Cloud HPC Advances Healthcare Research

2025-06-13 18:31:00

High-Performance Computing Powers Medical Advances

Healthcare researchers are leveraging on-demand high-performance computing (HPC) to accelerate breakthroughs in cardiovascular disease and other critical areas.

• On-demand HPC services offer immediate access to the latest hardware, bypassing lengthy setup times.
• Researchers can create customized HPC clusters, controlling resources and costs efficiently.
• HPC is enabling unprecedented analysis of complex biological structures, like LDL particles.
• Cloud-based solutions provide the scalability needed for large datasets and computationally intensive tasks.

How can high-performance computing help advance medical research? HPC, particularly through cloud services, provides the necessary computing power and resources to analyze massive datasets, accelerating discoveries in areas like cardiovascular disease. Services such as those offered by AWS are transforming how researchers tackle complex challenges.

While traditional HPC clusters have benefits, setting one up can take up to seven or eight months. By the time an institution procures the needed hardware and gets everything running, the technology might already be outdated. Moreover, procuring the necessary graphics processing units can be difficult.

Did You Know? Setting up a traditional HPC cluster can take 7-8 months, perhaps leading to outdated technology by the time it’s operational. Cloud-based HPC solutions offer a faster, more agile alternative.

AWS offers healthcare organizations several HPC options. The AWS Parallel Computing Service is a fully managed Simple Linux Utility for Resource Management cluster. Researchers can create a SLURM cluster that fits their specifications, such as processor types and latency needs, within 20 minutes. The user controls the compute nodes and builds the node groups themselves. Additionally, one can run a native or containerized app on AWS with the SLURM scheduler.

“You can create a compute surroundings that can run up to 100,000 CPUs, but if you ask for onyl two CPUs, that’s how much you’ll be charged for,” said Xu.”It’s on demand. You pay for what you use.”

An alternative is AWS ParallelCluster, for researchers who wont full control of the SLURM scheduler and its plug-ins. It’s an open-source solution that lets the user create a fully customized HPC cluster in the cloud that they manage themselves.

Researchers can choose from over 800 types of HPC instances. Resources such as amazon FSx for Lustre and Amazon File Cache can assist with HPC goals.

“We don’t want you to waste any resources, so you only pay for what you use,” said Xu.

RELATED: Follow three steps to successfully deploy high-performance computing.

Unraveling Cardiovascular Disease with HPC

Cardiovascular disease is the leading cause of human mortality globally. In 2019,18.6 million people died of the disease worldwide. Having a high amount of low-density lipoprotein in the blood increases the risk of cardiovascular disease. These LDL particles can build up in the blood, deposit on artery walls, and form plaques, potentially leading to heart attack or stroke.

Key Statistic: Cardiovascular disease caused 18.6 million deaths globally in 2019, highlighting the urgent need for advanced research and treatment methods.

In the U.S., 30% to 40% of the population over 50 takes statins to treat high cholesterol, according to Marcotrigiano. Statins target the receptor,not the particle itself.To understand the particle better, scientists at the NIH recently used HPC and cryo-electron microscopy to model the LDL particles, a process that was once thoght unfeasible, Marcotrigiano said.

Modeling these particles required vast amounts of data.One dataset contained 35,000 movies and about 17.5 terabytes of data. The movies also needed to be compressed into high-resolution images. Researchers aligned particles based on similarities and differences, classifying particles from a sample using both 2D and 3D systems.

This work provides a better understanding of how the particle binds to receptors, which will aid in developing new therapies targeting the particle itself.

“The only place we could do this was in the cloud,” said Marcotrigiano, adding that NIH used Amazon FSx for Lustre and several GPUs to process and store the data for this project.

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High-performance computing (HPC) is vital for advancing medical research,but its potential extends far beyond cardiovascular disease. HPC is a powerful tool, and its application is growing rapidly across various fields of medicine, including genomics, drug finding, and medical imaging. These computational resources are pivotal in addressing complex challenges and accelerating breakthroughs that can revolutionize healthcare as we know it.

