Engineers face many daily operational inefficiencies that inhibit their time-to-solution. Every day we work with engineers to provide solutions to computing resource limitations and management of HPC. Specifically, we excel at utilizing our platform to accelerate HPC engineering simulations. The impact is real: Rescale users have seen accelerated time-to-solution by 23%, allowing engineering teams to be 12% more productive overall.

In this article, we hope to give you exactly what you need to better plan for HPC in 2019.

(Your) 2019 Engineering Objectives: Measurably Improve Engineering Team Productivity

1. Shorten the turnaround time of your engineering services

2. Eliminate engineering hours spent in HPC queues

3. Increase the individual productivity of your engineers

4. Develop best practices for HPC usage by workflow

Some key issues engineers face when developing a product are simulation constraints due to queue times from lack of computing resources, software availability, architecture diversity, and departmental management. The shortage of these vital resources and tools results longer development cycles of the products that generate revenue.   

1. Shorten the turnaround time of your engineering services

By eliminating queue time and enabling engineers with the best HPC hardware and software, you can optimize your research pipeline and push innovations to the market, sooner.

The Proof:

Dinex, an automotive exhaust supplier, saw a reduction in time-to market of 25% by utilizing the Rescale platform. With abundant computing resources available through our public cloud partners, you gain the ability to mitigate queue time by immediately securing the resources as you need them. The abundant computing hardware and software diversity allows engineers to run simulation that were previously unsupported by on-premise systems (either based off intolerable queue time or software and hardware resource demand). The availability of software and computing resources, ability to innovate design of experiments, and the mitigation of queue time allow engineers to be more efficient and deliver products to market faster.

2. Eliminate engineering hours spent in HPC queues

Stop waiting to run your simulations because of limited HPC resources and/or low priority. Empower every engineer with the resources to run simulations immediately using our AWS, Azure, and IBM cloud resources.

The Proof:

Queues for running simulations can halt the research pipeline and waste valuable engineering time. A queue directly results in a delayed time-to-solution that can be critical to the progression of research. The days spent without answers can cost a company millions of dollars in engineer idle time. The ability to secure hardware as needed allows engineers to be agile with their computing resources and break the constraints of a static on-premise HPC system that limit their simulation volume and fidelity. These inefficiencies directly impact the company’s objective to bring innovations to the market and generate revenue; so, the ramifications of research inefficiencies reverberate throughout the entire organization and externally. By utilizing Rescale, you can run a single simulation on 10,000 cores, or run 10,000 simulations on 10 cores each: the availability of resources means there is no reason not to run a simulation immediately.

3. Increase the individual productivity of your engineers

Remove the constraints of static On-Premise HPC systems and engage a dynamic environment with a the latest HPC hardware and simulation software. Explore new DOE and optimize your research pipeline to achieve the fastest time-to-solutions.  

The Proof:

Rescale has over 300 ported and tuned software’s incorporated into our platform; many on a pay as you use model such as ANSYS, Siemens, CONVERGE, and LS-DYNA. Utilization of the endless, diverse computing resources allows engineers to use the best software on the best hardware, always. The coupling of the best software and hardware allows engineers to have the best results available, quickly. In addition, engineers are exposed to new software and computing resources that were previously unavailable. Some Rescale customers have seen as high as 80% reduction in time-to-answers. The freedom of architecture choices allows for the exploration of new processes in your design of experiments which can create quicker research pipelines with higher fidelity. Enabling researchers with the best tools HPC tools produces quicker results and increases productivity.

4. Develop best practices for HPC usage by workflow

Gain real time insight into your engineers activities and utilize the information to optimize your engineering departments operations and finances.

The Proof:

Scale X Enterprise allows you to fully manage your engineers by tracking expenses, allocating resources, and budgeting teams. With control of computing and software resources, budgets, projects, and access, you can fully manage how your engineering teams utilize cloud computing. In addition, access to billing summaries and real time spending dashboards allow you to monitor your computing expenses. Rescale doesn’t only provide a solution to engineering inefficiencies, it gives management the insight to innovate their own research pipeline.  

Rescale is a turn-key platform that enables access to limitless computing resources and over 300 ported and tuned softwares. With ScaleX Enterprise’s management dashboard, engineering departments are capable of fully managing and reporting on their HPC usage. Rescale has had significant impact on many of our customers; but to understand the true impact Rescale can have on your organization, it is best to reach out to us. With our confidential tools and industry leading knowledge, we can define the impact of Rescale on your engineering operations.

If you have any questions or interest in seeing how Rescale can improve your engineering department, please reach out to our specialists today.

This article was written by Thomas Helmonds.

Total Cost of Ownership (TCO) is a powerful financial tool that allows you to understand the direct and indirect expenses related to an asset, such as your HPC system. Calculating the TCO for an on-premise HPC system is direct: add up all expenses related to your system and its management for the entirety of its deployment. But what happens when you’re interested in switching to cloud-enabled HPC? Can you confidently compare the cloud-enabled HPC system’s TCO with an on-premise HPC system’s TCO?

