Titlebook: Euro-Par 2020: Parallel Processing; 26th International C Maciej Malawski,Krzysztof Rzadca Conference proceedings 2020 Springer Nature Switz

玉米

利用

A Learning-Based Approach for Evaluating the Capacity of Data Processing Pipelineson accuracy when predicting on new configurations and when the number of data sources changes. Furthermore, our analysis demonstrates that the best prediction results are obtained when metrics of different types are combined.

发微光

星球的光亮度

OmpMemOpt: Optimized Memory Movement for Heterogeneous Computingunderlying parallel programming model and implemented our optimization framework in the LLVM toolchain. We evaluated it with ten benchmarks and obtained a geometric speedup of 2.3., and reduced on average 50% of the total bytes transferred between the host and GPU.

AIL

Accelerating Deep Learning Inference with Cross-Layer Data Reuse on GPUsayers from state-of-the-art CNNs on two different GPU platforms, NVIDIA TITAN Xp and Tesla P4. The experiments show that the average speedup is 2.02 . on representative structures of CNNs, and 1.57. on end-to-end inference of SqueezeNet.

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Conference proceedings 2020nd, in August 2020. The conference was held virtually due to the coronavirus pandemic...The 39 full papers presented in this volume were carefully reviewed and selected from 158 submissions. They deal with parallel and distributed computing in general, focusing on support tools and environments; per

GIST

Parallel Scheduling of Data-Intensive Tasksdress the problem of parallel scheduling of a DAG of data-intensive tasks to minimize makespan. To do so, we propose greedy online scheduling algorithms that take load balancing, data dependencies, and data locality into account. Simulations and an experimental evaluation using an Apache Spark cluster demonstrate the advantages of our solutions.

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0302-9743 nd distributed programming, interfaces, and languages; multicore and manycore parallelism; parallel numerical methods and applications; and accelerator computing..978-3-030-57674-5978-3-030-57675-2Series ISSN 0302-9743 Series E-ISSN 1611-3349