玉米
发表于 2025-3-26 21:12:43
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利用
发表于 2025-3-27 04:11:31
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
发表于 2025-3-27 08:44:15
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星球的光亮度
发表于 2025-3-27 11:22:07
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
发表于 2025-3-27 17:29:51
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.
staging
发表于 2025-3-27 18:39:39
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不能仁慈
发表于 2025-3-27 21:59:03
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CODE
发表于 2025-3-28 03:32:11
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
发表于 2025-3-28 08:18:39
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.
delegate
发表于 2025-3-28 10:55:02
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