This question has been addressed by many different institutions.

Our view is simple: TCO is a poor financial tool for evaluating the value of cloud-enabled HPC. Comparing a system with a static environment against a dynamic environment creates an unreliable and misleading analysis. It is an apples to oranges comparison, and using TCO to assess cloud-enabled HPC attempts to make apple juice from oranges.

What is a static environment and how does it apply to my TCO analysis?

Static environments for TCO are used when you have set expense for a set return. For an on-premise system, you can get X amount of computing power for Y amount of dollars. This same relationship goes on for most expenses in the cost analysis of an on-premise HPC system until you reach a comprehensive TCO. There are some variable costs involved (fluctuation in software pricing, staffing, energy, unpredicted errors, etc.); however, margins can be used to monitor their influence on the TCO. Essentially, you end up with the general TCO analysis of X computing power = Y expenses ± margin of change. This is a great tool for comparing systems with little expense variations and known rewards that create a near-linear relationship. However, what happens when the computing power is nearly infinite, and the expenses are reactive, as is the case for cloud computing?

What is a dynamic environment and how does it apply to my TCO analysis?

A dynamic environment for a TCO analysis is a system where the expenses and rewards are not directly correlated, making them difficult to define and compare. In a cloud-enabled HPC system, you pay for computing power when you need it; there is little initial capital expenditure needed to use cloud-enabled HPC, when compared to on-premise HPC systems. In this environment, your expenses for HPC become less predictable and more reactive because they are generated from your computing demand. In addition, you are no longer constrained by a set limit of computing power, so your reward is extremely variable due to how much you utilize HPC. This scalability can heavily influence your HPC usage; especially if your current system is inhibiting your peak performance and potential Design of Experiment (DOE). The rewards of cloud computing beckon the question: if you have less restrictions on HPC, would you utilize it differently?

What happens when you use TCO to compare on-premise vs cloud-enabled HPC systems?

TCO is a tool that is helpful for static environments, but when you try to take the same static tool and apply it to a highly dynamic environment, it is misleading. For example, consider you want to calculate the TCO of an on-premise HPC system. First, you must predict your peak usage and utilization for a system that will be used for approximately 3 years. To manage all an organization’s requirements, trade offs are made between peak usage and the maintenance of high utilization.Then you must pay the massive initial capital expenditure to purchase all the hardware, software, and staff required to assemble and operate the system. Calculate all these expenses and you receive your TCO for a system that awards you limited computing power.

Now, try to use the same analysis of a cloud-enabled HPC system. Most take the projected peak computing power and average utilization and multiply it by the price to compute in their prospective cloud service provider. This is the first problem, you’re already treating both systems as if their rewards and expenses are equal. With cloud-enabled HPC systems, you have instant access to the latest hardware and software resources which means you are always utilizing the best infrastructure for your applications. In addition, your computing power becomes near-infinite meaning there is no reason to have a queue for running simulations, which increases your productivity. These innovations in the research and design process are essential to getting better products to market before competitors, and the inability to easily scale and upgrade resources for an on-premise HPC system can severely inhibit your ability to compete. The differences in rewards makes it hard to quantify the expenses associated with the aging on-premise HPC system’s effect on potential new workflows that can help you outcompete your competition.

When comparing HPC solution’s TCO, you must acknowledge the rewards provided by each solution, because the lack of a rewards should be reflected as an expense in the competitor’s TCO. For example, if your cloud computing solution provides no queue time, better computing performance, and new DOEs, but your on-premise solution doesn’t, then you must calculate the expenses of inefficiency correlated to the absence of rewards from the on-premise system. That is the only way to level the TCO with the corresponding rewards, but it proves extremely difficult to define exact numbers for each reward; henceforth, making TCO a misleading and inaccurate tool. Comparing the TCO and rewards of cloud-enabled and on-premise HPC systems is pointless because the tool does not address the reality of each system; one is static and requires massive investment to create limited computing power, and the other is agile and requires pay-as-you-go expenses for limitless computing power.

Determining the financial implications of incorporating cloud-enabled HPC into you HPC system can be difficult. Thankfully, Rescale has many specialists and confidential tools to help define the benefit of cloud-enabled HPC on your organization.

Come talk to us today.

This article was written by Thomas Helmonds.

Here at Rescale, we care very much about the cost and value of HPC. It was a major topic in our previous blog post “Addressing the Cloud Cynic.” So, we thought we’d back up some of our claims with the cliff notes from a key analyst’s study. In June 2018, Hyperion Research updated their global HPC study that used close to 700 different case studies over a 3 year period to help calculate the impact of HPC on enterprises. It breaks down the return on research and returns on investment associated with capital expenditures on HPC. The following information is focused on data extrapolated from the key industries that Rescale focuses on: Academic, Defense, Government, Life Sciences, Manufacturing, Oil and Gas, Telecomm, and Transportation.

