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inference Develop distributed model training and inference architectures leveraging GPU-based compute resources Implement server-less and containerized solutions using Docker, Kubernetes, and cloud-native
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at MGHPCC facility (i.e., data center, compute, storage, networking, and other core capabilities). Deploy, monitor, and manage CPUs, GPUs, storage, file systems, networking on HPC systems. Develop and deploy
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conferences. Qualifications: PhD in computer science with file systems, GPU architecture experience. Proven ability to articulate research work and findings in peer-reviewed proceedings. Knowledge of systems
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context . NDIF has recently received NSF support, including sufficient GPU resources to create a scalable service. This summer the project is embarking on a rapid summer initiative to implement: High
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implement state-of-the-art parallel GPU inference methods, and incorporate them into a system with job scheduling, routing, quota management, authentication, authorization, and telemetry to create a high