The Best GPUs for Scientific Research

Graphics processing units (GPUs) are used extensively in scientific computing. They provide the power to perform complex calculations and processor-intensive tasks like training machine learning models.

Adding virtually any GPU will deliver enhanced computing capabilities to support scientific research. However, the characteristics of a specific graphics processing unit may make it more suitable for handling the large datasets and complex calculations required to run scientific applications effectively.

Why Graphics Processing Units Are Used in Scientific Computing

The scientific community utilizes GPUs to provide additional computing power that is unavailable from standard central processing units (CPUs). CPUs are excellent at processing the sequential instructions traditionally used in general computer programming, but GPUs are capable of efficiently processing multiple instructions simultaneously to streamline many scientific computing tasks

Several factors make GPUs the correct type of processor for scientific computing. GPUs are typically used along with a CPU. The following are the most important reasons for deploying the computing power of GPUs to augment or replace a CPU.

Parallel processing capabilities

GPUs are constructed with up to thousands of small processing cores. Unlike CPU cores, which process instructions sequentially, GPU cores can operate on multiple tasks simultaneously. The parallelism provided by GPUs is essential for performing simulations efficiently, optimizing matrix operations, and training deep learning applications.

High throughput and enhanced performance

Manufacturers design GPUs with fast memory bandwidth and large memory capacity to limit slow I/O operations. This native memory, combined with a GPU’s parallel processing power, provides high throughput and enhanced performance. Large-scale calculations necessary for complex simulations can often be parallelized and run more efficiently with GPUs.

Scalability

Scientific research addresses complex and large-scale problems that traditional CPUs cannot solve. The scalability available with GPUs enables scientific computing to utilize systems powered by multiple processors for better speed and performance. Cloud providers offer customers dynamically scalable GPU environments to handle intensive computations and large volumes of data.

Energy efficiency and cost-effectiveness

A GPU can execute more operations than a CPU while consuming the same amount of power. This feature makes GPUs a logical and economically sound choice for the processing demands of scientific computing. A single GPU can typically outperform a CPU cluster, providing a cost-effective method of accelerating scientific processing.

Software support

Developers can create code optimized for GPU hardware due to the availability of CUDA libraries and other software development platforms like OpenCL. Software for specialized scientific fields such as physics or molecular dynamics is often tailored to take advantage of GPU acceleration.

Scientific Applications Leveraging the Computing Power of GPUs

The scientific community relies on the processing power and high-performance computing provided by graphical processing units for a wide variety of tasks and applications across numerous fields. The following are examples from some of the scientific disciplines utilizing GPU acceleration to support and streamline research.

Machine learning and deep learning

Researchers train machine learning and deep learning models using very large datasets. GPUs can process this large volume of data more efficiently than CPUs and are an integral part of the growth of artificial intelligence applications. Development frameworks like TensorFlow are optimized for GPUs and can significantly reduce the time required to train deep neural networks.

Reinforcement learning algorithms used for training deep learning models run faster on GPUs. GPU acceleration is vital for training complex applications leveraging natural language processing (NLP) for speech and image recognition. The parallelism provided by GPUs is essential for the continued development of machine and deep learning applications.

Simulations across multiple fields

GPUs’ speed, parallelism, and processing power are essential for running complex simulations in various scientific fields.

• Simulations are used to model complex systems and promote research in physics and engineering. Research disciplines leveraging GPU-accelerated simulations include molecular dynamics and computational fluid dynamics.
• Astrophysical simulations allow scientists to study complex phenomena like black hole behavior and star formation.
• Quantum mechanics uses Monte Carlo simulations to study molecular systems to improve material and drug design.
• Biomedical research utilizes simulations for protein structure prediction to study diseases and develop new drugs.

Healthcare and medical imaging

The healthcare field in general and medical imaging specifically process large volumes of data to perform research and diagnostics. Medical research benefits from the speed of GPU-accelerated applications, which allow timely and accurate data analysis.

