We are looking for highly skilled and motivated Postdoctoral Appointees to join our efforts around large-scale data storage and management for distributed computing systems and in particular HPC infrastructures. In this context, scientific instruments and hybrid HPC workloads (that combine simulations, big data analytics, and AI) generate, store and access huge amounts of data. You will be at the forefront of addressing major challenges and research questions in the design and development of runtimes that enable data management in a scalable, high-performance and resource-efficient fashion.
- We are interested in aspects: of caching, prefetching, versioning and lineage of data, checkpointing and intermediate data snapshots, incremental storage and evolution of data, placement on heterogeneous storage, etc.
- We are building on our experience with data models that capture the evolution of distributed datasets into a searchable lineage of snapshots that enable efficient storage and revisiting (DataStates) and well as checkpoint-restart systems (VELOC).
- We make use of composable services that bring flexibility (typically missing in HPC) around communication, concurrency management and building blocks such as BLOB and key-value stores (Mochi).
- We apply such data management techniques and principles in a variety of scenarios: AI network architecture search based on transfer learning, optimized data pipeline for large-scale AI training, AI model repositories with fine-grain incremental tensor storage and access, reducing I/O overheads of adjoint computations, reproducibility of workflows, etc.
In addition to addressing such transformative challenges,
- You will have the opportunity to get involved in several other efforts at the intersection of HPC, machine learning and big data analytics.
- We work closely with many domain experts to identify the requirements and bottlenecks of real-life scientific applications that address the needs of our society over the next decades.
- You will be part of a vibrant and diverse research community from more than 100 countries.
- Our lab hosts Aurora, one of the first Exascale supercomputers in the world, which you will have an opportunity to use for your experiments.
- In addition, you will have access to a large array of leading-edge experimental testbeds through the Joint Laboratory for System Evaluation (JLSE), which feature the latest technologies from top vendors like Intel, NVIDIA, AMD, etc.
- A recent or soon-to-be completed PhD degree (typically within the last 0-3 years)
- Familiarity with data management techniques: caching, indexing, asynchronous I/O
- Ability to conduct interdisciplinary research and participate in teamwork and broad collaborative efforts involving other laboratories and universities, supercomputer centers and industry
- Ability to model Argonne's core values: impact, respect, integrity, teamwork and safety
- Scientific background in distributed computing and HPC including:
- Strong code development skills with C/C++ and Python
- Familiarity with modern data management and I/O best practices
Job FamilyPostdoctoral Family
Job ProfilePostdoctoral Appointee
Worker TypeLong-Term (Fixed Term)
Time TypeFull time
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