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.