GPUs support deep learning models that perform computer-aided diagnosis and automatically detect abnormal conditions such as tumors and fractures. The models minimize diagnostic time and improve patient care. Healthcare providers also use deep learning models running on GPUs for predictive analytics regarding disease progression and patient outcomes.

Genomics requires the processing of massive datasets to identify and study DNA sequences. GPUs dramatically speed up this processing, allowing for more efficient and relevant research.

Meteorology and climate modeling

Meteorology and climate modeling study highly complex systems that can be significantly affected by slight variations in conditions. GPUs accelerate the simulations used to forecast weather and storm patterns. Large datasets are used for climate models so researchers can evaluate the long-term effects of climate change.

Data science and analytics

Data scientists use GPUs to facilitate big data analytics by processing large volumes of information. Analytics platforms like Hadoop have GPU-accelerated versions that take advantage of advanced hardware to simultaneously process data from multiple sources. Big data is the foundation of scientific visualizations across a wide range of fields.

Characteristics of the Best GPUs for Scientific Computing

GPUs are complex entities designed to deliver the power required by high-performance computing. Leading manufacturers like NVIDIA, AMD, and Intel differentiate their products with proprietary engineering and distinct architectures. Different GPU models address the needs of distinct user groups, such as gamers, business people, and scientists.

The following are some characteristics of a graphics processing unit that make it a good choice to handle scientific computing requirements.

Parallel processing capabilities

Parallel processing is essential for many scientific computing applications. Parallelism enables the processing of large data sets or running molecular simulations to be performed more efficiently than with a CPU. In some cases, parallel processing offers the only method of obtaining timely results from scientific calculations.

GPUs with many cores are better equipped to manage simultaneous operations and improve processing speed. Generally, more cores equate to enhanced parallelism. However, GPU manufacturers’ architectures may make it difficult to directly compare models based on their number of cores. Users engaged in scientific computing should look to maximize the number of cores when choosing a GPU.

GPU memory

High GPU memory bandwidth and memory capacity are essential for processing scientific workloads. Fast memory is critical to support the intensive computations required by simulations such as molecular dynamics or climate modeling. Scientific applications often rely on quickly and efficiently processing massive datasets. Data transfer speed can be dramatically affected by failing to address a GPU’s memory requirements.

Memory bandwidth

Memory bandwidth is measured in gigabytes per second (GB/s). Higher bandwidth minimizes bottlenecks and enables data to be efficiently moved in and out of memory. GPUs for scientific workloads should offer high-bandwidth memory such as GDDR6, HBM2, or HBM3. These technologies provide bandwidths ranging from several hundred to thousands of GB/s.

Memory capacity

Memory capacity is instrumental in supporting the data parallelism many scientific workloads require. Higher GPU memory capacity allows large datasets to be processed more efficiently and reduces time-consuming I/O operations. GPUs should have a minimum of 24 GB of internal memory for deep learning, medical research, or other scientific endeavors.

GPU acceleration

GPU acceleration benefits many types of scientific computing applications. Tensor cores are specialized hardware units designed to accelerate matrix operations. NVIDIA developed and introduced tensor cores and features them in their high-end GPUs. These cores are responsible for the speed demonstrated by GPU-accelerated applications.

GPUs equipped with tensor cores are excellent at scientific computing applications relying on matrix-heavy calculations, such as machine learning workflows, training neural networks, and supporting complex simulations. Scientific groups working in these disciplines should consider taking advantage of the GPU acceleration that is possible with tensor cores.

Double precision support

Many types of scientific applications require the accuracy of double-precision floating-point arithmetic (FP64). Fields like medical research, scientific visualization, and climate modeling benefit from the enhanced accuracy provided by FP64. All GPUs offer single-precision accuracy (FP32), but GPUs designed for the consumer market may offer weak FP64 support.

FP32 calculations are typically faster than FP64 but may not provide the accuracy necessary for a specific workload. GPUs optimized for FP64 performance should be utilized for scientific computing tasks that perform intensive computations like differential equations. NVIDIA and AMD have developed GPU models that optimize FP64 performance.

Power efficiency

Scientific computing requires power efficiency and effective thermal management. Workloads like producing complex simulations or computationally intensive calculations can run for long periods of time. A power-efficient GPU improves system stability and minimizes heat generation.

