AI infrastructure doesn’t have to be complicated or overwhelming Sponsored Content by DDN
Artificial Intelligence (AI) and Deep Learning (DL) are no longer the exclusive realm of high performance computing (HPC) research used by government and universities. A wide range of industries are now using AI and DL to extract value from data and aid in business decisions. AI is being used to uncover significant breakthroughs in areas such as medical diagnostics, locating financial fraud, autonomous vehicles and speech recognition for a number of market applications. However, AI and DL pose exceptional challenges and put significant strain on compute, storage and network resources. An AI-enabled datacenter must concurrently and efficiently handle activities involved in DL workflows, including data ingest, data curation, training, inference, validation and simulation. Thus, the storage and management of data has become a critical component of today’s data centers.
Optimizations for GPU computing and workloads
Many AI and DL workloads run best on Graphical Processing Units (GPUs), but a switch to GPU based processing also requires optimization across filesystems and storage. An organization architecting for sustained AI success should look for storage solutions that are turn-key, pre-configured and provide scale-out capabilities for capacity and performance—which will ensure data is in the right place at the right time. AI storage infrastructure must be architected for all types of I/O patterns and data layouts handling any thread count, the toughest IO patterns, and dynamic data placement. In addition, they must be container aware, deliver direct GPU integration, multi-rail networking, and work with accelerated protocols for AI frameworks. These capabilities will ensure that the GPU compute engines will remain saturated and delivering full AI application acceleration.
Automated data management
AI Storage solutions should deliver native data integrity and management features. Administrators need comprehensive data residency controls using policy-based placement to ensure the optimal mix of performance and cost, with the ability to track and move data between filesystems and object stores. These systems should support the migration of data to S3 for archive or data transfer. Users must be able to spin up and access filesystems on demand from S3, NFS and SMB interfaces at high performance computing speed which helps in reducing long term cloud costs and increases access points for applications. In addition, the storage system should intelligently move data between high-performance flash and large capacity disk to ensure efficient utilization of appropriate storage tiers.
Advanced security for a distributed at scale world
The nature of data involved in AI often requires advanced security and data safety in a distributed at-scale world. Native comprehensive security measures are a must both in the cloud (public, hybrid or private cloud) or in on-premise datacenters. Security features guarantee that rogue clients, virtual machines (VMs) and containers cannot gain root access on the filesystem, minimizing the risk of both external and internal bad actors. Secure audit logging as well as Shared Key or Kerberized authorization of clients and containers provide additional security to ensure only those users with the correct access rights can get to the data.
Enhanced multi-tenancy and container access and security
Storage systems with intelligent client software that enables file-level access to the shared storage system, and a direct connection to containers will also yield security benefits. Compartmentalized access to data at the system or container level further ensures data is only available to the right applications and users. Customers sharing space in the cloud with other customers or users need to be certain their storage enables multi-tenancy security options which export only specific datasets to users for customers that require trusted levels of segregation for their data.
Experience in Dealing with the Largest Data Challenges
Buyers should strongly consider infrastructure providers who have long experience in dealing with data at-scale challenges. Ensuring the stability and scalability of any project over time is the key to AI success. A supplier with experience in analytics and high performance computing will offer tested solutions that have been proven in the largest compute environments imaginable.
DDN Solutions: Ideal for AI workloads
DDN is the world’s largest privately held data storage company and the leading supplier to data-intensive, global organizations. DDN provides a new generation of innovative storage solutions to meet AI and DL application requirements while taking maximum advantage of new storage media and increased media capacities.
DDN successfully handles massive data loads across all areas of AI and DL with their next-generation parallel storage platforms. DDN delivers a true global data platform enabling and accelerating a wide-range of data-intensive workflows, at any scale. Their solutions provide powerful integrations for AI and HPC ecosystems through simple implementation and scaling models along with visibility into workflows and powerful global management features.
“Developing HPC and AI data management systems is very difficult. Safeguarding valuable data whilst consistently performing needs exposure to all the unnatural things that happen at exascale,” said James Coomer, senior vice president of product, DDN. “DDN’s team of 500 developers, field and support engineers possess the DNA to tame these harsh environments and have forged the best file systems for the job. DDN EXA5 is the culmination of DDN’s 20-year heritage of outperforming everything on the market and doing so with unmatched capability.”
The DDN shared parallel architecture
DDN solutions are fully-optimized to deliver data with high-throughput, low-latency and massive concurrency using a shared parallel architecture that can scale flexibly for most technical and economic benefits. Building on over a decade of experience deploying parallel filesystem solutions in the most demanding environments around the world, DDN delivers unparalleled performance, capability and flexibility for users looking to manage and gain insights from massive amounts of data. DDN solutions provide flexible access via a parallel client to various filesystems, interfaces and protocols including the Network File System (NFS), Hadoop Distributed File System (HDFS), Portable Operating System Interface (POSIX), Server Message Block (SMB) protocol and Amazon Simple Storage Service (S3) service offered by Amazon Web Services (AWS) that provides object storage through a web service interface. DDN storage allows data to be stored via solid state drives (SSDs), hard disk drives (HDDs), tape, S3 or NVMe (non-volatile memory express).
Artificial Intelligence (AI) and Deep Learning (DL) are increasingly being used by a wide range of organizations to gain insights into their data. However, the massive amounts of data that must be stored requires an optimized and flexible storage solution capable of efficiently managing AI and DL data.
DDN, a global leader in AI and DL multicloud data management, recently received multiple awards at the 2019 International Conference for High Performance Computing, Networking, Storage Analysis (SC) conference.
“The complexity of data required for AI projects does not necessarily mean unreasonable complexity for AI infrastructure,” said Kurt Kuckein, vice president of marketing at DDN, “By selecting the right storage, that is complementary to and optimized for AI computational demands, business can achieve scalable and successful AI with relative simplicity.”
Automating Storage Tiers Can Drive Faster, Deeper Analytical Insight, https://www.ddn.com/download/automating-storage-tiers-can-drive-faster-deeper-analytical-insight/
DDN is the world’s leading data management supplier to data-intensive, global organizations. The rapidly evolving competitive landscape makes it essential to ensure projects like AI initiatives can move quickly from investigation to production. For more than 20 years, DDN has focused on designing, deploying and optimizing solutions for production level AI, HPC and Big Data. DDN enables businesses to generate more value and accelerate time to insight from their data, on-premise and in multicloud environments. Organizations leverage the power of DDN technology and technical expertise to capture, store, process, analyze, collaborate and distribute information and content in the most efficient, reliable and cost-effective manner. DDN customers include many of the world’s leading financial services firms, banks, healthcare and life science organizations, manufacturing and energy companies, government and research facilities, and service providers who use their data to develop everything from innovative treatments for disease to new paths to revenue.
DDN has long been a partner of choice for organizations pursuing data-intensive projects at any scale. DDN provides significant technical expertise through its global research and development and field technical organizations. A worldwide team with hundreds of engineers and technical experts can be called upon to optimize every phase of a project: initial inception, solution architecture, systems deployment, customer support and future scaling needs. DDN laboratories are also equipped with leading GPU compute platforms to provide unique benchmarking and testing capabilities for AI and DL applications.
Strong customer focus coupled with technical excellence and deep field experience ensures that DDN delivers the best possible solution for any challenge. Taking a consultative approach, DDN experts perform an in-depth evaluation of requirements and provide application-level optimization of data workflows for a project. They will then design and propose an optimized, highly reliable and easy-to-use solution that accelerates the customer’s effort.
Contact DDN today and engage our team of experts to unleash the power of your AI projects.