Introduction
For decades the central processing unit (CPU) was the unquestioned workhorse of scientific computation. Its serial architecture, coupled with a predictable memory hierarchy, made it a comfortable choice for researchers who could rely on a single, well‑understood platform to run complex simulations, analyze vast data sets, and prototype new algorithms. The CPU’s dominance was not merely a matter of performance; it was also a cultural artifact. The software ecosystem—compilers, libraries, and operating systems—was built around the CPU, and the academic community had a long tradition of writing code that ran efficiently on it.
The turning point came when the limitations of the CPU began to surface. Energy consumption grew linearly with clock speed, and the silicon scaling that had driven performance gains for decades stalled. At the same time, the rise of machine learning and data‑intensive workloads revealed a mismatch between the CPU’s design and the needs of modern science. GPUs, originally engineered for rendering graphics, offered massively parallel execution units and high memory bandwidth, making them attractive for scientific workloads that could be expressed in a data‑parallel fashion. The shift from CPU to GPU was not a simple hardware upgrade; it represented a fundamental rethinking of how scientific systems are architected.
Today, accelerated computing is no longer a niche optimization but a cornerstone of research infrastructure. Extreme co‑design—where hardware, networking, and software are developed in tandem—has become the new frontier. This blog post explores how the “great flip” from CPU to GPU has reshaped scientific systems, the role of co‑design in pushing the envelope, and what the next wave of acceleration might look like.
Main Content
The Rise of GPU‑Accelerated Science
GPUs were born in the 1990s as a specialized accelerator for rendering images. Their architecture, featuring thousands of lightweight cores, is well suited for tasks that can be decomposed into many independent operations. When researchers began to port scientific codes to GPUs, they discovered that embarrassingly parallel problems—such as molecular dynamics, lattice QCD, and climate modeling—could achieve speedups of an order of magnitude or more. The key to this success was the ability to express computations in terms of data parallelism and to harness the GPU’s high memory bandwidth.
However, the transition was not trivial. Porting legacy CPU code to GPUs required a deep understanding of memory access patterns, thread synchronization, and the GPU’s execution model. This learning curve spurred the development of high‑level programming frameworks such as CUDA, OpenCL, and more recently, SYCL and Kokkos. These frameworks abstracted many of the low‑level details, allowing scientists to write code that could run on a variety of accelerators without sacrificing performance.
The impact on scientific discovery has been profound. For instance, the simulation of protein folding, once a computationally prohibitive task, became tractable on GPU clusters, enabling breakthroughs in drug discovery. Similarly, the ability to process terabytes of data in real time has accelerated research in astrophysics, where large‑scale sky surveys generate petabytes of imaging data.
Extreme Co‑Design: Hardware, Networking, and Software in Harmony
While GPUs provided the raw computational horsepower, the full potential of accelerated systems could only be realized through extreme co‑design. This approach treats hardware, networking, and software as a single, tightly coupled ecosystem rather than as separate layers.
At the hardware level, new interconnects such as NVLink and InfiniBand HDR have dramatically increased the bandwidth between GPUs and between GPUs and CPUs. These interconnects reduce the latency of data movement, which is critical for tightly coupled simulations that require frequent communication between compute nodes. Moreover, the advent of heterogeneous architectures—where CPUs, GPUs, and specialized accelerators like tensor processing units (TPUs) coexist on the same chip—has opened new avenues for optimizing data flow.
Networking has evolved to support the demands of large‑scale scientific workloads. Software‑defined networking (SDN) and programmable data planes allow researchers to tailor network behavior to the specific traffic patterns of their applications. For example, a climate model that exchanges data between neighboring grid cells can benefit from a custom routing policy that prioritizes low‑latency paths.
