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High Performance Computing 101: Funding Your HPC Through Technology Grants

By marketing@site-a.com

While the current economic environment can be a time of great uncertainty, it has proven to also be a time of significant opportunity for those seeking grants to help fund a variety of technology solutions, especially High Performance Computing / HPC  solutions.  Despite some budget cuts, the National Institutes of Health received a $2.6 billion or 7 percent increase in fiscal year 2020 from FY 2019.  Budget increases are also taking place in other  technology-focused market segments such as education and medical research.

Researchers are well aware that much of the groundbreaking research facilitated by HPC is made possible only through grant funding programs offered by NIH and other institutions that have just received increased funding.  Should cuts ultimately materialize, researchers can expect increased competition for funding in what is already a very competitive grant-funding process.  One of the most well-known funding programs for acquiring HPC resources, the National Science Foundation’s Major Research Instrumentation (MRI) grant, already sees only 1 out of 5 applicants receive funding.

For those organizations that focus on or utilize High Performance Computing infrastructure as a critical component to their research, the current atmosphere for funding has elevated the need to implement best practices when applying for financial grants and understanding the full range of funding channels that are available.  For example, those applying for Major Research Instrumentation / MRI grants through the National Science Foundation can significantly increase their chances of receiving funding by focusing upon the following seven core elements of grant success.

Seven Keys To A Successful MRI Grant Proposal

  1. Understand the Source.  If you are applying for a grant from the National Science Foundation, it is critical that you focus on understanding what is important to the NSF, its current research goals and objectives, and how these could intersect with high performance computing.  To get started visit https://www.nsf.gov/funding/preparing/
  2. Find HPC Funding Opportunities. While there are numerous HPC funding sources and your PSSC Labs team can help you with this process, you will want to review the National Science Foundation’s “Find Funding” resources:  https://www.nsf.gov/funding/index.jsp
  3. Get Specific.  When developing your grant proposal make sure that you clearly define your HPC computing needs. Also, know that your PSSC Labs team can work with you to clearly and effectively communicate your needs with all the details that the funding source is expecting.  It is critical that you show that you will have a support team from the very start of your project through to completion.
  4. Be Prepared. Cost-Sharing is Key.  According to NSF, “…the America COMPETES Act of 2007 (Public Law 110-69), cost-sharing of precisely 30% of the total project cost is required for Ph.D.-granting institutions of higher education and for non-degree-granting organizations. Non-Ph.D.-granting institutions of higher education are exempt from the cost-sharing requirement and cannot include it.”  Learn more about cost sharing by clicking here and also remember that the PSSC Labs Team can help you explore the practical realities of the MRI HPC grant proposal requirements including cost-sharing.
  5. Focus on Benefits.  Communicate the full potential impact and the practical benefits of your research and the role of high performance computing.  The PSSC Labs team can work with you to provide research-backed insights into HPC performance data that can be critical to your grant proposal.  PSSC also will work with you to demonstrate that you understand the HPC Cluster and the impact your Cluster will have on the research proposal.
  6. Build the Need.  Talk to your data center and HPC team members as well as others in your area/market/industry to gather and build insights that will be a critical part of your proposal.  You may also want to explore other successful MRI grants to learn what has been funded and how this could intersect with your research.  Learn more.
  7. Share the Vision.  Articulate the full impact of the research that will be made possible by the grant as well as the impact your research will have on your community and those whom you serve.  You can also explore the MRI Map of Recent Awards for additional inspiration for your HPC project.

PSSC Labs has a 25+ year record working with grant winners to help secure grant funding from the Department of Defense, Department of Energy, National Science Foundation, National Institutes of Health, and many more grant-giving organizations.  Our experience deploying turnkey HPC Clusters for computational chemistry, weather modeling, computational fluid dynamics, and biomedical informatics applications has helped us find specific and less known grants for scientists that are applicable to their research based on their field of study, parties affected by their research, and many other criteria.