The Data:

– The average revenue earned per dollar spent on HPC is $332.80.
    — The 3 highest are Transportation, Government, and Oil and Gas at $1804.00, $1205.00, and $416.00, respectively.
    — The 3 lowest are Academic, Defense, and Manufacturing at $9.20, $75.00, and $83.00, respectively.

– The average profit earned per dollar spent on HPC is $40.60.
    — The 3 highest are Government, Oil, and Gas, and Academics at $112.00, $53.00, and $44.00, respectively.
    — The 3 lowest are Defense, Transportation, and Manufacturing at $5.30, $15.60, and $20.20, respectively.

– The average cost of innovation required $11M in HPC usage.
    — The key drivers of HPC use are scientific breakthroughs and support research programs at $76M and $27M, respectively.
    — Those two types of innovation create a strong HPC market in Companies and Academics, not Government sectors.

Key Takeaways:

– The data is heavily influenced by the outliers.
    — Average revenue generated, excluding the top 2 highest outliers (Transportation and Government), is equal to $159/dollar spent on HPC.
    — Average profit generated, excluding the top and bottom outliers (Government and Defense), is equal to $34/ dollar spent on HPC.

– Government and Oil and Energy have seen the largest return on HPC in both revenue and profit.
    — Interestingly, Transportation companies that utilized HPC were the largest revenue earners, but the second smallest profit earners.
    — Companies engaged in innovations in scientific breakthrough and support research programs are strong drivers of HPC usage.

Understanding the impact and value of HPC on your organization is not as easy as applying industry metrics to your company. The true cost of ownership and practical application for HPC requires a deep dive into organizational needs and inefficiencies; however, these market statistics prove that HPC has had a positive impact on all organization and should be considered for every organization.

*The statistics provided were calculated from a subgroup of the overall data that represents Rescales’ target markets. The full study can be viewed in the link provided.

Joseph, Conway, and Norton. (2018, June 1). Economic Models For Financial ROI And Innovation From HPC Investments. Retrieved from: https://www.hpcuserforum.com/ROI/

 

This article was written by Thomas Helmonds.

Rescale - Announces Innovations to Accelerate Time to Results

SAN FRANCISCONov. 14, 2018 — Rescale, the leader in enterprise big compute in the cloud, today at Supercomputing 2018 announced the release of new high-performance computing (HPC) workflow capabilities: high-performance storage (HPS) and End-to-End desktops. With these tools, enterprises can now boost efficiency and enhance team collaboration.

“Rescale HPS gives users high performance access to shared data, and together with the new End-to-End desktop workflows, users are able to accelerate time to results for simulations in the cloud,” says Gabriel Broner, Vice President and General Manager, High Performance Computing at Rescale. “Our customers are looking forward to leveraging these new capabilities to achieve real-time interactive HPC.”

Key innovations include:

Rescale High Performance Storage (HPS) greatly improves simulation workflow efficiency and speed by providing fast access to recurring shared data (such as geometry, meshing and load scenario data, etc.). Furthermore, this high-speed persistent filestore can be shared across multiple concurrent users, clusters, or desktops.

End-to-End desktop workflows allow users to launch multi-node, high-core count jobs directly from the application software user interface (UI). This provides direct interactivity with the job, allowing pausing, modifying the mesh(es), and resuming, all directly from the application UI.

The advanced abilities of Rescale HPS and End-to-End desktop workflows give users a simpler transition to hybrid and cloud. Shared high performance storage is common on-premise and Rescale brings this capability to cloud workflows. For users who today run jobs in a workstation, End-to-End desktops enable them to continue a traditional workstation experience, while transparently leveraging hundreds of cores across powerful large clusters in the cloud.

“Rescale is continuing to lead the charge when taking enterprises into the future of high-performance computing in the cloud,” says Steve Conway, Hyperion Research. “With these new innovations, Rescale is providing users with enhanced performance, collaboration capabilities and the possibility to interact real-time with their simulations.”

For more information on storage and virtual desktops, visit www.rescale.com.

About Rescale
Rescale is the leader in enterprise big compute in the cloud. Rescale empowers the world’s transformative executives, IT leaders, engineers, and scientists to securely manage product innovation to be first to market. Rescale’s ScaleX® multi-cloud platform, built on the most powerful high-performance computing infrastructure available, seamlessly matches software applications with the best architecture in the cloud or on-premise to run complex data processing and simulations. Rescale partners with the four largest cloud service providers, and enables over 125 Global 2000 enterprise customers including four of the top five largest global automotive manufacturers and two of the top three largest global aerospace and defense companies. For more information on Rescale, visit www.rescale.com.

This article was written by Samantha Lindsay.