Why GPUs are used in HPC

Overview

Graphics Processing Units, or GPUs, are now a central technology in modern high performance computing. They started as specialized chips for rendering images and video, but their design turned out to be very effective for many scientific and data processing tasks. This chapter explains why HPC systems invest so heavily in GPUs and other accelerators, and what makes them attractive compared to using only traditional CPUs.

Massive Throughput Instead of Low Latency

A CPU is designed to respond quickly to many different kinds of tasks, often involving complex logic and unpredictable control flow. A GPU is designed to execute a very large number of very similar operations at the same time. In other words, CPUs focus on low latency per task, while GPUs focus on high throughput of many tasks.

Many HPC workloads can be written as the same operation applied to huge arrays of numbers. For example, in a simulation you might update the state of each cell in a large grid, or in machine learning you might apply the same mathematical kernel to many training examples. In such cases, the flexibility of the CPU is less important, and the ability of the GPU to run thousands of operations concurrently is far more valuable.

This throughput oriented design is the main reason GPUs can deliver much higher peak performance than CPUs for the right kind of problem.

Higher Floating Point Performance per Device

The most visible advantage of GPUs in HPC is their raw floating point performance. Manufacturers pack many more arithmetic units into a GPU than into a CPU. These units operate in a style similar to SIMD vectorization, but scaled up across thousands of concurrent threads.

Sustained performance depends on many factors, but peak specifications illustrate the difference. A single modern server CPU may provide on the order of a few teraflops of double precision performance. A high end HPC GPU can provide tens of teraflops of double precision, and far more in single or mixed precision.

In large systems this multiplies quickly. A cluster node with several GPUs can easily provide an order of magnitude more floating point capability than a CPU only node of similar cost and power budget. For compute bound scientific codes that have been adapted to accelerators, this directly translates into shorter time to solution.

Better Performance per Watt

Energy consumption is a central concern in HPC. Power and cooling limit the size of supercomputers and dominate operating costs. A key argument for GPUs is that they deliver more useful work per unit of energy than general purpose CPUs, for suitable applications.

This is possible because GPU hardware is simpler and more specialized. Many transistors are devoted to arithmetic units and data paths, not to large caches or complex control logic. When an application keeps the GPU fed with parallel work, a large fraction of the consumed power directly supports arithmetic operations.

For system designers, this means that a given power budget can support a substantially higher aggregate performance if many GPU accelerators are used. For users and application teams, this means that accelerated codes can offer both faster results and lower energy per simulation or per training run.

Exploiting Massive Data Parallelism

Many HPC problems are naturally data parallel. The same computation is applied to many elements of a dataset, often with limited interaction between elements at each step. Examples include:

Updating each cell of a computational grid in a time stepping simulation, where each cell update depends on its neighbors.

Applying linear algebra operations such as matrix vector or matrix matrix products.

Performing local operations on pixels or voxels in image and volume processing.

In data parallel problems, each element or block of data can be handled by an independent thread, as long as memory accesses are managed carefully. GPUs provide hardware support for tens of thousands of simultaneous lightweight threads. When the algorithm fits this model, almost all the hardware resources of the GPU can be kept busy.

This style of parallelism matches very well with the design of modern GPUs. The result is that codes with regular data parallel structure often show large speedups when ported to GPUs, compared to their CPU only versions.

Memory Bandwidth Advantages

For many HPC applications, the bottleneck is not pure arithmetic but the rate at which data can be moved between memory and compute units. GPUs are built with very high bandwidth connections to their on board global memory. The absolute bandwidth of GPU memory is typically several times higher than that of the main memory attached to a CPU.

When an algorithm performs a modest amount of computation per byte of data, performance can be limited by memory bandwidth. In such memory bound scenarios, the GPU’s higher bandwidth can be as important as its higher arithmetic throughput. Examples include sparse linear algebra, stencil computations, and many finite difference schemes.

However, the transfer between CPU memory and GPU memory can be expensive. This is one reason why GPU programming models place emphasis on careful data placement and on minimizing data movement between the host and the accelerator. Still, for many codes, once data is resident in GPU memory, kernels can consume it at rates that are not achievable on CPU memory systems.

Cost Effectiveness and System Density

HPC centers must consider purchase cost, operating cost, and physical constraints such as rack space and cooling. GPUs help satisfy these constraints by providing high performance in a relatively compact and power efficient form.

A GPU enabled node can deliver much of the performance that would otherwise require many more CPU only nodes. This increases compute density in a data center and can reduce the size of the interconnect network required for a given aggregate performance. Fewer nodes mean fewer network ports and cables, and often a simpler overall system design.

From a user perspective, GPUs can be cost effective because they reduce the number of nodes needed to achieve a target time to solution. License costs for commercial software, queue waiting times, and system administration overheads can all be lower when the same workload is processed on fewer, more powerful nodes.

Fit for Modern Application Trends

HPC is no longer limited to traditional simulation codes. Many modern workloads are based on machine learning, data analytics, and signal processing. GPUs are well suited for these as well.

Machine learning, in particular deep neural networks, involves large matrix and tensor operations. These match GPU capabilities extremely well. Vendors have added specialized units, such as tensor cores, specifically optimized for these operations and for reduced precision arithmetic. This makes GPUs very attractive for AI workflows, which have become central to both scientific and industrial HPC.

In addition, many scientific applications now combine simulation and learning. For example, they may use neural networks as surrogate models within larger physical simulations. This convergence of HPC and AI further strengthens the role of accelerators in modern compute environments.

Support in Software Ecosystems

Hardware capabilities are only useful when software can exploit them. One reason GPUs have become so dominant in HPC is the maturity of their software ecosystems.

On the low level side, vendor provided programming models such as CUDA and directive based models such as OpenACC and certain OpenMP offloading features allow developers to target GPUs directly. At higher levels, many widely used scientific libraries are already GPU accelerated. This includes linear algebra libraries, FFT libraries, and domain specific packages in fields such as molecular dynamics and computational fluid dynamics.

In machine learning and data analytics, major frameworks were designed with GPUs in mind from the beginning. Users can often benefit from accelerators with little or no custom GPU specific code. This broad software support significantly lowers the barrier to entry and increases the practical value of GPUs in HPC.

Enabling New Science Through Faster Time to Solution

Ultimately, the primary reason GPUs are used in HPC is that they enable new science and engineering that would otherwise be too slow or too expensive. Faster hardware is not just about doing the same calculations quicker. It changes what is possible.

With GPU acceleration, simulations can run at higher resolution, with more complex models, or over longer timescales. Parameter sweeps can cover larger regions of parameter space. Uncertainty quantification methods can run many more samples. Machine learning models can be trained on larger datasets or tuned more thoroughly.

For researchers, this means being able to test more hypotheses in a given time. For industry, it means reducing design cycles, performing more virtual prototyping, and improving product quality and reliability. In many cases, only GPU accelerated clusters can provide the necessary performance within reasonable budgets and power constraints.

Summary

GPUs are used in HPC because their architecture is well matched to a wide class of highly parallel, data intensive workloads. They provide significantly higher floating point throughput, greater memory bandwidth, better performance per watt, and attractive cost effectiveness compared to CPU only systems. Combined with strong software support and the rise of AI and data driven methods, these advantages make GPUs central components of modern high performance computing systems.