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Events

Join Us at Identiverse for Identities for Everything

Photo courtesy of Dan Vogel If you're in the Washington, D.C. area on Thursday, June 27, and attending Identiverse, swing by. Dan Vogel and Norka Lucena from the Oracle Cloud Infrastructure team are presenting Identities for Everything at 2:35 in the Ballroom at the Washington Hilton. As a growing cloud service provider, we faced a problem when building our robust multitenancy identity system. The implementation for authorizing advanced services like compute and DBaaS instances was safe but could be complicated for customers to easily reason about. We solved this problem by creating a new type of principal actor, called resource principals, that abstracts both physical and logical resources, and self-identifies when communicating with infrastructure services. Resource principals are a novel way to distribute trust at scale. We have found four patterns of resource principals that can be mixed to define all of our cloud resources to date: Infrastructure—using physical identifiers (for example, compute instances) Ephemeral—using injected identifiers (for example, Kubernetes ReplicaSets) Stacked—projecting one principal into another (for example, a managed cache) Asserted—collective resources reduced into an individual (for example, object storage) By assigning Identities to everything in our infrastructure, we reduce the scope and number of distributed credentials, better capture customer intention of infrastructure interaction, and produce more precise and actionable audit logs. Dan and Norka will show how it works by building a simple cloud app and applying these patterns to result in a clear authentication and authorization story. We hope that by sharing these patterns we can improve identity solutions of other cloud products by embracing the idea of safe, secure identities for everything. If you can't attend our session but still want to learn more about how to use Resource Principals with Oracle Cloud, check out this white paper on best practices for IAM on Oracle Cloud Infrastructure.

Photo courtesy of Dan Vogel If you're in the Washington, D.C. area on Thursday, June 27, and attending Identiverse, swing by. Dan Vogel and Norka Lucena from the Oracle Cloud Infrastructure team...

