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Optimize weather and climate models using Python-embedded DSL and data-centric techniques. Improve productivity and performance through abstraction, semi-automated workflows, and transfer tuning for efficient scaling on supercomputers.
Explore interactive data movement visualization for program performance optimization. Analyze dataflow, reuse patterns, and optimize applications without execution using SDFG IR and static analysis techniques.
Explore AMD/Xilinx AI Engine architecture: vector processors, data movement, synchronization, and application mapping for machine learning and 5G acceleration.
Explore heterogeneous serverless computing for efficient HPC, HPDA, and AI workloads. Learn about innovative accelerator uses and flexible deployment models from edge to exascale.
Explore next-gen networks for machine learning, focusing on optical fabrics and scheduling techniques to accelerate distributed DNN training workloads and overcome datacenter network bottlenecks.
Explore self-adjusting networks optimized for datacenter traffic, including metrics, design principles, and research challenges. Gain insights into demand-aware graphs and related optimization problems.
Explore spatial computing strategies for post-Moore's Law era, addressing challenges in scaling compute power from chips to clusters for AI and high-performance computing.
Optimize Python for high performance on CPUs, GPUs, and FPGAs. Learn HPC-oriented extensions, automatic optimizations, and portable performance techniques across architectures.
Explore new approaches for enabling domain experts to program future HPC systems efficiently, focusing on compiler and runtime techniques for distributed heterogeneous computing.
Explore neural graph databases, combining graph databases with graph neural networks using LPG2vec encoder for improved prediction accuracy in complex graph datasets.
Explore scalable synchronization in manycore systems with LRSCwait. Learn about polling-free and retry-free operations for improved throughput and energy efficiency in shared-memory architectures.
Compress multidimensional weather and climate data into neural networks, achieving high compression ratios while preserving accuracy for climate change analysis and severe weather prediction.
Explore HexaMesh, an optimized chiplet arrangement for 2.5D integration, outperforming grid layouts in network diameter, bisection bandwidth, latency, and throughput for large-scale designs.
Explore a novel Network-on-Chip topology for scalable chip design. Learn design principles, cost-performance trade-offs, and toolchain development for customizing NoC architectures to meet diverse goals.
Discover how Performance-Detective reduces performance modeling costs while maintaining accuracy. Learn to deduce parameter interactions and derive efficient experiment designs for optimizing application configurations.
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