Twelve quick tips designing AI-driven is a M.Tech project topic for Biotechnology & Biomedical Engineering. It gives students a clear starting point for research, implementation planning, and documentation.
Twelve quick tips designing AI-driven Project Details
| Abstract |
High-performance computing (HPC) clusters are the main technological backbone of large-scale scientific computation. Historically, these systems have processed steps in a pipeline in a determined manner, with linear processing and predictable performance. However, with the growing role of artificial intelligence (AI) and foundational models, a new computing paradigm is emerging. AI workflows are iterative, data-driven and probabilistic. This leads to challenges surrounding data gravity, heterogeneous computing resource management, and orchestration of workflows. The following describes a set of twelve guidelines, designed to assist researchers with the creation of AI-based HPC workflows that are efficient, scalable, and reproducible. These guidelines are designed to help address system-level bottlenecks by example to show
the use of containerisation to improve the portability of computing environments and the use of controlled job arrays to create feedback loops that adaptive the execution of a workflow, and the reduction of small file I/O. This methodology is designed to move away from rigid, pipeline execution towards more adaptive and smart computational models. These design outcomes are intended to meet the extreme throughput and resource requirements of modern computational biology, although the principles can apply to many uses of distributed computing.
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| Reference Paper |
Twelve quick tips for designing AI-driven HPC workflows |
| Domain |
Biotechnology & Biomedical Engineering |
| Sub-Domain |
Computational Biology / Bioinformatics |
| PDF Download |
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