Partners

Machine Learning with H2O.ai and Oracle ERP

Packaged line-of-business (LOB) applications are an area where Oracle is a market leader. These applications contain an enormous amount of data that has the potential to give amazing insight into core business functions, enabling gains in areas such as: Operational efficiency Cross sell / up sell Customer experience Oracle is investing heavily in moving these LOB applications to our cloud, Oracle Cloud Infrastructure (OCI). In parallel, we're investing in the ecosystem, partnering with key ISVs to enable their workloads on our cloud, which allows our customers to leverage the latest innovations in conjunction with the LOB applications that they depend on. H2O Driverless AI H2O.ai is a leader in the AI/ML space. Their platform, Driverless AI (DAI), automates much of the machine learning lifecycle, from data ingestion to data engineering and modeling, on to deployment. It enables both data scientists and relatively naive users to generate sophisticated ML models that can have an enormous impact on their business. Late last year, we began integration work with H2O.ai. Initial efforts focused on creating Terraform templates to automate the deployment of Driverless AI on Oracle Cloud Infrastructure. Building on that, H2O.ai was the first of our Quick Start templates to go live. Driverless AI can be deployed today on Oracle Cloud Infrastructure by using Terraform modules that our team and the H2O.ai team developed jointly. Those modules are available on the Quick Start on GitHub. We’re currently exploring ways to use this kind of data in Oracle enterprise applications to build tailored ML models. An example architecture might look like this: On Oracle Cloud Infrastructure, Driverless AI can be deployed on NVIDIA GPU machines. This accelerates the building of models, further reducing the end-to-end lifecycle for machine learning. Oracle Retail Advanced Inventory Planning The Oracle Retail Advanced Inventory Planning (AIP) module in Oracle ERP is one potential source of interesting data for ML with H2O.ai. An external merchandising system, forecasting system, and replenishment optimization system are integrated with AIP to provide the inventory/foundation data and the forecasting data to AIP to effectively plan the inventory flow across the retailer's supply chain.  Because AIP can integrate with any forecasting system, Driverless AI could be used to build a model that accounts for both high frequency (for example, weekend) and lower frequency (for example, holiday) seasonalities. Driverless AI ships with a time-series recipe based on causal splits (moving windows), lag features, interactions thereof, and the automatic detection of time grouping columns (such as Store and Dept for a dataset with weekly sales for each store and department). Oracle Retail Merchandising System The Oracle Retail Merchandising System (RMS) module in Oracle ERP is another fascinating touchpoint. This module includes the following information: Expenses: The direct and indirect costs incurred in moving a purchased item from the supplier's warehouse/factory to the purchase order receiving location. Inventory Transfers: An organized framework for monitoring the movement of stock. Return to Vendor (RTV): Transactions that are used to send merchandise back to a vendor. Inventory Adjustments: Increase or decrease inventory to account for events that occur outside the normal course of business (for example, receipts, sales, and stock counts). Purchase Order Receipts (Shipments): Record the increment to on-hand when goods are received from a supplier. Stock Counts: Inventory is counted in the store and compared against the system inventory level for discrepancies.   RMS contains a rich dataset that could be used to build models in Driverless AI for anomaly detection around RTV, inventory adjustment, and other events. Oracle Retail Price Management The Oracle Retail Price Management module in Oracle ERP includes the following information: Item ID: ID that is assigned when the price event is created at the transaction item level. Cost Change Date: The effective date of the past or future cost change. Retail Change Date: The effective date of the past or future retail change. Cost: The cost on the effective date of the cost or retail change. Retail: The regular selling retail on the effective date of the cost or retail change. Markup %: The markup percent on the effective date of the cost or retail change. The markup percent is calculated using the calculation method specified by your system options.   With Driverless AI, we could use past cost changes to train a regression model. That model could suggest future pricing, automatically incorporating both seasonality and product lifecycle. Also, by combining Retail Price Management data with marketing, clickstream, or other end-customer data, a regression model could be built to predict the benefit of pricing changes while accounting for other variables that affect sales. Oracle Retail Trade Management The Oracle Retail Trade Management module in Oracle ERP includes the following information: Landed Cost: The total cost of an item received from a vendor inclusive of the supplier cost and all costs associated with moving the item from the supplier's warehouse or factory to the purchase order receiving location. Expenses: The direct and indirect costs incurred in moving a purchased item from the supplier's warehouse/factory to the purchase order receiving location. Country Level Expenses: The costs of bringing merchandise from the origin country, through the lading port, to the import country's discharge port. Zone Level Expenses: The costs of bringing merchandise from the import country's discharge port to the purchase order receiving location. Assessments: The cost components that represent the total tax, fee, and duty charges for an item. Transportation: The facility to track information from trading partners as merchandise is transported from the manufacturer through customs clearance in the importing country. Actual Landed Costs: The actual landed cost incurred when buying an import item.   With Retail Trade Management data tracking costs and delays in items being received at their final stocking location, Driverless AI could be used to build a risk model to estimate the impact of changing exact import/transportation routes. Oracle Retail Invoice Matching The Oracle Retail Invoice Matching module in Oracle ERP includes the following information: Invoice Matching Results for Shipments: Shipment records are updated with the invoice matching, which attempts to match all invoices in ready-to-match, unresolved, or multi-unresolved status. Receiver Cost Adjustments: Updates the purchase order, shipment, and potentially the item cost in RMS, depending on the reason code action used. Receiver Unit Adjustments: Invoice matching discrepancies are resolved through a receiver unit adjustment. By joining the information in Retail Invoice Matching with data in other modules, we can build a risk model in Driverless AI for suppliers to predict the probability of invoicing issues for future orders. Next Steps This post gives a high-level view of how an open Oracle ecosystem enables our customers to leverage the latest technologies from our partner ecosystem with the LOB applications that they've relied on for decades to run their business. We're actively working with several customers to prove this out in their environments. In addition, my team is working to create a more detailed demo of the integration described here. We look forward to presenting that in more detail, both on this blog and at several upcoming meetups that Oracle and H2O.ai are jointly organizing. If you have questions, please reach out to Ben.Lackey@Oracle.com or Peter.Solimo@H2O.ai. We'd love to work with you and see what ML can do with your data!

Packaged line-of-business (LOB) applications are an area where Oracle is a market leader. These applications contain an enormous amount of data that has the potential to give amazing insight into...

