From the latest and best in gaming, design, and 3D to machine learning, the Graphics Processing Unit (GPU) is an integral part of the mechanics of modern society. For engineers and scientists, processes involving Computational Fluid Dynamics (CFD) simulation services pose many problems. This is because the fluid flow is difficult to achieve with variation in pressure when speed changes, varying density ratio, and compression due to gravity.

Moreover, engineers might face an unorthodox simulation system that needs to be more powerful. CFD simulation services experience a drastic increase in the workload if the model includes multiple conditions simultaneously or if the calculations involve an infinite grid or turbulence modeling.

Let’s explore how using multiple GPUs can improve CFD simulation services and its advantages over other parallel processing methods.

Why You Should Use Multiple GPUs When Running CFD Simulations

A GPU is a graphics processing unit that can be used for tasks other than just graphics rendering. The use of multiple GPUs for parallel computing has increased in recent years. This is because GPUs are becoming more powerful and multifunctional.

This has led to the development of custom CFD simulation services that can take advantage of the power and multifunctionality of GPUs.

Several factors have contributed to the rise and will continue to contribute to the growth in GPU-accelerated computing. These factors include:

• The availability of high-end graphics cards with performance equivalent to that of mid-range CPUs.

• The introduction of new programming models, such as CUDA and OpenCL.

• Open-source C++ libraries such as OpenMM and Thrust.

• The accessibility of high-performance computing clusters with multiple GPUs per node.

The benefits of using multi-GPUs for CFD simulations include the following:

• Less execution time for the computation.

• Increasing parallelism on supercomputers and clusters with multiple GPUs per node.

• Lowering the number of data transfers that are required to process simulations.

• Reducing queuing time for jobs and increasing the time for users to run simulations requiring data access or satellite retrievals.

Most CFD simulation services can be time-sliced to utilize the available computing resources. These applications include solution visualization, such as turbulence visualizations, heat transfer through walls, fluid interaction with machinery and contaminants, and real-time simulations.

Why Do Users Need to Consider a Multi-GPU-Based Solver?

A multi-GPU-based solver is necessary for solving complex engineering and scientific problems. This type of solver can deliver better performance than a single GPU-based solver. Using multiple GPUs on a single node increases the speed of the computations by distributing the load among the GPUs. This is because of the architecture of the GPU, which is optimized for massively parallel processing.

A GPU-based solver is a type of parallel computing which means it solves problems in a reasonable time. A multi-GPU-only solver is a type of solver that uses GPUs to solve an optimization problem. This type of solver is often used to reduce hardware and cloud costs. The multi-GPU-only solver saves money on hardware and cloud costs. Still, the quality and speed of the solution will depend on the hardware.

GPU-Based Solvers in Detail

GPUs have been used to accelerate various applications, including computations in engineering and science. But as the number of cores available on GPUs has increased, designers can solve more complex problems. This is especially true for computationally intensive issues requiring large-scale parallelization. Let us look at multigrid methods and how they can be accelerated using GPUs.

Multigrid methods are one of the most widely used techniques in solving such problems, especially for large-scale computations.

The most popular form of multigrid method is the finite volume method (FVM). FVM solves partial differential equations (PDEs) by splitting the domain into smaller cells which are solved independently. The solution from one cell is then used to approximate the solution to neighboring cells. The process is repeated until an approximate solution for the entire domain has been found.

Traditionally, solving these problems requires sequential processing, which can be computationally demanding. However, by utilizing GPUs, this analysis can be accelerated by 3x or more.

How to Capitalize Your GPU Investment

Soon, designers saw the vast potential for GPUs with the continuous domain shift from CPU-bound to GPU-bound computing problem-solving. This breakthrough came along when we realized we could perform an otherworldly number of calculations using parallel processing over multiple concurrent processing units.

Single-GPU systems are suitable for small simulations that don’t exceed 10 million cells. You will need multiple GPU systems with at least four GPUs for more extensive simulations. Multiple GPU systems are better for complex simulations because they can process more data in a shorter time than a single GPU system can.

You must ensure that your CFD simulation services are fully optimized for multiple GPUs. Engineers can do this using a CFD simulation service package like Ansys Fluent or ANSYS CFX. Most CFD suppliers have made it easy to use the GPU by making its license very inexpensive, and this has made the adoption process much more straightforward. There may be many restrictions on models that run entirely on a GPU. Still, if your model satisfies all criteria, the accelerations might be spectacular.

The bottom line is: the more powerful the GPU, the better it can handle 3D content and post-processing.

Take Away

Engineering simulation software is quite expensive and would take a long time to run its simulations. The number of cores/threads and processors and the single-threaded performance are significant. In this situation, running simulations on a single GPU will provide you with limited capability to rotate the flow.

GPUs have immense power and are capable of tackling problems or sometimes even impossible ones in seconds. This has made them a popular choice among engineers and scientists in all fields because they help reduce heat-up time.

Mechartés is the best choice for companies and engineering professionals working with CFD simulation services for fluid flow analysis. Our CFD experts have received extensive training in the software tools necessary for mesh design and boundary condition implementation, resulting in interpretation and hardware selection.

Our support team is here for you. The combination of our in-depth industry experience and cutting-edge CFD Application skills allows us to aid in deciding what processor and GPUs work best for your simulation efforts.
Call us or email us to find out more.