Analysis of Computing Power Application Scenarios and Demand in Scientific Research: The New Paradigm Revolution Driven by AI

The integration of supercomputing and artificial intelligence is reshaping the boundaries of scientific research.

Jim Gray once proposed four paradigms of scientific research: empirical science, theoretical science, computational science, and data science. Today, artificial intelligence technology is catalyzing the birth of the "Fifth Paradigm" in scientific research.

Under this new paradigm, the approach to scientific research is undergoing fundamental changes: drug development cycles have been shortened from years to hours, material discovery efficiency has increased by 40 times, and typhoon path prediction errors are only 38 kilometers—all of these have become reality.

01 Hyper-Intelligent Convergence: Accelerating Transformation in Research Paradigms

In the past, scientific discovery relied on human intuition and repeated experimentation; now, the combination of artificial intelligence and supercomputing is accelerating this process by several orders of magnitude.

In recent years, experts in the scientific research field generally believe that the deep integration of supercomputing and AI is catalyzing a new paradigm in scientific research.

Industry experts point out: "With the successful development of a new generation of supercomputers, the deep integration of computing and intelligent technologies is bringing new opportunities for transformative changes in scientific research paradigms."

This transformation is not only occurring at the theoretical level but has also been translated into improvements in actual scientific research productivity.

Globally, accelerated computing has become mainstream. In 2019, nearly 70% of the TOP100 high-performance computing systems were CPU-only, but today this number has plummeted to below 15%.

The vast majority of TOP100 systems have adopted accelerated computing architectures, with most relying on GPUs for core computing power support.

02 Typical Application Scenarios of Computing Power in Scientific Research

Life Sciences and Healthcare

In the field of life sciences, computing power is driving breakthroughs in unprecedented ways.

A research team's exploration of fetal ultrasound large models has increased the accuracy of fetal ultrasound diagnosis to over 91%, while reducing the time required by 60%.

By establishing an intelligent diagnosis and quality control service platform, the traditional screening process that took at least 30 to 40 minutes, with low efficiency and high rates of missed and misdiagnoses, has been addressed.

A medical institution developed a "Critical Care Diagnosis and Treatment Large Model" that covers the entire process from patient admission, in-department care, treatment plan development, to prognosis management.

While it cannot replace doctors' decisions, it can provide treatment plans, nutritional advice, interpretation of examination data, etc., effectively reducing the burden on medical staff and improving treatment efficiency.

Basic Scientific Research

In the field of high-energy physics, large particle collider facilities generate massive amounts of data every second.

The difficulty of capturing rare, high-value physical signals from such vast amounts of data is considerable.

With the help of professional tool scheduling systems, researchers can automatically decompose and efficiently plan particle physics research tasks, generating analysis programs that cover all stages of the particle physics workflow.

Scientific foundation large models released by research institutions have systematically mastered the core theorems, laws, and professional knowledge of the six major disciplines of mathematics, physics, chemistry, astronomy, earth science, and biology, and have achieved in-depth understanding of various scientific modal data such as waves, spectra, and fields.

Materials Science and Chemistry

In the field of materials research and development, computing bottlenecks are particularly evident. A survey in materials science and computational chemistry shows that 94% of research teams abandoned at least one planned simulation project in the past year because it exceeded expected runtimes or computing budgets.

These figures clearly indicate that computing capacity is a limiting factor for research output.

Approximately 52% of teams stopped 1 to 5 projects, while 41% abandoned 6 to 10 projects. These numbers paint a stark reality for an industry reliant on fast turnaround times.

Almost all teams reported abandoning projects, expressed concerns about intellectual property protection, and felt constrained by computing and workflow complexities.

Climate and Environmental Science

In climate research, the combination of supercomputing and AI is significantly improving prediction accuracy.

Scientific models created by machine learning methods have been able to control typhoon path prediction errors to just 38 kilometers.

The application of environmental monitoring technology can accurately measure various changes such as temperature, pressure, vibration, sound, and gas concentration, enabling urban safety monitoring and intelligent detection of various pipeline leaks.

03 Analysis of Computing Power Demand Characteristics in Scientific Research

Diversified Demand

Computing power demands in scientific research show clear multi-level and diversified characteristics.

Leading research institutions typically divide computing resources into three main types: fragmentary computing power, entire machine computing power, and public computing power pools to accurately meet the computing needs of different scenarios.

• Fragmentary Computing Power meets small-scale computing demands like single or half cards, suitable for lightweight tasks in daily research and teaching

• Entire Machine Computing Power provides computing power of single or multiple entire machines, supporting heavy tasks like large model fine-tuning and reinforcement learning

• Public Computing Power Pools deploy open-source large models, providing large model interface services for various disciplines

Severe Computing Power Bottlenecks

Currently, computing power shortages have become a key factor constraining research innovation.

