Performance Architect at NVIDIA developing innovative hardware architectures for AI and high-performance computing. Collaborating on performance modeling and system-level optimizations across large-scale deep learning workloads.
Responsibilities
Develop innovative HW architectures to extend the state of the art in parallel computing performance, energy efficiency and programmability.
Build the mathematical frameworks required to reason about system availability and workload goodput at massive scales.
Reason about overall Deep Learning workload performance under various scheduling, parallelization, and resiliency strategies.
Conduct "what-if" studies on hardware configurations, infrastructure knobs, and workload strategies to identify optimal system-level trade-offs.
Work closely with wider architecture and product teams to guide the hardware/software roadmap using data-driven performance and reliability projections.
Build and refine high-level simulators in python to model the interaction between knobs that impact performance and resiliency.
Requirements
MS or PhD in a Computer Science, Computer Engineering, Electrical Engineering or equivalent experience.
6+ years of relevant industry or research work experience.
Strong background in analytical and probabilistic modeling.
2+ years of experience in parallel computing architectures, distributed systems, or interconnect fabrics.
A strong understanding of distributed deep learning workloads scheduling in large scale systems.
Proficiency in Python for building performance and reliability models.
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