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SpaceTech Expo Highlights Public Private Partnerships for the Final Frontier

By marketing@site-a.com

SpaceTech Expo 2022, Long Beach, CA

PSSC Labs continues to support space technology and just attended the 2022 SpaceTech Expo in Long Beach, CA. We found some phenomenal resources for the future of space, and we hope to share those with you.

One of the major takeaways from this event was the keynote given by major Adam A. Burnetta, Program Manager, Space Enterprise Consortium, United States Space Force (USSF). Major Burnetta shed light on many new initiatives the U.S. Government is doing to close supply chain gaps for advanced space research and technology. Noted by Major Burnetta is a shift in funding towards more commercial industry investment. Major Burnetta explained that Space Systems Command for the USSF wants to leverage the commercial abilities that we have seen explode in the last five years. In a clear statement Major Burnetta says, “Space Systems Command is instituting a simpler method to engage and facilitate industry partnership in order to leverage commercial innovation and investment on space technology.” Major Burnetta went on to discuss how the field offices will interact with the businesses local to them in order to facilitate direct lines of communication with the industry.

This type of government and commercial partnership outreach program is very similar to the post-war innovation period. In fact, the National Bureau of Economic Research analyzed the research and development contracts of that time and concluded that government spending for R&D in the commercial sector was valued at $7.4 billion in today’s dollar. This will be a welcome change for many businesses looking to work with the USSF. Major Burnetta’s presentation explained the government wants to adopt, buy, adapt, and create from the commercial platform in order to advanced space development. America’s goal is clear – continued superiority over the vast expanse of space, but this time with the help of businesses that make it possible.

References:

SpaceTech Expo 2022 Keynote: Major Adam A. Burnetta Program Manager, Space Enterprise Consortium, US Space Force

World War II R&D spending catalyzed post-war innovation hubs. NBER. (2020, September). Retrieved May 24, 2022, from https://www.nber.org/digest/sep20/world-war-ii-rd-spending-catalyzed-post-war-innovation-hubs

We hope to see you at the next expo! Interested in future tech updates from PSSC? Please fill out the form below to be added to our mailing list.


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PSSC Labs Featured in TechRepublic: 4 steps to implementing high-performance computing for big data processing

By marketing@site-a.com

If your company needs high-performance computing
for its big data, an in-house operation might work best. Here’s what you
need to know, including how high-performance computing and Hadoop
differ.

In the big data world, not every company needs high performance
computing (HPC), but nearly all who work with big data have adopted
Hadoop-style analytics computing.

The difference between HPC and Hadoop can be hard to distinguish because it is possible to run Hadoop analytics jobs on HPC gear, although not vice versa. Both HPC and Hadoop analytics use parallel processing of data, but in a Hadoop/analytics environment, data is stored on commodity hardware and distributed across multiple nodes of this hardware. In HPC, where the size of data files is much greater, data storage is centralized. HPC, because of the sheer volume of its files, also requires more expensive networking communications, such as Infiniband, because the size of the files it processes require high throughput and low latency.

Read the full article: https://www.techrepublic.com/article/4-steps-to-implementing-high-performance-computing-for-big-data-processing/

About PSSC Labs 

For technology powered visionaries with a passion for challenging the status quo, PSSC Labs is the answer for hand-crafted HPC and Big Data computing solutions that deliver relentless performance with the absolute lowest total cost of ownership.  

 We are true innovators offering high performance computing solutions to solve the world’s most demanding problems. For 25+ years, organizations of all sizes and from a variety of sectors rely on PSSC Labs’ computing systems. We are proud to support many departments within the United States government, Fortune 500 companies, as well as small and medium-sized businesses.  

All products are designed and built at the company’s headquarters in Lake Forest, California.

