Postdoctoral Appointee – HPC Accelerator Hardware Analysis

ALCF’s datascience group seeks a post-doctoral candidate to work on reduced precision numerical accuracy on GPU and other accelerator hardware. The successful candidate will develop and deploy tests to measure accuracy and reproducibility of critical operations in novel floating point formats, particularly those accelerated with dedicated hardware features such as TensorCores (Nvidia) and equivalent. The candidate will also have the opportunity to explore these tests and features in novel AI Hardware, such as that hosted at the ALCF AI-Testbed [Register to View] work will take place in a multi-disciplinary environment and will offer opportunities to interact with a wide range of talents from the whole spectrum of HPC research. The successful candidate will be expected to present and publish their work at major symposia and journals.

Conducts research aligned with Argonne’s strategic missions in computation. Conducts work in computational science and leadership computing and promotes findings through publications, presentations, program reports, software dissemination and other means of reporting outcomes. Contributes to discovery and technological advances in domain science, with emphasis on the applications of exascale (and beyond) computer architectures, e.g., to address data architecture challenges and algorithm implementation challenges of high-performance simulations. Strengthens career path opportunities by applying new techniques, developing independent research capabilities and building professional skills such as communication, networking and leadership. Works with vendors to investigate reproducibility and reliability of hardware accelerated linear algebra operations.

Position Requirements

• A recent Ph.D. (typically completed within 3 years) and 0-3 years of experience in computational chemistry, medicinal chemistry, computer science, biological science, or a related field
• Experience in GPU and accelerator computing, including reduced precision.
• Experience related to parallel algorithms, I/O architectures, or performance evaluation and tuning
• Extensive expertise in multicore systems, threading, and scientific application codes
• Effective written and communication skills

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