If you are a researcher looking to acquire computing resources, reach out to PSSC Labs, and we will help you find grant programs that are applicable to your specific research. From the National Science Foundation’s Major Research Instrumentation grant, to the National Institutes of Health’s S10 grant, our 25+ year background working with leading research universities has given us an intimate understanding of the funding channels that are available and the application strategies you should employ.

Whether or not funding is ultimately increased or decreased to these grant giving institutions, it never hurts to explore all available funding channels.  Talk to us about your research, and let PSSC Labs help you maximize your chances for securing funding.

PSSC Labs Featured in Becker’s Hospital Review: Is cloud computing the best option to handle the life sciences data avalanche?

By marketing@site-a.com

While cloud services are becoming more sophisticated and customizable, they may fall short in handling the “data avalanche” that the life sciences industry is grappling with.

Data sets that are too large and complex for traditional data processing methods to handle are a sticky issue in the sciences, perhaps no more so than in the life sciences industry. From biotechnology to pharmaceuticals to medical device manufacturers, private-sector organizations and research institutions spend their days performing research and development tasks that produce terabytes of raw data gleaned from clinical results, disease states, scientific studies, and individual patient data.

This “data avalanche” must then be sorted, analyzed, and distilled into actionable information so that new medications, biomedical devices, and other products can be developed, refined, tested, and approved for sale as quickly as possible. Because of the gravity of the decisions that depend on this information — human lives are literally at stake – life sciences companies operate under some of the closest scrutiny and strictest regulations in the world.

To cope with the data avalanche and compete in an increasingly hypercompetitive global marketplace, many life sciences organizations are considering cloud computing solutions to increase their data processing power and storage capabilities. However, cloud solutions can often fall short in big data processing capabilities and result in cybersecurity issues, eye-popping monthly bills, and other problems.

Big Data = Big Cloud Bills
Cloud providers are quick to tout the alleged cost savings of using their services over purchasing in-house IT infrastructure. They claim that customers do not have to come up with large capital investments upfront and pay only for what they use, as they go. This works out well for some companies, particularly small start-up firms that are cash-poor and that aren’t dealing with very large data sets or highly complex computations.

However, cloud services such as AWS are notorious for sky-high monthly bills filled with hidden “gotchas,” especially for companies that require a lot of computing power. Deep Value, which develops complex research-driven trading algorithms, decided to run some numbers once their AWS bills began to exceed $70,000/month. In the end, they discovered that using AWS was 380% more expensive than purchasing their own high-performance computing equipment.

In addition to unexpected line items on their monthly invoice, cloud customers can also be hit hard by the indirect costs of performance problems and cyberattacks.

Cloud Performance May Not Be Up to Par
In the life sciences, the reliability and uptime of mission-critical systems are paramount, and a key marketing point of cloud services is the idea that customers don’t have to worry about maintaining their own equipment. Yet as cloud computing grows in popularity, cracks are appearing in its foundation. In February 2017, AWS suffered an outage that was so bad, it couldn’t get into its own systems to communicate with the throngs of customers that were knocked offline – all due to a misconfiguration on the part of an AWS employee.

The cloud doesn’t necessarily beat in-house infrastructure in the performance category, either, especially when processing enormous data sets. Cloud service providers typically run multiple servers in different locations, which can cause very serious latency issues when transferring large data sets and performing the highly complex calculations that life sciences companies run all day long.

The Dark Cloud of Cybersecurity Concerns
Over the past few months, an epidemic of AWS breaches impacted organizations large and small, including Verizon, the Republican National Committee, and a company called Talent Pen that processed job applications containing the personal information of thousands of Americans who held Top Secret security clearances. All of these breaches were due to the affected organizations (or their third-party vendors) not having configured their AWS security settings properly.

Cloud security settings can be very tricky, but even if an organization gets them right, they can still be hacked through no fault of their own. Because so much valuable data from so many different organizations is being migrated to the cloud, data centers have become highly attractive targets for hackers. Financial regulators and the tech industry are so concerned about the possibility of a major attack on AWS that in the wake of the February outage, they sounded the alarm over what they deem an over-reliance on the AWS service by the organizations that form the bedrock of American society, particularly financial companies.