According to reports in authoritative journals, a survey of scholars from dozens of institutions worldwide shows that many researchers are frustrated by the limited computing resources available for artificial intelligence research.

66% of respondents rated their satisfaction with computing power at 3 or below (on a 5-point scale).

The lack of access to state-of-the-art computing systems in academia may hinder the development of large language models and other AI research.

In particular, academic researchers struggle to obtain sufficiently powerful computing resources, as the computer chips needed to train AI models typically cost thousands of dollars.

Significant Return on Investment

Despite facing computing power bottlenecks, research institutions achieve remarkable returns on computing power investments.

A survey shows that simulation saves approximately $109,000 per R&D project on average, primarily due to reduced reliance on experimental work.

About one-third of respondents stated that each project could save over $100,000.

Most institutions also allocate considerable annual budgets for simulation—about half of respondents spend between $200,000 and $500,000 annually on simulation, while slightly less than a quarter spend between $500,000 and $700,000 per year.

04 Innovations in Computing Power Supply Models for Scientific Research

Computing Power Platform Construction

Research institutions are actively building advanced computing power infrastructure.

Through years of exploration and practice, leading research institutions have created integrated computing power platforms that carry "cloud computing, intelligent computing, and supercomputing".

The total computing power scale of some institutions has reached hundreds of Petaflops.

Such platforms not only undertake research tasks but also deeply serve talent cultivation and informatization needs, becoming important supports for institutional digital transformation.

Open Architecture and Ecosystem Co-construction

Open Computing Architecture has become an important approach to addressing computing power challenges.

Industry experts propose that open computing architecture is a collaborative innovation system for large-scale intelligent computing scenarios.

It uses GPUs as the core for efficient tightly-coupled system design, promoting full-stack innovation across the industry chain from computing, storage, and networking to software.

Leading scientific computing platforms provide precise support for multi-disciplinary research scenarios, significantly lowering the barriers to research innovation and enabling experts without AI backgrounds to get started quickly.

The core capability of this platform lies in compressing the development cycle of scientific large models from months to hours, greatly enhancing research efficiency.

Computing Power Leasing and Inclusive Services

Faced with the fluctuating and urgent computing power demands of research institutions, computing power leasing services have become a cost-effective choice.

Professional computing power cloud platforms provide elastic computing services with GPU computing power, offering strong computing capabilities and supporting flexible resource scaling according to business growth.

They offer mixed billing models including annual and monthly packages, allowing uninterrupted business elasticity to increase AI computing power resources in the cloud beyond basic fixed computing resources.

05 Omniyq Cloud Engine: High-Cost-Effective Computing Power Service Solution for Scientific Research

Addressing the computing challenges faced by research institutions, the Omniyq Cloud Engine computing power leasing platform (www.omniyq.com) has launched a specialized high-cost-effective computing power service and support plan for research institutions.

The platform implements multiple measures to ensure user data security, including data encryption, access control, firewalls, vulnerability scanning, and security audits to prevent risks such as data leakage, tampering, and loss.

These security measures are crucial for research institutions, as surveys show that 100% of research teams have concerns about intellectual property security when using cloud-based or third-party tools.

The platform provides various GPU card types including B200/H200/H100/H800/A800/A100/V100/4090/3090, supporting multiple delivery forms such as cloud hosts, clusters, and bare metal cloud services, meeting various application scenarios including deep learning, scientific computing, and graphic visualization.

Through flexible billing methods and exclusive services, Omniyq Cloud Engine provides research institutions with high-cost-effective computing power solutions.

1v1 exclusive customer service + technical dual-guarantee butler-style service, available 24/7, ensures the persistent and stable operation of research business.

Specifically targeting top research institutions, Omniyq Cloud Engine has launched a Computing Power Support Plan, providing stable and efficient computing power support for scientific computing in the field of artificial intelligence through optimized resource allocation and intelligent scheduling, assisting the smooth implementation of national-level research projects.

Globally, computing power bottlenecks are limiting the pace of scientific discovery. A survey shows that 94% of research teams have been forced to abandon planned projects due to computing resource constraints. Computing power leasing services are becoming a key force in breaking this bottleneck.

Computing power platforms like Omniyq Cloud Engine, by providing high-cost-effective, flexible, and scalable computing power services, are becoming indispensable infrastructure supporting research innovation.

They enable researchers to focus on the scientific problems themselves without worrying about computing resources—this is the true meaning of "inclusive computing power."

In the future, as "hyper-intelligent convergence" continues to deepen, computing power will become a fundamental resource like electricity, promoting the maturation of the Fifth Paradigm of scientific research and unlocking infinite possibilities for human scientific discovery.