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Multi-Scale computing systems aim at bringing the computing as close as possible to the data sources, to optimize both computation and networking. These systems are composed of at least three computing layers: the terminal layer, the edge layer, and the cl ...
Machine learning (ML) applications are ubiquitous. They run in different environments such as datacenters, the cloud, and even on edge devices. Despite where they run, distributing ML training seems the only way to attain scalable, high-quality learning. B ...
Systems and method for a task scheduler with dynamic adjustment of concurrency levels and task granularity are disclosed for improved execution of highly concurrent analytical and transactional systems. The task scheduler can avoid both over commitment and ...
2020
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Motivated by applications in shared mobility, we address the problem of allocating a group of agents to a set of resources to maximize a cumulative welfare objective. We model the welfare obtainable from each resource as a monotone DR-submodular function w ...
PMLR2021
Modern GPUs suffer from cache contention due to the limited cache size that is shared across tens of concurrently running warps. To increase the per-warp cache size prior techniques proposed warp throttling which limits the number of active warps. Warp thr ...
ASSOC COMPUTING MACHINERY2019
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The Internet of Things has revolutionized the lifestyle in all aspects. Considering the huge number of connected objects and the plethora of real-time services, edge computing approaches have emerged. Resource allocation is one of the most important challe ...
SPRINGER INTERNATIONAL PUBLISHING AG2019
An evolved real-time data processing strategy is proposed for high-energy physics experiments, and its implementation at the LHCb experiment is presented. The reduced event model allows not only the signal candidate firing the trigger to be persisted, as p ...
IOP PUBLISHING LTD2019
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DNN inference accelerators executing online services exhibit low average loads because of service demand variability, leading to poor resource utilization. Unfortunately, reclaiming idle inference cycles is difficult as other workloads can not execute on a ...
ACM2021
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Online Transaction Processing (OLTP) deployments are migrating from on-premise to cloud settings in order to exploit the elasticity of cloud infrastructure which allows them to adapt to workload variations. However, cloud adaptation comes at the cost of re ...
ASSOC COMPUTING MACHINERY2020
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Modern cloud data centers (DCs) need to tackle efficiently the increasing demand for computing resources and address the energy efficiency challenge. Therefore, it is essential to develop resource provisioning policies that are aware of virtual machine (VM ...
