By Elad Raz, Founder and CEO, NextSilicon
[OPINION/INSIGHT ARTICLE] The surging demand for supercomputing power, driven by the insatiable appetite of AI/ML, big data analytics, and scientific research, has driven the HPC industry to push the boundaries of processor technology. As we’ve moved from the era of Moore’s Law and CPU dominance to the age of GPU acceleration, we’ve witnessed remarkable advancements in parallel processing and throughput. GPUs, with their ability to handle multiple tasks simultaneously, have become the go-to solution in fields from climate modeling to drug discovery.
Today, the top 10 most powerful supercomputers in the world, as ranked by the TOP500 list, are all GPU-accelerated systems. However, GPUs are not a panacea. Many applications, particularly those that rely on sequential processing, frequent branching (such as real-time decision-making), or global memory access (like graph algorithms or particle simulations), continue to rely heavily on CPUs to perform tasks that GPUs struggle to manage. Heterogeneous computing, with CPUs and GPUs working together is still essential for maximizing performance and flexibility. Even in the Frontier exascale supercomputer at Oak Ridge National Lab, which has led the pack in performance, CPUs still play a crucial role in handling workloads that are not well-suited to GPUs’ parallel architecture.
In this article we’ll discuss the porting and optimization challenges of GPU computing as well as a future in which those challenges are more effectively addressed.
Software-Hardware Mismatch: Navigating the Porting Challenge
The fundamental differences between CPUs and GPUs exemplify the challenges in adapting software to new hardware architectures. CPUs are designed for serial processing (though they can handle some multithreading across multiple cores), excelling at executing a single stream of instructions quickly and efficiently. In contrast, while GPUs excel at parallel workloads, they struggle with tasks requiring strong single-thread performance.
This divergence in designs means CPU-based applications must undergo extensive code refactoring and optimization to take full advantage of the parallelism offered by GPUs. This process is not only time-consuming but also requires a deep understanding of both the original application and the intricacies of GPU programming, as developers need to rethink their algorithms and data structures from the ground up.
Moreover, the memory management paradigms of GPUs differ significantly from those of CPUs. Efficient utilization of GPU memory hierarchies, including shared memory, caches, and global memory, is crucial for achieving optimal performance. Developers must carefully orchestrate data movement and optimize memory access patterns, adding yet another layer of complexity to the porting process.
Compounding these challenges is the steep learning curve that developers face when transitioning from CPU-based programming to GPU architectures. Mastering new GPU programming models, such as CUDA, ROCm, or OpenCL, and acquiring the expertise to leverage GPU-specific optimization techniques can take years of dedicated effort.
The Two-Stage Gauntlet: Language Porting and Hardware Optimization
Porting code to a new architecture, particularly for GPU-based systems, typically involves a two-stage process. The first stage is the language port, where software written in one programming language is adapted to another. This translation requires a deep understanding of both the source and target languages as well as the specific features of the hardware being targeted. Developers must ensure that the translated code can efficiently leverage the new architecture’s capabilities to deliver performance on par with, or better than, the original system.
The second stage—often the more time-consuming one—is hardware-specific optimization. This phase involves refining the code to fully exploit the nuances of the target GPU architecture. Key areas of focus include:
- Cache optimization: Developers optimize memory access patterns to maximize cache utilization, reduce cache misses, and improve data locality, all of which are crucial for better performance on the new hardware.
- Enhancing memory locality: Techniques like data reordering and restructuring are employed to reduce memory latency and improve throughput, thereby boosting overall performance.
- Vectorization: By rewriting or annotating code to use SIMD (Single Instruction, Multiple Data) instructions, developers can enable parallel processing of data elements, significantly increasing computational efficiency.
- Parallelization: Algorithms are parallelized wherever possible, leveraging multiple cores or processing units to accelerate computations.
The drawbacks of this process are notable. Even after a successful language translation, the GPU-based application may not work as well as the CPU version. Achieving performance parity—or better yet, surpassing CPU performance—often requires multiple rounds of profiling, analysis, and optimization, each consuming considerable time and resources. This process can extend over months or even years, as trial-and-error becomes integral to the workflow.