HPC Investments Continue into ISC 2019

Today, 95 percent of all traditional high-performance computing (HPC) is still done in traditional on-premises deployments. The sporadic demand cycles and rapid evolution of specialized HPC technologies make flexible cloud deployment a great fit for enterprise usage. But other cloud providers simply haven’t been able to penetrate this market for a range of reasons, from performance to cost to a lack of key features, such as remote direct memory access (RDMA) capability. Over the last 12 months, we have invested significantly, in both technology and partnerships, to make Oracle Cloud Infrastructure the best place to run your Big Compute and HPC workloads. At OpenWorld 2018, Larry announced clustered networking, which lets customers run their Message-Passing Interface (MPI) workloads with performance comparable to, and in some cases better than, on-premises HPC clusters. This was the first, and is still the only, bare metal HPC offering with 100G RDMA in a public cloud.* It's in limited availability today, and we expect it to be generally available later in the year. Even further out, we’re working on a truly flexible and scalable architecture in which you can have bare metal GPUs, HPC instances, and even Exadata on a clustered network. This opens up use cases such as running a distributed training job on a cluster of GPUs that pull data from an Exadata, and then deploying the model on a set of compute nodes, all over the clustered network. We pushed the boundaries on this new offering with the ability to scale to up to 20,000 cores for a single job. This is far beyond what any other cloud can offer today for MPI workloads while maintaining efficiency and performance. To see the benchmarks that compare us and other providers, see the blog post that we published this week. Here’s a peek: We also partnered with Altair last year at OpenWorld to launch their Hyperworks CFD unlimited running our bare metal NVIDIA GPU offerings. We recently started working with them on their crash application, called Altair Radioss, and how clustered networking can help reduce the time and cost for these crash simulation jobs. For details, including benchmarks, read the blog post. This week we’re in Frankfurt at ISC 2019, along with our partners, showcasing some of these capabilities. You can talk to our engineering teams and try out some of the technologies at our booth, located at H-730. Some other things you’ll want to catch during the week: Vendor Showdown on Monday, June17, at Panorama 2, starting at 1:15 p.m. Exhibitor Forum Session on Tuesday, June 18, at Booth N-210, starting at 11:20 a.m. Blog: Accelerating DEM Simulations with Rocky on Oracle Cloud and NVIDIA Blog: Making Cars Safer with Oracle Cloud and Altair Radioss Blog: Large Clusters, Lowest Latency: Clustered Networking on Oracle Cloud Infrastructure Hands-on demos and labs at our booth H-730 Looking forward to seeing you there! Karan   * Based on comparison to AWS, Azure, and Google Cloud Platform as of June 3, 2019.

Today, 95 percent of all traditional high-performance computing (HPC) is still done in traditional on-premises deployments. The sporadic demand cycles and rapid evolution of specialized...

Accelerating DEM Simulations with Rocky on Oracle Cloud and NVIDIA

It’s a sunny afternoon, you’re mowing your lawn, and the grass buildup in your mower disrupts your smooth progress. This disruption could have been avoided if the design process for your mower involved airflow modeling with particles. Similarly, you’d hope that the discrete element method (DEM) was used to simulate the flow of beans through the coffee machines that you trust to brew your coffee.   DEM simulation packages like Rocky DEM from ESSS include particles and can be coupled with computational fluid dynamics or the finite element method to improve results. However, they add a layer of complexity that results in increased simulation time. To speed up the simulation, Rocky DEM provides the option to parallelize to a high number of CPUs or gain even more speed by unleashing multiple NVIDIA GPUs in Oracle Cloud Infrastructure. No special setup or driver is needed to run Rocky on Oracle Cloud Infrastructure. Import your model, choose the number of CPUs or NVIDIA GPUs, and start working. Using Oracle Cloud Infrastructure removes the wait time for resources in your on-premises cluster. It also avoids having people battle for high-end GPUs at peak times and then having those GPUs sit idle for the rest of the week. “Oracle Cloud Infrastructure and Rocky DEM have collaborated to provide a scalable experience to customers with performance similar to on-premises clusters. The bare metal NVIDIA GPU servers, without hypervisor overhead, further help to tackle very large problems in a reasonable amount of time,” said Marcus Reis, Vice President of ESSS. Depending on the simulation, Oracle Cloud Infrastructure provides different machine shapes to stay cost-effective without compromising on compute power. The following table shows the machine shapes suited for Rocky. Explore all the different storage options, remote direct memory access (RDMA) capabilities, and the composition of NVIDIA GPUs on our Compute service page.   Shape CPU GPU VM.Standard2.4 4 - BM.Standard2.52 52 - BM.HPC2.36 36 - VM.GPU2.1 12 1 x P100 VM.GPU3.1 6 1 x V100 BM.GPU2.2 28 2 x P100 BM.GPU3.8 52 8 x V100   “NVIDIA and Oracle Cloud Infrastructure are collaborating to help customers reduce their computation time from days to hours by providing GPUs for HPC applications. The Tesla P100 and advanced V100 GPUs increase customer productivity while reducing cost,” said Paresh Kharya, Director of Product Marketing, Accelerated Compute, NVIDIA. The following chart shows that NVIDIA GPUs offer up to 6X better price-performance than CPU-based instances for this simulation. It also shows faster results at a similar price when switching from P100 to V100 GPUs or increasing the core count of CPUs.   Oracle Cloud Infrastructure provides bare metal instances with up to 8 Tesla V100 GPUs, and it’s making a difference. Companies can start thinking about the engineering and the design of their next product rather than worrying about simulation runtimes or compute resource availability. Get started on Oracle Cloud Infrastructure, and run your Rocky DEM workloads today!