PSSC Labs Featured in Data Center Post: Why Going Green Doesn’t Have to Mean Feeling Blue

By marketing@site-a.com

If you’ve ever purchased an off-the-shelf laptop, you’ll likely be slammed with bloatware.  It’s the glut of mostly useless software that comes pre-installed on every machine, hogging resources and slowing down your cool new purchase. Fortunately, most bloatware can be easily uninstalled. On a similar note, and more insidious to your company’s bottom line is the excessive hardware that often comes with buying traditionally manufactured server platforms. It’s a silent but constant resource hog, impossible to remove and constantly eating away at your operating budget.

Traditional computer server manufacturers often try minimizing their own operating cost by creating standard platforms. The lower the SKUs, the faster and cheaper the manufacturing process generally is, along with more simplified logistics of handling their inventory. So for people looking to buy one of these platforms, traditionally they must look at a tradeoff between performance, pricing, and long term operating expenditure of the hardware.

The problem with buying a standard server platform is that a small business selling a simple SaaS app can end up buying the same setup as a company that is using the server for high performance computing. The disparate server needs means that if the standard platform happens to fit exactly the level of processing power and storage that your company’s needs, then you are in luck. But for the majority of companies, you’ll probably end up with more hardware than you actually need, because these standard platforms are designed for a wide range of needs rather than your specific one. And when you find yourself nearing the end of the spectrum for your current setup, upgrading could mean a dramatic shift in cost as the next step up offers way more storage or processing power than you actually need.

Yet it’s a fallacy that more performance will equate to high capital expenditure and operating expenses. After all, with a standard server platform you are likely paying for hardware you don’t need, not to mention the high energy cost and long term OpEx associated with powering all that excess hardware. Companies like Facebook and Apple have long figured out that custom hardware not only results in higher performance, but overall lower operating costs as well. The Open Compute Project (OCP) is one example of these large enterprise companies working to make hardware that is more efficient, flexible, scalable, and cost effective. However, OCP isn’t available to small businesses that average 100-1000 nodes, selling mainly to larger enterprises requiring upwards of 10,000 nodes, and requires a tremendous amount of lead time for product delivery.

In fact, few of the businesses developing custom hardware gear them toward the average size data center. However, custom hardware developers do exist, and they are worth seeking out. After all, bearing a larger than necessary energy footprint from all the excess hardware is not only costly, but results in a heavy environmental toll as well.  According to the EPA, volume servers are responsible for 68 percent of all electricity consumed by IT equipment in data centers in 2006. A study by the U.S. government found that in 2014 US data centers consumed about 70 billion kilowatt-hours of electricity, making up two percent of the country’s total energy consumption. A custom, turnkey server solution built for your specific server needs will maximize performance while greatly reducing the excess energy expenditure, which means lower long term OpEx.

In addition, while some businesses can expect higher initial cost for these customized server solutions, that may not always be the case. For example, if you are on the lower end of the storage and processing needs, a custom solution may turn out to be more cost effective than buying a standard platform with much more upgraded hardware than you actually need. The specialization from the software perspective, not to mention the ability to customize software on servers delivered by turn-key solution providers, offer businesses much greater flexibility, higher performance, and overall lower expenditure over the long run.

Niche manufacturers exist to fill a much needed gap between the OCP providers that service larger enterprises and the inefficient standard platforms that many smaller businesses are stuck with. Taking similar machines and similar high-quality components as traditional server manufacturers, they can design unique, customized server hardware that greatly reduces inefficiency, creating a solution that is better for business and better for the environment as well.

Source: https://datacenterpost.com/why-going-green-doesnt-have-to-mean-feeling-blue-by-alex-lesser-executive-vice-president-at-pssc-labs/

PSSC Labs Featured in Cloud Tech News: Four industries where on-premises infrastructure beats the cloud

By marketing@site-a.com

Cloud computing has fundamentally transformed information technology by offering enterprises an allegedly cheaper, more flexible, and relatively maintenance-free alternative to purchasing their own IT infrastructure. However, despite the mad rush to migrate to the cloud, cloud solutions are not always the best or least expensive choice, particularly in industries that work with very large, complex data sets, perform many intricate mathematical calculations, possess invaluable digital intellectual property, or are subject to certain compliance standards.

