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Introduction to Mathematical Thinking
Mechanics of Materials I: Fundamentals of Stress & Strain and Axial Loading
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Explore challenges and solutions in evaluating large-scale machine learning systems, including federated networks and large language models. Gain insights into reliable assessment methods.
Explore advanced ML techniques for climate data processing, compression, and Earth virtualization, enhancing high-performance computing in climate science.
Explore network performance variability in cloud computing and its impact on HPC workload scalability, comparing cloud providers with on-premise systems.
Explore efficient scheduling techniques for dataflow architectures using canonical task graphs, steady-state analysis, and spatial block partitioning to optimize performance and device utilization.
Explore Earth Virtualization Engines (EVE) and their potential impact on climate change research with Bjorn Stevens from MPI Hamburg.
Explore massively parallel distributed supercomputers, focusing on distributed-memory parallelism and RDMA through MPI. Gain insights into current designs and future challenges in high-performance computing.
Explore HPVM's approach to programming heterogeneous parallel systems, focusing on performance, ease of use, and retargetability across diverse hardware targets.
Explore MPI's crucial role in modern AI and its impact on parallel computing, honoring Rusty Lusk's contributions to the field.
Explore integrated silicon photonics for high-bandwidth, low-energy chip communication, enabling Pb/s speeds and flexible architectures for distributed ML applications.
Explore a chiplet-based architecture for generative inference, featuring block floating point datatypes and optimizations for large language models in AI accelerated computing.
Optimize distributed multilinear algebra computations with Deinsum framework. Automate data movement-optimal tiling and scheduling for enhanced performance in high-dimensional tensor operations.
Explore synergies between HPC and AI/ML, focusing on scalable algorithms, efficient resource use, and data-driven models for performance optimization in large-scale computing environments.
Explore data-driven approaches for memory architecture in exascale supercomputing and cloud memory disaggregation. Gain insights into system-level memory problems and their solutions.
Explore in-network compute techniques to accelerate data serialization/deserialization protocols, enhancing efficiency in datacenter and HPC systems. Learn about SmartNIC offloading and its impact on performance.
Explore DeepHyper: Automate DNN design for scientific data using scalable neural architecture and hyperparameter search. Learn to generate DNN ensembles for uncertainty estimation in various applications.
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