It’s a sunny afternoon, you’re mowing your lawn, and the grass buildup in your mower disrupts your smooth progress. This disruption could have been avoided if the design process for your mower...

Events

Large Clusters, Lowest Latency: Cluster Networking on Oracle Cloud Infrastructure

Oracle Cloud Infrastructure has expanded cluster networking by enabling remote direct memory access (RDMA)-connected clusters of up to 20,000 cores on our BM.HPC2.36 instance type. Our groundbreaking, backend network fabric lets you use Mellanox’s ConnectX-5, 100-Gbps network interface cards with RDMA over Converged Ethernet (RoCE) v2 to create clusters with the same low-latency networking and application scalability that you expect on premises. Oracle Cloud Infrastructure is leading the cloud high performance computing (HPC) battle in performance and price. Over the last few months, we have set new cloud standards for internode latency, cloud HPC benchmarks, and application performance. Oracle Cloud Infrastructure's bare metal infrastructure lets you run on-premises performance in the cloud. In addition to connecting bare metal nodes together through RDMA, cluster networking provides a fabric that will enable future instances and products to communicate at extremely low latencies. Performance Ultra-low node-to-node latency is expected on HPC systems. Partners like Exabyte.io have demonstrated Oracle Cloud Infrastructure's leading edge with those metrics. But when you have applications running on thousands of cores, low node-to-node latency isn’t enough. The ability to scale models down to a very small size is more important. In computational fluid dynamics (CFD), users typically want to know the smallest amount of work they can do on a node before they hit a network bottleneck that limits the scalability of their cluster. This is the network efficiency of an HPC cluster or, in other words, getting the most “bang for your buck”! The following chart shows the performance of Oracle’s cluster networking fabric. We scale above 100% below 10,000 simulation cells per core with popular CFD codes, the same performance that you would see on premises. It’s also important to note that without the penalty of virtualization, bare metal HPC machines can use all the cores on the node without having to reserve any cores for costly overhead.   The ability for a simulation model to scale this way highlights two important design features. The first is the stability of the underlying network fabric, which can transfer data fast and consistently. The second important design feature is that there is no additional traffic or overhead on the network to limit throughput or latency. You can see this stability in the following chart, which compares on-premises HPC network efficiency to cloud HPC network efficiency. CFD is not the only type of simulation to benefit from using Oracle’s cluster networking. Crash simulations, like those run on Altair’s RADIOSS or LS-Dyna from LSTC, and financial matching simulations, like those offered by BJSS, also use cluster networking. Price Oracle Cloud Infrastructure offers the best performance by default. You don’t pay extra for performance of block storage, RDMA capability, or network bandwidth, and the first 10 TB of egress is free. Cluster networking follows that same paradigm—there is no additional charge for it. Availability Today, cluster networking is available in the regions that have our HPC instances: Ashburn, London, Frankfurt, and Tokyo. Cluster networking will continue to spread throughout all of our regions as cluster networking-enabled instances continue to roll out. To deploy your HPC cluster using cluster networking, reach out to your Oracle rep or contact us directly. Also, visit us at the ISC High Performance conference in Frankfurt June 16–20. We’re in booth H-730. Hope to see you there.

Oracle Cloud Infrastructure has expanded cluster networking by enabling remote direct memory access (RDMA)-connected clusters of up to 20,000 cores on our BM.HPC2.36 instance type. Our groundbreaking,...