Performance portability should be a major concern to those who wish to effectively implement AI and ML
In the ever-evolving landscape of artificial intelligence (AI) and machine learning (ML), performance portability has emerged as a critical factor influencing the efficiency and adaptability of computational workloads.
Put simply, performance portability
is the ability of a program or application to maintain consistent and efficient performance across various computing platforms. In fact, if performance portability isn’t prioritized, the efficiency and effectiveness of both AI and ML can be lost.
Hopping Between Architectures
Until recently, there were two “laws” that governed hardware development: Moore’s Law which states transistors on a microchip double every two years, and Dennard Scaling which states power consumption per transistor decreases as transistor density increases. Various factors — including the physical limitations of ever-shrinking transistors — have caused both of these laws to break down and lose effectiveness.
Industry leaders are still looking for ways to grow, leading to a rush to design specialized hardware that can increase efficiency. Specifically, technology like specially designed CPUs, GPUs, TPUs, accelerators, and interconnects are coming on the market to bring about more efficiency.
Especially in AI and ML, computational workloads often transition between these different hardware devices. While these solutions hold the promise of exciting results, they do present a problem in the form of software portability. In a sense, there is a growing shift from generalization to specialization in computing hardware, and getting these specialized pieces of technology to interact efficiently has become an important challenge to overcome.
In short, performance portability ensures that software maintains efficient performance across diverse computing platforms.
Something to Contain it All
Containers are simple and effective solutions to the problems brought on by varied hardware platforms. A container is a standard unit of software that bundles up code and all its dependencies, ensuring that the application runs quickly and reliably across different computing environments.
For instance, containers can provide a bridge between developing an application on a traditional laptop and deploying to a specific cluster. This is possible thanks to a container’s ability to provide an isolated environment that allows them to ensure that software can be reliably and securely transferred across different computing platforms. They also provide uniform application behavior, optimized resource usage, agility during development, the embrace of microservices, and integrated management tools.
For many, the performance portability problems from specialized hardware can be solved through the thoughtful and effective use of containers.
Of course, the topic of performance portability is extremely complex and deserves much study and contemplation. Sylabs has written a more in-depth technical brief for those who wish to dive deeper into the ramifications of performance portability.