High-performance computing is a powerful weapon in the fight to find treatments and cures for all kinds of diseases.  

Big hungry application beasts like Schrodinger, Amber, and Fastrocs, depending on how they are leveraged, provide keen insight on molecular level medicine allowing scientists to craft their own compounds.

Depending on whether you get your data from scopes, tests, databases or another source, it could take days or weeks for a single system to render a job to give you an answer, not to mention the risk of it failing and/or being rebooted, causing massive delays and awkward meetings.

That’s where High-Performance Computing (HPC) comes in. HPC allows you to run your jobs faster, more safely, and across an array of systems, processing the same large-scale and intricate jobs that typically take days for a single system, in mere hours.

And it’s scalable for need and usage. 

But the power of HPC, like with any tool, comes with caveats. 

Because of the complexity of the work it performs, HPC leverages hundreds of different add-on apps and modules to accomplish very specific tasks in a scientific research environment. 

Many of these apps are created by scientific software companies and come with the full support of the company, and its development teams. Others are spun-up by firms or small groups of scientists to fit within their compute environment or perform tasks essential to their process. Still, others are created “one-off” by a single scientist or team to support the specific needs of a project on which they’re working at the time. 

And therein lies the challenge with HPC: Though many of the apps that fall into those last two categories can and do prove to be useful to many scientists, optimizing and running them can be tricky. Without formal documentation, regular updates, and support, users often find themselves facing issues never before experienced by the small group who created the software, and with little recourse.

What’s a scientist to do? Find the person who built it? Figure out how to fix it on their own? Find something comparable that hopefully can accomplish the same goal?   

All of these options are both speculative and time-consuming. 

That’s because computing applications, particularly homegrown open-source apps, can be tricky at best.  

For scientists to properly leverage these tools to move their research forward, not only do the solutions need to be implemented correctly, they need to be held up by a team with deep scientific and technical expertise, ready and able to test, evaluate, and build support knowledge. 

And that is only one of the common challenges. The others are less obvious.

Think of it this way. 

High-Performance Computing, as the name implies, is like a high-power dragster. Its engine is built perfectly to shatter speed records. Its body, carefully designed to cut effortlessly through the air. And its driver trained and skilled to steer it across the finish line gracefully.  

The whole unit is built for high performance. 

In a lab, the same principles must apply. 

Workflow bottlenecks, throughput issues, and job optimization challenges must be eliminated. Additionally, you have to have the right environment upon which to build your HPC system in order for it to function at its best. 

You can choose the cloud, which has many benefits.  The cloud is great for scaling, testing, setting up solutions, burst computing, and multiple rapid setups of arrays, all without system maintenance. 

You can choose a local on-prem build, which gives you more control over horsepower, predictability of costs, and a flexible security profile. (There are pros and cons to both options, so hybrid solutions are often the best choice — but what and how?)  

Either way, and as you can see, there are a number of considerations critical for your ability to leverage the power of supercomputing in drug discovery.  

The Value of an Experienced Partner

Implementing HPC is a massive project, particularly for research IT teams likely already overstretched.  

Hiring more people can be time-consuming and costly. And pulling in a vendor can be risky.  That is, unless it’s an established crew, with extensive experience and knowledge, and a deep bench full of talent.  That saves teams time and money. 

For almost 30 years, RCH Solutions has served that role. We’ve helped life sciences companies of all sizes clear the path to discovery by delivering scientific computing expertise and workflow best practices to support R&D teams.  

If you’re looking for support in your HPC environment, learn how RCH can help your team.  

RCH Solutions is a global provider of computational science expertise, helping Life Sciences and Healthcare firms of all sizes clear the path to discovery for nearly 30 years. If you’re interesting in learning how RCH can support your goals, get in touch with us here. 

Essential Questions to Ask When Evaluating Your Options

Cloud computing is having a fundamental impact on the biotech industry. Tasks that were extremely time consuming or simply not possible even a decade ago can now be performed quickly and efficiently in the Cloud. 

Take big data storage and analysis. Amazon Web Service, Microsoft Azure, and Google Cloud – to name just the three biggest – offer storage and Cloud computing services that allow companies to store massive data sets and provide the computing power required to analyze it.

Affordable access to these powerful tools shortens timelines and allows even small companies to perform tasks that, until recently, were limited to deep-pocket companies that could afford to buy the hardware needed.

Cloud computing solutions also transfer the power of selection and implementation into the hands of the functional areas. IT is no longer the rate-limiting step in implementation; cloud pay-to-play solutions can be turned on just as quickly as credit card information can be transmitted.  

And there are capital considerations as well. The difference is upfront capital expense (CapEx) in on-prem storage, vs. operational expense (OpEx) for the cloud.  Not having to come up with large sums of money immediately is an advantage of the Cloud.

But there is a flip side. In the biotech/biopharma world, compliance with regulatory requirements, such as 21 CFR Part 11, rank high on the list of issues, and Cloud-based systems might not afford the necessary protection. Security is another important consideration. After all, Cloud computing means sharing your company’s sensitive information to a third-party service provider. 

Not to mention, the many benefits to on-premise options, including the ability to tailor your environment to meet very specific company needs. 

For these reasons, conversations centered on the implementation or better execution of Cloud solutions permeate research and IT teams, especially as the working world shifts toward higher adoption of virtual work and collaboration practices. 

If you’re exploring which Cloud vs. on-prem solutions are right for your work and team, consider the following critical considerations before making any moves:

• Business objective. What is the main objective of migrating your business to the cloud, and how will the cloud support your broader R&D or data goals?
• Impact.  How will a migration impact your organization’s ability to maintain productivity, and can you afford outages if needed? 
• Readiness.  Are you prepared to support a cloud infrastructure?  What steps must you take now to ensure compatibility between current on-premise deployments and cloud?
• Workflow. What applications make sense to keep on-premise and which would be ripe for the cloud?  One size (or in this case, storage strategy) does not fit all. 
• Capital.  Have you assessed costs, including expenses related to the dedicated human resources necessary to support the migration?
• Time. Have you thought about realistic timelines and possible roadblocks that could increase migration times?
• Risk Mitigation. What are some known risks or cons that may make you, or your organization, hesitant, and how will your CSP support efforts to reduce risk through all phases of your relationship?
• Security. Will your data be secure? What security protocols can you trust your cloud service provider to follow to ensure you realize the many benefits of the cloud without sacrificing security?  
• Compliance.  Will your cloud service provider meet client compliance?
• Business Continuity and Disaster Recovery.  How will your cloud service provider accommodate and plan for the unknown … a requirement we know all too well following COVID-19. 

And this list could go on. 

The bottom line?  As compelling as a complete move into the Cloud may sound, teams need to carefully consider all the many factors before operationalizing a plan.  And when in doubt, an experienced Cloud computing expert can be the navigator organizations need to ensure the decisions they make are right for their needs and goals. 

You can find more information about how RCH Solutions can help develop your Cloud strategy here.

Looking for support for your AI Initiatives?

RCH Solutions is a global provider of computational science expertise, helping Life Sciences and Healthcare firms of all sizes clear the path to discovery for nearly 30 years. If you’re interesting in learning how RCH can support your goals, get in touch with us here. 

An experienced Bio-IT partner can help you determine if AI is right for your project. At RCH, we’ve helped our customers successfully navigate and leverage an evolving technology landscape to best meet their R&D IT needs for nearly three decades. Talk to us about how we can help you, too.