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      Bare Metal Cloud: Key Advantages and Critical Use Cases to Gain a Competitive Edge

      Cloud environments today are part of the IT infrastructure of most enterprises due to all the benefits they provide, including flexibility, scalability, ease of use and pay-as-you-go consumption and billing.

      But not all cloud infrastructure is the same.

      In this multicloud world, finding the right fit between a workload and a cloud provider becomes a new challenge. Application components, such as web-based content serving platforms, real-time analytics engines, machine learning clusters and Real-Time Bidding (RTB) engines integrating dozens of partners, all require different features and may call for different providers. Enterprises are looking at application components and IT initiatives on a project by project basis, seeking the right provider for each use case. Easy cloud-to-cloud interconnectivity allows scalable applications to be distributed over infrastructure from multiple providers.

      Bare Metal cloud is a deployment model that provides unique and valuable advantages, especially compared to the popular virtualized/VM cloud models that are common with hyperscale providers. Let’s explore the benefits of the bare metal cloud model and highlight some use cases where it offers a distinctive edge.

      Advantages of the Bare Metal Cloud Model

      Both bare metal cloud and the VM-based hyperscale cloud model provide flexibility and scalability. They both allow for DevOps driven provisioning and the infrastructure-as-code approach. They both help with demand-based capacity management and a pay-as-you-go budget allocation.

      But bare metal cloud has unique advantages:

      Whether you need NVMe storage for high IOPS, a specific GPU model, or a unique RAM-to-CPU ratio or RAID level, bare metal is highly customizable. Your physical server can be built to the unique specifications required by your application.

      Dedicated Resources
      Bare Metal cloud enables high-performance computing, as no virtualization is used and there is no hypervisor overhead. All the compute cycles and resources are dedicated to the application.

      Tuned for Performance
      Bare metal hardware can be tuned for performance and features, be it disabling hyperthreading in the CPU or changing BIOS and IPMI configurations. In the 2018 report, Price-Performance Analysis: Bare Metal vs. Cloud Hosting, INAP Bare Metal was tested against IBM and Amazon AWS cloud offerings. In Hadoop cluster performance testing, INAP’s cluster completed the workload 6% faster than IBM Cloud’s Bare Metal cluster and 6% faster than AWS’s EC2 offering, and 3% faster than AWS’s EMR offering.

      Additional Security on Dedicated Machine Instances
      With a bare metal server, security measures, like full end-to-end encryption or Intel’s Trusted Execution and Open Attestation, can be easily integrated.

      Full Hardware Control
      Bare metal servers allow full control of the hardware environment. This is especially important when integrating SAN storage, specific firewalls and other unique appliances required by your applications.

      Cost Predictability
      Bare metal server instances are generally bundled with bandwidth. This eliminates the need to worry about bandwidth cost overages, which tend to cause significant variations in cloud consumption costs and are a major concern for many organizations. For example, the Price Performance Analysis report concluded that INAP’s Bare Metal machine configuration was 32 percent less expensive than the same configuration running on IBM Cloud. The report can be found for download here.

      Efficient Compute Resources
      Bare metal cloud offers more cost-effective compute resources when compared to the VM-based model for similar compute capacity in terms of cores, memory and storage.

      Bare Metal Cloud Workload Application Use Cases

      Given these benefits, a bare metal cloud provides a competitive advantage for many applications. Feedback from customers indicates it is critical for some use cases. Here is a long—but not exhaustive—list of use cases:

      • High-performance computing, where any overhead should be avoided, and hardware components are selected and tuned for maximum performance: e.g., computing clusters for silicon chip design.
      • AdTech and Fintech applications, especially where Real-Time Bidding (RTB) is involved and speedy access to user profiles and assets data is required.
      • Real-time analytics/recommendation engine clusters where specific hardware and storage is needed to support the real-time nature of the workloads.
      • Gaming applications where performance is needed either for raw compute or 3-D rendering. Hardware is commonly tuned for such applications.
      • Workloads where database access time is essential. In such cases, special hardware components are used, or high performance NVMe-based SAN arrays are integrated.
      • Security-oriented applications that leverage unique Intel/AMD CPU features: end-to-end encryption including memory, trust execution environments, etc.
      • Applications with high outbound bandwidth usage, especially collaboration applications based on real-time communications and webRTC platforms.
      • Cases where a dedicated compute environment is needed either by policy, due to business requirements or for compliance.
      • Most applications where compute resource usage is steady and continuous, the application is not dependent on PaaS services, the hardware footprint size is considerable, and cost is a limiting concern.

      Is Bare Metal Your Best Fit?

      Bare Metal cloud provides many benefits when compared to virtualization-based cloud offerings.

      Bare Metal allows for high performance computing with a highly customizable hardware resources that can be tuned up for maximum performance. It offers a dedicated compute environment with more control on the resources and more security in a cost-effective way.

      Bare metal cloud can be an attractive solution to consider for your next workload or application and it is a choice validated and proven by some of the largest enterprises with mission-critical applications.

      Interested in learning more about INAP Bare Metal?

