Titlebook: Euro-Par 2022: Parallel Processing; 28th International C José Cano,Phil Trinder Conference proceedings 2022 Springer Nature Switzerland AG

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SOB

Accelerating Parallel Operation for Compacting Selected Elements on GPUsgence. The task of this operation is to produce a smaller output array by writing selected elements of an input array contiguously back to a new output array. The selected elements are usually defined by means of a bit mask. With the always increasing amount of data elements to be processed in the d

tolerance

A Methodology to Scale Containerized HPC Infrastructures in the Cloudith the usual Kubernetes syntax for recipes, and our approach automatically translates the description to a full-fledged containerized HPC cluster. Moreover, resource extensions or shrinks are handled, allowing a dynamic resize of the containerized HPC cluster without disturbing its running. The Kub

Myelin

Cucumber: Renewable-Aware Admission Control for Delay-Tolerant Cloud and Edge Workloadspossible countermeasure is equipping IT infrastructure directly with on-site renewable energy sources. Yet, particularly smaller data centers may not be able to use all generated power directly at all times, while feeding it into the public grid or energy storage is often not an option. To maximize

商议

0302-9743 sgow, UK, in August 2022..The 25 full papers presented in this volume were carefully reviewed and selected from 102 submissions. The conference Euro-Par 2022 covers all aspects of parallel and distributed computing, ranging from theory to practice, scaling from the smallest.to the largest parallel a

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Schlemms-Canal

Gesellschaft für Natur- und Heilkundertitioning that balances peak memory usage. Our approach is DL-framework agnostic and orthogonal to existing memory optimizations found in large-scale DNN training systems. Our results show that our approach enables training of neural networks that are 1.55 times larger than existing partitioning solutions in terms of the number of parameters.

MORT

„Selbsthilfebewegung“ und Public Health) on a variety of GPU platforms, (ii) for different sizes of the input array, (iii) for bit distributions of the corresponding bit mask, and (iv) for data types. As we are going to show, we achieve significant speedups compared to the state-of-the-art implementation.

optic-nerve

Characterization of Different User Behaviors for Demand Response in Data Centerse study the impact of these behaviors on four different metrics: the energy consumed during and after the time window, the mean waiting time and the mean slowdown. We also characterize the conditions under which the involvement of users is the most beneficial.

AVID

mCAP: Memory-Centric Partitioning for Large-Scale Pipeline-Parallel DNN Trainingrtitioning that balances peak memory usage. Our approach is DL-framework agnostic and orthogonal to existing memory optimizations found in large-scale DNN training systems. Our results show that our approach enables training of neural networks that are 1.55 times larger than existing partitioning solutions in terms of the number of parameters.