and this extends beyond processing capabilities. Huawei provides three different HPC architectures to its customers, whereas IBM allows for data storage customization and HPE bills customers based on flexible consumption models.

    Fremont, CA: Technologies such as artificial intelligence (AI) and edge computing can enhance the capabilities of HPC and provide high-performing processing power to different sectors.

    Here are the key technology trends impacting the high-performance computing theme:

    Flexibility

    The processing diversity between old central processing units (CPUs), GPUs, ASICs, and field-programmable gate arrays (FPGAs) is increasing all the time. Workloads can vary greatly, so flexibility that provides different computing for different use cases is critical.

    Customization of HPC offerings is becoming more common, and this extends beyond processing capabilities. Huawei provides three different HPC architectures to its customers, whereas IBM allows for data storage customization and HPE bills customers based on flexible consumption models.

    Clients have the option of hosting their HPC data center on-premises, in the cloud, or at the edge. Some vendors provide a mix of solutions for various workloads in a single package.

    AI

    Maintaining peak performance necessitates running the data center at full capacity. Nonetheless, IT managers frequently leave a margin for error, a capacity protection gap, to ensure that activities are not disrupted. Over-provisioning is expensive and wastes computing space, processing power, and electricity.

    Datacenter administrators are increasingly concerned about running out of storage space, which is why an increasing number of data centers are implementing DCIM programs to detect idle processing, storage, and cooling capacity.

    DCIM enables data centers to run at full capacity while minimizing risk.

    In addition, AI can be used for security purposes, such as screening and analyzing incoming and outgoing data, detecting malware, as well as implementing behavioral analytics to protect data.

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