Beyond Cardiovascular: other Medical Applications of HPC

The capacity of HPC to process and analyze vast datasets makes it invaluable in several other areas of medical research. Consider these applications:

• Genomics Research: HPC enables the analysis of massive genomic datasets, wich is crucial for understanding genetic diseases, personalized medicine, and drug response prediction.
• Drug Discovery: Researchers use HPC to simulate drug interactions, identify potential drug candidates, and accelerate the drug advancement process.
• Medical Imaging: HPC enhances medical image analysis,including applications in radiology,helping to improve the accuracy of diagnoses and treatment planning.
• Cancer Research: HPC helps model cancer cells, understand tumor behavior, and develop tailored cancer treatments.
• Infectious Disease Research: HPC allows scientists to simulate the spread of infectious diseases,enabling the development of effective prevention and treatment strategies.

The ability of HPC to handle these tasks is transforming medical research into a more data-driven discipline.

Benefits of Cloud-Based HPC for Medical Research

Cloud-based HPC offers a number of advantages over traditional, on-premises solutions, especially relevant in the fast-moving world of medical research. These advantages include:

• Scalability: cloud HPC can adjust to fluctuating needs, ensuring access to sufficient computing resources without the need for important upfront investment.
• Cost-Effectiveness: Cloud services follow a pay-as-you-go model, reducing unnecessary expenses and allowing researchers to optimize their budgets.
• Accessibility: Researchers can access cloud resources from anywhere, fostering collaboration and facilitating remote work.
• Latest Technology: Cloud providers continuously update their hardware and software, ensuring researchers have access to the latest tools and technologies.

These features empower medical research teams to pursue aspiring projects that were once unachievable. Cloud HPC solutions provide scalability, adaptability, and cost-effectiveness, which are critical for researchers dealing with large datasets and complex analyses.

Real-World Examples: HPC in Action

Many institutions and companies are successfully utilizing HPC to advance medical research.

• NIH (National Institutes of Health): As demonstrated in the cardiovascular research exmaple discussed earlier, the NIH utilizes HPC extensively for various projects, including genomics, proteomics, and drug discovery.
• Pharmaceutical Companies: Organizations such as Pfizer and Roche leverage HPC to accelerate drug development, simulate clinical trials, and improve drug efficacy.
• Academic institutions: Universities like Harvard and Stanford use HPC resources for projects in genomics, biomedical engineering, and public health.

These are a few examples of medical research institutions that are using of HPC to improve healthcare.

Myths vs. Facts: HPC in Medicine

it’s crucial to clear up common misconceptions about HPC in medicine.

Myth: HPC is only for large institutions with big budgets.

Fact: Cloud-based HPC solutions have made advanced computing accessible and affordable for research teams of all sizes.

Myth: HPC is arduous to learn and use.

Fact: Cloud providers offer user-pleasant interfaces and support, making it easier than ever to access and utilize HPC resources.

Myth: Traditional HPC clusters are always the best option.

Fact: Cloud HPC provides a significantly faster and more flexible option compared to traditional clusters.

Frequently asked Questions (FAQs)

Here are some frequently asked questions about HPC and its application in medical research. These provide more context.

Q: What are the main barriers to adopting HPC in medical research?
A: The primary barriers include the need for specialized skills,concerns about data security,and the initial costs associated with setting up and maintaining infrastructure.

Q: How can researchers overcome the requirement of for specialized skills when utilizing HPC?
A: Cloud providers offer user-friendly interfaces, pre-configured applications, and technical support to simplify the adoption of HPC.

Q: What are the data security measures one must consider when using cloud-based HPC?
A: Cloud providers employ robust security measures such as data encryption, access controls, and compliance certifications to protect sensitive data.

Q: Is it possible to integrate HPC into existing research workflows?
A: most cloud-based HPC solutions integrate with popular scientific tools and workflows, making it easier for researchers to transition and use their existing tools.

Q: What is the future of HPC in medicine?
A: The future involves greater integration of HPC with AI, machine learning, and other advanced technologies to yield previously unthinkable medical breakthroughs and a better understanding of diseases.