Excessive heat can damage hardware and negatively impact system performance. Groups with high performance computing tasks should opt for a modern GPU with native power management features. Additional air or liquid cooling solutions may be necessary for multi-GPU configurations.

Scalability

Taking advantage of multi-GPU configurations can improve the performance of scientific computing applications. Complex simulations or tasks involving massive datasets may need more power or memory than a single GPU can provide. Deploying multiple GPUs can substantially reduce the training time of deep learning models.

A multi-GPU environment allows more than one GPU to communicate at high speeds for improved system performance. NVIDIA NVLink and the AMD Infinity Fabric support communication between GPU nodes. Scientific computing users should look for scalable GPU models that support distributed environments.

Compatible software environment

Scientific computing groups should look for GPUs that integrate with an existing software environment or framework. Familiarity with the framework eliminates a learning curve and streamlines productivity. Teams should ensure the GPUs they select are compatible with their current environment.

CUDA from NVIDIA is the leading framework for scientific and high-performance computing. ADM’s ROCm framework is similar to NVIDIA’s CUDA libraries and is gaining popularity for artificial intelligence and scientific applications. Development frameworks like OpenMP and TensorFlow have built-in GPU support.

The Best GPU Models for Scientific Computing

GPUs for scientific computing should demonstrate many or all previously discussed characteristics. GPU manufacturers are continuously refining and introducing cutting-edge technology to their products. The best GPU for a specific scientific application today may be surpassed by tomorrow’s innovations.

Considering those issues, the following are some of the best GPUs for scientific computing. They are generally the most powerful and advanced devices available from the major GPU manufacturers.

NVIDIA GPUs for scientific computing

NVIDIA produces multiple GPUs with the characteristics that make them an excellent choice for scientific computing.

NVIDIA H100 Tensor Core GPU

The H100 Tensor Core GPU is designed for data center use and is built using the Hopper architecture. The device has up to 120 GB of high-speed HBM3 memory and 14,592 CUDA cores to support cutting-edge parallel processing. The H100 supports advanced AI workloads and scientific simulations with 30 TFLOPS performance for FP64 precision calculations.

NVIDIA RTX 6000 Ada Generation

This GPU is designed to power AI workflows from desktop workstations. It is built on the NVIDIA Ada Lovelace architecture and combines 142 third-generation RT Cores, 568 fourth-generation Tensor Cores, and 18,176 CUDA cores with 48GB of graphics memory. The GPU delivers excellent graphics, AI, and compute performance to address a wide range of scientific applications.

NVIDIA L40

The NVIDIA L40 features 48 GB of high-speed GDDR6 memory and has 18,176 CUDA cores and 58 Tensor cores to handle scientific applications requiring parallel processing. The L40 is a versatile GPU capable of powering AI and machine learning workflows for business and scientific users.

AMD GPUs for scientific computing

AMD’s GPUs utilize a different architecture than NVIDIA. The devices provide parallelism with stream processors rather than CUDA cores. The following models address scientific computing requirements.

AMD Instinct MI250X

The Instinct MI250X accelerator is designed to supercharge high-performance computing, AI, and scientific workloads. It provides advanced parallelism with 14,080 stream processors (cores) and 128 GB of HBM2 memory. The GPU excels at double-precision math with 220 compute units.

AMD Instinct MI300

The Instinct MI300 series accelerators can handle the most demanding scientific workloads. With 304 compute units and up to 256 GB of HBM3 memory, they are equally well-suited for AI and HPC workloads. Memory bandwidth of up to 6 TB/s provides lightning-fast performance for data-intensive applications.

Atlantic.Net’s Hosted NVIDIA GPU Solutions

Atlantic.Net’s hosted NVIDIA GPU solutions offer customers versatile systems suitable for scientific computing. Customers can choose from a system built around the NVIDIA H100 NVL GPU or the NVIDIA L40S GPU to power AI and scientific workloads without building an on-premises infrastructure.

Contact us today to start supercharging your scientific computing applications.