The cloud security risk to life sciences organizations is three pronged: a cyberattack that brings down their cloud could leave them unable to operate; invaluable market research and digital intellectual property could be stolen by competitors or foreign governments; and a hack could mean running afoul of a myriad of government regulations and being hit with millions of dollars of fines and lawsuits.

In-House IT Infrastructure Means Optimum Customization & Control
Finally, cloud services suffer from customization and control issues. When using a cloud service, the ability to make changes is quite limited. Services such as AWS offer a menu of items that fit most organizations’ needs – unless those needs are highly specialized. Organizations that own their own computing equipment have complete control over their data environments and can act quickly to make adjustments or implement new features.

Rather than rushing to migrate to the cloud, certain life sciences organizations might be better served by investing in their own high-performance computing equipment to reduce their costs, improve their cyber security, and make the data avalanche work for them to accelerate innovation. Only a true comparison between the two can shed light on which option works best.

Source: https://www.beckershospitalreview.com/healthcare-information-technology/is-cloud-computing-the-best-option-to-handle-the-life-sciences-data-avalanche.html

PSSC Labs Featured in IDG Connect: “Big Intelligence” is the real AI

By marketing@site-a.com

Everyone knows the scenario – after years of development and advancements, machines imbued with Artificial Intelligence somehow become self-aware without the knowledge of their human creators and end up destroying humanity as we know it. It’s a crazy premise, but if you listen to Tesla and SpaceX CEO Elon Musk and other futurists, it’s a possibility.

Science fiction movies have a habit of predicting doom and destruction, but the truth is we don’t have to worry about this dystopian world where AI becomes something man cannot control. True, there could be unpredictable social consequence much like those brought about by the rise of social media. But in terms of actual takeover and destruction, the odds are slim to none. Rather than fretting about killer robots, it’s time to realise that the AI revolution is actually the proliferation of “Big Intelligence” and its future is much more benign. Big Intelligence is where we are today in terms of automation, robotics and computing – and it bears little similarity to the sentient machines most people think of when they hear AI. What’s more, fear of AI shouldn’t hinder the legitimately useful work Big Intelligence can help complete.

AI is Big Intelligence in disguise

Many companies like to tout the AI capabilities of their offerings, but much of the literature you’ll find is nothing more than marketing gimmick. The technology being commercialised or used in research is not really AI, but simply better programming and faster data crunching fueled by advancements in hardware. While futurists like to envision the all-knowing, self-aware machine, Artificial Intelligence or machine learning is not a substitute for human intelligence, and even the most advanced machines are far from substituting a human brain.

With the convergence of large datasets with faster computers and better code to process the data, Big Intelligence has progressed over the past decade due to real technology advancements that allow us to collect and process data at an ever increasing rate, and it’s what powers most AI platforms today. But the key here is not some technology that can replace human intelligence – rather, AI as it currently stands is a set of tools that merely help us better process, interpret, and understand the mass amounts of data companies gather from actual thinking humans. It can help businesses better predict their own and their customers’ needs, to better optimise and even conserve resources.

Hardware is critical to Big Intelligence

The only reason we are discussing the possibility of AI is due to recent advancements in computing performance. If the hardware could not process data in near real-time, things like self-driving cars, automated logistics centres, operating systems that are virtual and learn through association would still be figments of our imagination.  However, ultimately these so-called AI data still rely on constant data input from humans, without which they could not function.

Artificial Intelligence is a misnomer. Most of what we consider AI is really a high-performance computer (HPC) crunching a massive amount of data, which does not have intelligence the way a human brain does to conceptualise, reach its own conclusions and think for itself. What it can do is improve computing processes through automation, but the end result of most automated processes still requires human supervision.

Computers can now process data in real time, but all that processing power is useless if people feed the machine bad data from the get go or don’t know what to do with all that information and analysis once they have it. Cognitive solutions that leverage AI can provide explanations, recommendations, and inform what future actions or outcomes might be required via their predictive nature, but it’s still a human who is feeding the beast.  We are still the “intelligence” behind AI – the artificial part is being able to crunch data at a scale and time-span humans can’t achieve.