Elad Raz, NextSilicon
This lengthy optimization phase can lead to frustration, especially when stakeholders unfamiliar with these challenges expect a seamless transition. A well-known example took place at Los Alamos National Laboratory, where a team ported their PENNANT hydrodynamics mini-app to GPUs, only to see a modest 10-30 percent performance improvement after many months of effort.
The Hidden Costs of GPU Acceleration
Beyond the technical complexities, the industry’s growing reliance on GPU-based systems has introduced a new set of economic challenges, driven in part by the challenges of limited vendor options and the resulting vendor lock-in. At the heart of this lock-in are domain-specific languages (DSLs), tailored to optimize performance for specific GPU architectures but lacking portability across other platforms. These DSLs, combined with proprietary programming models, make it difficult to switch platforms, often forcing organizations to adapt their workloads to the vendor’s technology rather than using solutions that meet their evolving needs. This leaves organizations dependent on a single vendor’s ecosystem, limiting flexibility and driving up costs.
The acquisition costs of high-performance data center GPUs can be staggering, often dwarfing the upfront investment required for traditional CPU-based systems. For many organizations, this level of investment is simply unattainable, as the costs associated with building or upgrading infrastructure to accommodate GPUs are beyond reach.
Integrating GPUs into existing infrastructure often necessitates costly upgrades to power, cooling, and networking capabilities, further compounding the financial burden. This leaves organizations unable to scale up without building entirely new data centers, an expense few can afford. And let’s not forget the additional licensing fees associated with GPU-accelerated software and development tools.
Once deployed, GPU-based systems can also incur significant operational costs, with power consumption among the largest of them. In many cases, the power bill can exceed the cost of the GPU hardware itself, especially at full capacity. This creates a difficult ongoing balancing act where power, performance and cost must be constantly optimized. The need for advanced cooling systems adds another layer of complexity and cost.
The result is that many organizations without the means to invest in GPUs are left on the outside looking in, unable to compete in a rapidly evolving landscape. Innovation stalls, insight is delayed, and the gap between the haves and have-nots in the HPC and AI space grows wider.
Bridging the Past, Present and Future
At the heart of the porting challenge lies the need to harmonize diverse and rapidly evolving software ecosystems and hardware architectures.
Current strategies to address the challenges of porting applications to diverse processor architectures often fall short. Portable programming models like OpenMP, Kokkos, and RAJA aim to reduce platform-specific complexities, but they still demand significant developer effort and optimization to achieve peak performance.
What the HPC-AI industry truly needs is a new generation of intelligent, adaptive accelerators—solutions designed to dynamically optimize performance for evolving workloads—whether legacy, current, or future. It’s critically important that these software-defined hardware solutions reduce the need for extensive porting while simultaneously lowering operational costs and improving performance portability across diverse workloads.
By decoupling hardware from software, organizations could achieve a level of futureproofing that is sorely lacking today, offering the flexibility and scalability necessary to accommodate the fast-evolving landscape of HPC-AI without frequent hardware upgrades or major software rewrites.
Developing architectures that intelligently abstract the underlying hardware—allowing the hardware to adapt to the application—can help overcome these challenges. By combining the strengths of both CPUs and GPUs, these architectures enable real-time performance optimization, scaling across diverse workloads without relying on vendor-specific tools. This reduces the complexity of porting and lowers costs, offering a flexible solution that meets evolving HPC demands.
As we stand on the cusp of this new era, all stakeholders — including hardware vendors, software developers, data center architects, and enterprise end-users — need to champion the development and adoption of intelligent porting solutions. By embracing such innovations, we can unlock unprecedented levels of computational power and efficiency, enabling scientific and business breakthroughs that would have otherwise been out of reach.
Elad Raz is the founder and CEO of NextSilicon, a company developing a new approach to HPC-AI architecture that drives the industry forward by solving its biggest, most fundamental problems.
