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Artificial Intelligence (AI) is a resource hog. AI-powered programs will grind to a halt unless developers continue to seek out the fastest, most scalable, most power-efficient and lowest-cost hardware, software and Cloud platforms to run their workloads.
As the AI arena shifts toward workload-optimized architectures, there’s a growing need for standard benchmarking frameworks to help practitioners assess which target hardware/software stacks are best suited for training, inferencing, and other workloads.
In the past year, the AI industry has moved rapidly to develop open, transparent, and vendor-agnostic frameworks for benchmarking for evaluating the comparative performance of different hardware/software stacks in the running of diverse workloads. Here the most important of these initiatives, as judged by the degree of industry participation, the breadth of their missions, the range of target hardware/software environments they’re including in their scope, and their progress in putting together useful frameworks for benchmarking today’s top AI challenges.
Today’s Internet Services are undergoing fundamental changes and shifting to an intelligent computing era where AI is widely employed to augment services. In this context, many innovative AI algorithms, systems, and architectures are proposed, and thus the importance of benchmarking and evaluating them rises. However, modern Internet services adopt a micro-service based architecture and consist of various modules. The diversity of these modules and complexity of execution paths, the massive scale and complex hierarchy of datacenter infrastructure, the confidential issues of data sets and workloads pose great challenges to benchmarking.