HPC Migration in the Cloud: Getting it Right from the Start
High-Performance Computing (HPC) has long been an incredible accelerant in the race to discover and develop novel drugs and therapies for both new and well-known diseases. And a HPC migration to the Cloud might be your next step to maintain or grow your organization’s competitive advantage.
Whether it’s a full HPC migration to the Cloud or a uniquely architected hybrid approach, evolving your HPC ecosystem to the Cloud brings critical advantages and benefits including:
- Flexibility and scalability
- Optimized costs
- Enhanced security
- Compliance
- Backup, recovery, and failover
- Simplified management and monitoring
And with incredibly careful planning, strategic design, effective implementation and with the right support, the capabilities and accelerated outcomes of migrating your HPC systems to the Cloud can lead to truly accelerated breakthroughs and drug discovery.
But with this level of promise and performance, comes challenges and caveats that require strategic consideration throughout all phases of your supercomputing and HPC development, migration and management.
So, before you commence your HPC Migration from on-premise data centers or traditional HPC clusters to the Cloud, here are some key considerations to keep in mind throughout your planning phase.
1. Assess & Understand Your Legacy HPC Environment
Building a comprehensive migration plan and strategy from inception is necessary for optimization and sustainable outcomes. A proper assessment includes an evaluation of the current state of your legacy hardware, software, and the data resources available for use, as well as the system’s capabilities, reliability, scalability, and flexibility, prioritizing security and maintenance of the system.
Gaining a deep and thorough understanding of your current infrastructure and computing environment will help identify technical constraints or bottlenecks that exist, and inform the order that might be necessary for migration. And that level of insight can streamline and circumvent major, arguably avoidable, hurdles that your organization might face.
2. Determine the Right Cloud Provider and Tooling
Determining the right HPC Cloud provider for your organization can be a complex process, but an irrefutable critical one. In fact, your entire computing environment depends on it. It involves researching the available options, comparing features and services, and evaluating cost, reputation and performance.
Amazon Web Service, Microsoft Azure, and Google Cloud – to name just the three biggest – offer storage and Cloud computing services that drive accelerated innovation for companies by offering fast networking and virtually unlimited infrastructure to store and manage massive data sets the computing power required to analyze it. Ultimately, many vendors offer different types of cloud infrastructure that run large, complex simulations and deep learning workloads in the cloud, and it is important to first select the one that best meets the needs of your unique HPC workloads between public cloud, private cloud, or hybrid cloud infrastructure.
3. Plan for the Right Design & Deployment
In order to effectively plan for a HPC Migration in the Cloud, it is important to clearly define the objectives, determine the requirements and constraints, identify the expected outcomes, and a timeline for the project.
From a more technical perspective, it is important to consider the application’s specific requirements and the inherent capabilities including storage requirements, memory capacity, and other components that may be needed to run the application. If a workload requires a particular operating system, for example, then it should be chosen accordingly.
Finally, it is important to understand the networking and security requirements of the application before working through the design, and definitely the deployment phase, of your HPC Migration.
The HPC Migration Journey Begins Here…
By properly considering all of these factors, it is possible to effectively plan for your organization’s HPC migration and its ability to leverage the power of supercomputing in drug discovery.
Assuming your plan is comprehensive, effective and sustainable, implementing your HPC migration plan is ultimately still a massive undertaking, particularly for research IT teams likely already overstretched or for an existing Bio-IT vendor lacking specialized knowledge and skills.
So, if your team is ready to take the leap and begin your HPC migration, get in touch with our team today.
The Next Phase of Your HPC Migration in the Cloud
A HPC migration to the Cloud can be an incredibly complex process, but with strategic planning and design, effective implementation and with the right support, your team will be well on their way to sustainable success. Click below and get in touch with our team to learn more about our comprehensive HPC Migration services that support all phases of your HPC migration journey, regardless of which stage you are in.
I know we may not be ready to think about this.
Nearly a year after the first case was reported, we are still deep in the throes of a global pandemic. And although we saw the two highly effective vaccines, not to mention a range of promising treatments, developed and approved in record time, distribution of those vaccines is still in its early phase and we are far from in the clear. Because various new strains and mutations are showing up as well, instead of one target we are now tracking toward several.
Thinking about the next deadly virus lurking deep in the jungle—what it may be and worse, what it may cause—is not only terrifying, it’s also tiring.
Nevertheless, we must. It’s not a matter of if another novel virus with deadly potential will be discovered, it’s when.
Fortunately, we’re not alone in this fight.
Research scientists have the super-human power of supercomputers to help their teams get ahead of the next outbreak and gain an edge not only for their company’s profit but also for their own survival.
Here’s why.
This discipline of high-performance computing (HPC) focuses on matching resource-intensive computing processes with the technical architecture best-suited for the task, enabling research capabilities not otherwise possible by humans alone.
Emerging technologies that rely on AI, ML, and DL algorithms, like many of those that were invaluable to R&D teams in their quest to understand and overcome COVID-19, often require immense computing power to produce results. Bio-pharmaceutical research tasks can easily require millions or billions of parameters to generate useful results, which is far beyond the processing capabilities of most on-premises computing systems (and those tasks can be too few and far in between for the need to own large server farms to do them).
High-Performance Computing Improves Model Rendering and Predictive Analysis
Today, HPC is being used effectively through all phases of the drug-development life cycle.
One way, in particular, is through model rendering; scientists are using high-performance computing to build models of biological and chemical structures for analysis, inventing compounds, and performing analysis on whether those compounds will treat symptoms or cure a disease before ever reaching a patient. This ability to simulate conditions for trials programmatically increases the predictability of drug success and produces better trial outcomes that ultimately accelerates the speed at which a drug can be brought to market. And help people who are sick.
But, as useful as model rendering is for attacking diseases we know about, it also has the potential to provide insight into diseases and disease mutations we don’t yet know about, docking ultra-large libraries to discover new chemotypes and supporting the multitude of scientific tools that require HP for handing its computing to improve its performance.
But even labs that already have access to supercomputing hardware may find themselves disadvantaged when running particular processes.
Luckily, hardware optimization and Cloud integration can eliminate processing bottlenecks, and allow flexibility in handling challenges when dealing with constantly changing demands of task size to frequency, commonly encountered in laboratories with frequent large-scale computing needs.
Some computer-aided drug design tasks perform best under high-speed single-thread CPU architectures. Others are better served by massively parallelized graphical processing units (GPUs) with multiple-thread architectures. Every particular computing problem has its own unique optimal resolution architecture.
Optimizing Your HPC Environment for the Unknown
A research lab with limited supercomputing access (and limited human resources) will not be able to optimize its computing resources to solve all of its computing problems effectively. It will be able to solve some processes quickly while others suffer from significant inefficiencies, whether that’s a result of insufficient computing power or access to it.
When it comes to the large-scale computing resources that emerging technologies demand—technologies essential to preventing or at least minimizing the destruction of the next outbreak—these inefficiencies can become roadblocks to progress. In fact, it’s not unusual for ultra-large library docking processes to require tens of thousands of core hours to complete. Having access to Cloud systems with thousands of optimized cores (that can expand to your needs) could mean the difference between waiting for weeks or hours for the result. And when lives are at stake, weeks become an eternity.
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 more than 30 years. If you’re interesting in learning how RCH can support your goals, get in touch with us here.