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Field
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Shifting bits: Adaptive numerical precision for GPU software in particle physics and beyond (S3.5-COM-Richmond)
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FPGAs, CGRAs, and many Machine Learning accelerators, offer significant opportunities for improving performance and energy efficiency compared to traditional CPUs/GPUs. Yet, porting and optimizing code
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and optimization strategies for large-scale or streaming data. Develop parallelized and GPU-accelerated learning modules, ensuring scalability and performance efficiency. Build and maintain robust data
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Work location Zürich or Lugano Topic B: Simultaneous tree traversal with Producer-Consumer pattern on GPUs Abstract Simultaneous tree traversal, also referred to as dual tree traversal, can be applied
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compressible gas dynamics, heat transfer, free-surface/melt behaviour, and mass transfer driven by phase change, within a GPU-accelerated solver to reduce simulation turnaround times. You will develop and
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compressible gas dynamics, heat transfer, free-surface/melt behaviour, and mass transfer driven by phase change, within a GPU-accelerated solver to reduce simulation turnaround times. You will develop and
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dynamic and enterprising individual to join us as a Senior HPC Application Engineer. Key Responsibilities Port scientific applications to GPU (e.g., using CUDA, HIP, OpenACC) or optimize for multi-core CPUs
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 16 days ago
directly with internal and external stakeholders to resolve issues and drive operational improvements. PREFERRED: Familiarity with IT service management frameworks, vendor coordination, and GPU or cloud
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 16 days ago
supporting end-users in technical research environments. PREFERRED: Familiarity with GPU computing; scripting in Python or Bash; and container orchestration tools like Docker and Kubernetes; and experience in
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scientists and engineers are accustomed to. Moreover, the vast majority of the performance associated with these reduced precision formats resides on special hardware units such as tensor cores on NVIDIA GPUs