Let’s take a look at four industries where in-house IT infrastructure still beats the cloud.

Cybersecurity

In the cybersecurity field, the ability to process extremely large data sets as quickly as possible is crucial, especially as cyber attacks shift away from “lone wolf” one-off hacks and towards highly organized, sophisticated operations carried out by well-trained cyber criminals. IBM estimates that the average organization encounters an average of 200,000 security event alerts each day. An enormous amount of computing power is required to run the SIEM systems that not only detect those anomalies, but also analyze them, separate the false positives from the possible attacks, and deliver actionable information to security analysts – way more power than even the most robust cloud solution could possibly deliver.

In addition to latency problems, the amount of bandwidth consumed would become very expensive, very quickly, making on-premises equipment the most cost-effective solution over time. On-premises cybersecurity hardware also prevents chain-reaction situations where client organizations end up getting hacked because the cloud vendor that their cybersecurity provider was using did.

Adtech

The internet has transformed the way in which consumers and businesses shop. Long buying cycles have been replaced by “just-in-time,” last-minute purchasing decisions, and advertising-weary prospects are using ad blockers, email filters, and DVR fast-forward functions to tune out traditional advertising. To reach these elusive prospects, companies are turning to ad tech, which employs extensive market research and big data analytics to deliver highly targeted marketing messages to qualified prospects at the precise time that they are ready to buy.

The complex data sets and high-level analytics that power the ad tech industry require computing power that is already beyond what any could solution could offer. As the industry matures, ad tech firms will require even more power and more storage space. On-premises equipment can be scaled much more quickly than cloud solutions, and latency problems are avoided.

The ad tech industry also faces intellectual property issues. As Amazon, Google, and other cloud providers enter the market research and ad tech spaces themselves, questions arise as to the safety of digital intellectual property stored on a competing company’s cloud service. Dropbox listed Amazon’s move into the file-sharing space as one of the reasons why it decided to ditch AWS for its own equipment.

Life sciences

The life sciences industry is grappling with a “data avalanche” of clinical results, disease states, scientific studies, and individual patient data, which results in stratospheric cloud bills and latency problems. Because researchers work on limited budgets, simply storing all of this data on the cloud could deplete a project’s funding – and that’s before anything is actually done with it.

Cyber security and compliance issues also come into play due to the sensitive nature of this data. Storing patient data on the cloud may result in an organization running afoul of HIPAA and other privacy regulations if the cloud provider gets hacked, even if the hack turns out to be the provider’s fault.

Finally, on-premises hardware, unlike cloud solutions, keeps running even if the internet is down, which makes on-site equipment a must for scientists who are performing research in remote areas where internet access is spotty.

Design and engineering

Much like cyber security, ad tech, and the life sciences, design and engineering involves performing intricate calculations on very large, complex data sets, as well as running memory-intensive software and generating terabytes of new data every day. A cloud solution would be both wildly expensive and far too slow. In particular, cloud servers offer very poor interdomain communication; a physical server equipped with a high-performance communication architecture such as Intel’s Omni-Path is less expensive than a cloud solution is less expensive and offers low communication latency, low power consumption, and a high throughput.

Design and engineering companies also face a constant threat from digital intellectual property theft; everything from new product prototypes to R&D data could be targeted by competitors, cyber extortionists, or even foreign governments. Digital IP is simply too sensitive to be stored in the cloud, especially since hackers are increasingly targeting cloud providers as the industry grows.

Beyond cloud-first hype, a balanced approach

Despite its drawbacks, cloud computing does have a place in many organizations’ IT ecosystems. Many companies use on-premises equipment to handle standard functions and store highly sensitive data and employ cloud solutions when they require additional capacity or to store less-sensitive data.

Instead of migrating to the cloud because it’s trendy, and “everyone” says it costs less and offers more flexibility than in-house equipment, enterprises should take a step back, consider their individual computing needs, and perform an objective cost analysis.

Source: https://www.cloudcomputing-news.net/news/2017/dec/04/four-industries-where-premises-infrastructure-beats-cloud/

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