the proceedings contain 39 papers. the special focus in this conference is on parallel and distributedcomputing. the topics include: A makespan lower bound for the tiled cholesky factorization based on ALAP schedule;...
ISBN:
(纸本)9783030576745
the proceedings contain 39 papers. the special focus in this conference is on parallel and distributedcomputing. the topics include: A makespan lower bound for the tiled cholesky factorization based on ALAP schedule;preface;skipping non-essential instructions makes data-dependence profiling faster;Optimal GPU-CPU offloading strategies for deep neural network training;improving mapping for sparse direct solvers: A trade-off between data locality and load balancing;modelling standard and randomized slimmed folded clos networks;ompMemOpt: Optimized memory movement for heterogeneous computing;Accelerating deep learning inference with cross-layer data reuse on GPUs;distributed fine-grained traffic speed prediction for large-scale transportation networks based on automatic LSTM customization and sharing;Optimizing FFT-based convolution on ARMv8 multi-core CPUs;maximizing I/O bandwidth for reverse time migration on heterogeneous large-scale systems;TorqueDB: distributed querying of time-series data from edge-local storage;data-centric distributedcomputing on networks of mobile devices;a toolchain to verify the parallelization of OmpSs-2 applications;WPSP: A multi-correlated weighted policy for VM selection and migration for cloud computing;LCP-aware parallel string sorting;Mobile RAM and shape formation by programmable particles;approximation algorithm for estimating distances in distributed virtual environments;on the power of randomization in distributed algorithms in dynamic networks with adaptive adversaries;3D coded SUMMA: Communication-efficient and robust parallel matrix multiplication;managing failures in task-based parallel workflows in distributedcomputing environments;accelerating nested data parallelism: Preserving regularity;using dynamic broadcasts to improve task-based runtime performances;a compression-based design for higher throughput in a lock-free hash map;NV-PhTM: An efficient phase-based transactional system for non-volatile memory.
Fog computing is a new paradigm used to process high-intensive tasks from IoT devices with low latency requirements. therefore, to improve the quality of services and user satisfaction the offloaded tasks should be as...
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We consider the case of a set of energy harvesting edge nodes, equipped with photovoltaic panels that implement some kind of monitoring service. To ensure that the service operates in an optimal way, nodes have someti...
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ISBN:
(纸本)9798350339826
We consider the case of a set of energy harvesting edge nodes, equipped with photovoltaic panels that implement some kind of monitoring service. To ensure that the service operates in an optimal way, nodes have sometimes offload some of their data to other nodes. We show that this kind of task offloading (migration) can improve service performance by avoiding temporary interruptions and prolonging the overall service lifetime. We present a centralized algorithm based on Linear Programming optimization problem solution and a distributed implementation.
the current battlefield environment is complex, with numerous and unevenly distributed targets in the attack area. It is difficult for commanders to make accurate real-time choices on the minimum amount of bombs to be...
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distributed databases are often used when scalability, fault tolerance, and high availability are crucial. they excel in scenarios where traditional, centralized databases may struggle to handle the increasing volume ...
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Deep neural networks (DNNs) require distributed training strategies to deal with large data sizes. TensorFlow is one of the most widely used frameworks that support distributed training. Among the TensorFlow training ...
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Withthe emergence of IoT, new computing paradigms have also emerged. Initial IoT systems had all the computing happening on the cloud. Withthe emergence of Industry 4.0 and IoT being the major building block, clouds...
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the application of GPU to accelerate large-scale smoke simulation is a hot research topic in computational fluid dynamics. However, the current smoke parallelcomputing methods for different scale smoke flow field, th...
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Withthe continuous advancement of large-scale models and expanding volumes of data, a single acceleration hardware is no longer sufficient to meet the training demands. Simply stacking multiple acceleration hardware ...
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Cloud data centers, comprising a diverse set of heterogeneous resources working collaboratively to achieve high-performance computing, face the challenge of resource dynamism, where performance fluctuates over time. T...
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