      CHAT NOW

      Layachi Khodja


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      Use Cases for Linode GPU Instances

      Updated by Linode

      Written by Linode

      What are GPUs?

      GPUs (Graphical Processing Units) are specialized hardware originally created to manipulate computer graphics and image processing. GPUs are designed to process large blocks of data in parallel making them excellent for compute intensive tasks that require thousands of simultaneous threads. Because a GPU has significantly more logical cores than a standard CPU, it can perform computations that process large amounts of data in parallel, more efficiently. This means GPUs accelerate the large calculations that are required by big data, video encoding, AI, and machine learning.

      The Linode GPU Instance

      Linode GPU Instances include NVIDIA Quadro RTX 6000 GPU cards with Tensor, ray tracing (RT), and CUDA cores. Read more about the NVIDIA RTX 6000 here.

      Use Cases

      Machine Learning and AI

      Machine learning is a powerful approach to data science that uses large sets of data to build prediction algorithms. These prediction algorithms are commonly used in “recommendation” features on many popular music and video applications, online shops, and search engines. When you receive intelligent recommendations tailored to your own tastes, machine learning is often responsible. Other areas where you might find machine learning being used is in self-driving cars, process automation, security, marketing analytics, and health care.

      AI (Artificial Intelligence) is a broad concept that describes technology designed to behave intelligently and mimic the cognitive functions of humans, like learning, decision making, and speech recognition. AI uses large sets of data to learn and adapt in order to achieve a specific goal. GPUs provide the processing power needed for common AI and machine learning tasks like input data preprocessing and model building.

      Below is a list of common tools used for machine learning and AI that can be installed on a Linode GPU instance:

      • TensorFlow – a free, open-source, machine learning framework, and deep learning library. Tensorflow was originally developed by Google for internal use and later fully released to the public under the Apache License.

      • PyTorch – a machine learning library for Python that uses the popular GPU optimized Torch framework.

      • Apache Mahout – a scalable library of machine learning algorithms, and a distributed linear algebra framework designed to let mathematicians, statisticians, and data scientists quickly implement their own algorithms.

      Big Data

      Big data is a discipline that analyzes and extracts meaningful insights from large and complex data sets. These sets are so large and complex that they require specialized software and hardware to appropriately capture, manage, and process the data. When thinking of big data and whether or not the term applies to you, it often helps to visualize the “three Vs”:

      • Volume: Generally, if you are working with terabytes, exabytes, petabytes, or more amounts of information you are in the realm of big data.

      • Velocity: With Big Data, you’re using data that is being created, called, moved, and interacted with at a high velocity. One example is the real time data generated on social media platforms by its users.

      • Variety: Variety refers to the many different types of data formats with which you may need to interact. Photos, video, audio, and documents can all be written and saved in a number of different formats. It is important to consider the variety of data that you will collect in order to appropriately categorize it.

      GPUs can help give Big Data systems the additional computational capabilities they need for ideal performance. Below are a few examples of tools which you can use for your own big data solutions:

      • Hadoop – an Apache project that allows the creation of parallel processing applications on large data sets, distributed across networked nodes.

      • Apache Spark – a unified analytics engine for large-scale data processing designed with speed and ease of use in mind.

      • Apache Storm – a distributed computation system that processes streaming data in real time.

      Video Encoding

      Video Encoding is the process of taking a video file’s original source format and converting it to another format that is viewable on a different device or using a different tool. This resource intensive task can be greatly accelerated using the power of GPUs.

      • FFmpeg – a popular open-source multimedia manipulation framework that supports a large number of video formats.

      General Purpose Computing using CUDA

      CUDA (Compute Unified Device Architecture) is a parallel computing platform and API that allows you to interact more directly with the GPU for general purpose computing. In practice, this means that a developer can write code in C, C++, or many other supported languages utilizing their GPU to create their own tools and programs.

      If you’re interested in using CUDA on your GPU Linode, see the following resources:

      Graphics Processing

      One of the most traditional use cases for a GPU is graphics processing. Transforming a large set of pixels or vertices with a shader or simulating realistic lighting via ray tracing are massive parallel processing tasks. Ray tracing is a computationally intensive process that simulates lights in a scene and renders the reflections, refractions, shadows, and indirect lighting. It’s impossible to do on GPUs in real-time without hardware-based ray tracing acceleration. The Linode GPU Instances offers real-time ray tracing capabilities using a single GPU.

      New to the NVIDIA RTX 6000 are the following shading enhancements:

      • Mesh shading models for vertex, tessellation, and geometry stages in the graphics pipeline
      • Variable Rate Shading to dynamically control shading rate
      • Texture-Space Shading which utilizes a private memory held texture space
      • Multi-View Rendering allowing for rendering multiple views in a single pass.

      Where to Go from Here

      If you are ready to get started with Linode GPU, our Getting Started with Linode GPU Instances guide walks you through deploying a Linode GPU Instance and installing the GPU drivers so that you can best utilize the use cases you’ve read in this guide.

      To see the extensive array of Docker container applications available, check out NVIDIA’s site. Note: To access some of these projects you need an NGC account.

      Find answers, ask questions, and help others.

      This guide is published under a CC BY-ND 4.0 license.

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