Titlebook: High Performance Computing; 36th International C Bradford L. Chamberlain,Ana-Lucia Varbanescu,Piotr Conference proceedings 2021 Springer Na

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uthors of the volume are distinguished scientists who are leading experts in the field, and who have contributed important, original data to our understanding of the mechanisms of appetite control. They have quite different scientific backgrounds and, together, they represent all relevant discipline

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Bradford L. Chamberlain,Ana-Lucia Varbanescu,Piotr

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Auto-Precision Scaling for Distributed Deep Learningtate-of-the-art methods. To make it available to researchers and developers, we design and implement CPD (Customized-Precision Deep Learning) system, which can simulate the training process using an arbitrary low-precision customized floating-point format. We integrate CPD into PyTorch and make it o

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FPGA Acceleration of Number Theoretic Transformork is used to reduce the data communication complexity between NTT stages. We implement the proposed architecture for various polynomial degrees, moduli, and data parallelism on state-of-the-art FPGAs. Experimental results show that our architecture configured to perform 4096 polynomial degree NTT

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A Tunable Implementation of Quality-of-Service Classes for HPC Networksn constrained to a limited number of classes..We propose a practical QoS implementation for large-scale, low-diameter networks, such as the dragonfly topology, using flexible bandwidth shaping along with traffic prioritization to reduce the impact of interference on communication performance. Our de

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Scalability of Streaming Anomaly Detection in an Unbounded Key Space Using Migrating Threadsncies. As with the earlier paper, results are promising, with both far better scaling and increased performance over previously reported implementations, on a platform with considerably less intrinsic hardware computational resources.