The promise of AI has been around a long time, but never went anywhere because hardware could not sustain that much data analysis. No one could capitalise on the concept. That is not the case today. Hardware has advanced to such a degree that for each new automation concept there is a company that builds the hardware necessary to realise the idea.  Super-fast multi-core processors or massive storage devices are tomorrow’s recycling candidate. Cloud computing, virtualisation, faster processors – all make up the core technology of what we call AI.

In addition, the amount of data companies now churn out is almost unfathomable. Almost everything we touch is sending data to someone from smartphones, to the internet, to online processes, to set-top boxes, and on and on. Technology is everywhere and every bit of it is a data source. At the convergence of all this Big Data and mock AI technology is nothing that resembles actual sentience – it’s simply Big Intelligence. And we’ll continue to see it improve as this convergence of better programming through High Power Computing and Big Data as companies traditional working with one or the other begin to bring the two together in ever more creative applications.

We are living in a truly exciting time of data and high-performance computing. We now have the ability to take advantage of data at scale and analyse that data in real time. But we shouldn’t let the misnomer of AI scare us away from this pursuit. Let’s call Big Intelligence what it is and enjoy the power more data and advanced hardware has bestowed on the human race.

Source: http://www.idgconnect.com/blog-abstract/28283/-big-intelligence-real-ai

The Benefits of Delivering a Diskless HPC Cluster

By marketing@site-a.com

We have manufactured a lot of HPC Clusters over the past 20+ years… 2237, to be exact.  One of our goals is to deliver a fully functioning system that remains 100% operational for as long as possible.  We understand that capital expense budgets are tight and systems may be in operation well beyond the normal three-year warranty period. In fact, we have seen many clusters that are in production nearly ten years since the delivery date.  Seeing these clusters that are a bit long in the tooth gives us great pride in knowing our HPC platforms are some of the most reliable in the industry but they also give us a long term perspective on which components tend to fail over a period time. 

What we typically tell our end users is any component with a moving part is going to fail over time.  So what components inside of a server have moving parts?  Luckily there aren’t many.  Power supplies, fans and hard drives are the most obvious ones.  Obviously, a systems needs a power supply and fans for operation; but what about a hard drive? 

Our PowerWulf HPC Clusters are configured in a very traditional manner with a Head Node and Compute Nodes networked together with a high speed backplane.  The Head Node contains the basic operating system and HPC software tools (our tool set is called CBeST). For protection we configure our Head Node with two hard drives configured in a RAID 1 mirror.  This is a very inexpensive way to add system protection as well as adding an easy OS upgrade path.  It is probably not possible to remove the Head Node hard drives and still have a functioning HPC Cluster.  But what about the hard drives on the Compute Nodes?  Are they really necessary?  The answer is no.  Compute Nodes really just need a small Operating System kernel that can be loaded into memory on boot.  Our latest version of CBeST Cluster Management Toolkit allows for this diskless Compute Node configuration.  Operation is very simple.  Just boot up the Head Node and then boot the Compute Nodes which will connect via PXE boot to the Head Node.  The Head Node then pushes a small OS kernel down to each Compute Node which stores the image in memory.  The process is seamless to the end user.

What are the benefits and drawbacks of a “Diskless” HPC Cluster?  As we discussed the most obvious benefit is reduced support cost and down time in the event of Compute Node disk failure.  Now no one needs to go into the data center to swap a disk and reimage that drive, configure the network settings and configure the OS.  The other benefit is regarding security.  Many government agencies we work with required “hardened” systems.  Removing the hard drives on the Compute Nodes ensure that’s there isn’t any data that exists on the Nodes that can be extracted by accessing and removing the hard drive.  In terms of drawbacks, the only possible issue may be slower time to boot.  Our engineers would argue that this is not really an issue with our latest version of CBeST.

All in all, removing the disks from our PowerWulf HPC Cluster should help extend the longevity even more.  Who knows maybe one day we will see our cluster reaching a 20-year lifespan!