On the software side, the rise of domain‑specific languages (DSLs) and compiler frameworks has enabled automatic generation of efficient kernels for heterogeneous hardware. Tools such as Halide for image processing and TensorFlow for machine learning illustrate how a high‑level description of a computation can be compiled into optimized code for CPUs, GPUs, or specialized ASICs. In scientific computing, DSLs like Firedrake and PyOP2 provide similar benefits for finite element methods.
The synergy between these layers is exemplified by projects such as the Summit supercomputer, which combines AMD EPYC CPUs, NVIDIA GPUs, and a high‑speed interconnect to deliver exascale performance. The architecture of Summit was the result of a concerted effort to align hardware capabilities with the needs of scientific applications, ensuring that software could fully exploit the available resources.
The Role of AI in Accelerated Systems
Artificial intelligence has become both a beneficiary and a driver of accelerated computing. On one hand, AI workloads—particularly deep neural networks—are inherently parallel and thus well suited to GPUs and other accelerators. On the other hand, AI techniques are increasingly being applied to optimize scientific workflows themselves.
One promising area is the use of reinforcement learning to schedule tasks across heterogeneous resources. By learning optimal placement strategies, such systems can reduce idle time and improve overall throughput. Another application is the use of generative models to accelerate simulation. For instance, a generative adversarial network (GAN) can learn to predict the outcome of a complex fluid dynamics simulation, providing a rapid approximation that can be refined by a more detailed model when necessary.
Moreover, AI is being used to analyze the vast amounts of data produced by scientific experiments. In high‑energy physics, for example, convolutional neural networks are employed to sift through collision events, identifying rare phenomena that would otherwise be buried in noise. These AI pipelines themselves run on accelerated hardware, creating a virtuous cycle where the tools that drive discovery also benefit from the very acceleration they require.
What Comes Next: Beyond GPUs
While GPUs remain the dominant accelerator for many scientific workloads, the field is rapidly moving toward a more diverse ecosystem. Field‑Programmable Gate Arrays (FPGAs) offer low‑latency, energy‑efficient execution for streaming workloads, and their reconfigurability makes them attractive for evolving scientific algorithms. Similarly, application‑specific integrated circuits (ASICs) designed for particular domains—such as quantum chemistry or gravitational wave detection—promise unmatched performance per watt.
Another frontier is the integration of quantum accelerators. Although still in early stages, quantum processors could solve certain classes of problems—such as combinatorial optimization and quantum simulation—far more efficiently than classical hardware. Hybrid classical‑quantum systems, where a quantum co‑processor is coupled to a GPU‑accelerated CPU cluster, could unlock new scientific capabilities.
Finally, the software stack must evolve to keep pace with hardware diversity. Portable performance frameworks, such as Kokkos and RAJA, are already making strides in this direction, but further advances are needed to support emerging architectures without sacrificing productivity.
Conclusion
The transition from CPU to GPU has fundamentally altered the landscape of scientific computing. What began as a pragmatic response to energy constraints and the rise of data‑intensive workloads has blossomed into a holistic paradigm of extreme co‑design, where hardware, networking, and software are engineered in concert. This shift has accelerated discovery across disciplines—from molecular biology to astrophysics—by delivering unprecedented computational power and flexibility.
Looking ahead, the next wave of acceleration will likely involve a richer mix of accelerators, including FPGAs, ASICs, and quantum processors. The challenge will be to integrate these heterogeneous resources into a coherent ecosystem that remains accessible to researchers. By continuing to invest in co‑design, high‑level programming abstractions, and AI‑driven optimization, the scientific community can ensure that accelerated computing remains a catalyst for innovation.
Call to Action
If you’re a researcher, engineer, or enthusiast eager to harness the power of accelerated computing, start by exploring the tools and frameworks that enable GPU and heterogeneous programming. Engage with open‑source communities, contribute to projects like Kokkos or Firedrake, and experiment with AI‑based optimization techniques. By sharing knowledge and collaborating across disciplines, we can accelerate the pace of discovery and unlock the full potential of next‑generation scientific systems.