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IEEE Communications Letters, 2021
The emergence of edge computing provides an effective solution to execute distributed model training (DMT). The deployment of training data among edge nodes affects the training efficiency and network resource usage.
Yajie Li +5 more
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The emergence of edge computing provides an effective solution to execute distributed model training (DMT). The deployment of training data among edge nodes affects the training efficiency and network resource usage.
Yajie Li +5 more
semanticscholar +1 more source
High-Level Stream and Data Parallelism in C++ for Multi-Cores
Brazilian Symposium on Programming Languages, 2021Stream processing applications have seen an increasing demand with the increased availability of sensors, IoT devices, and user data. Modern systems can generate millions of data items per day that require to be processed timely. To deal with this demand,
Junior Loff +3 more
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Parallel DEPSO-Scout: Data Parallelism [PDF]
2018 International Electrical Engineering Congress (iEECON), 2018 DEPSO-Scout is a hybrid optimization algorithm combining Differential Evolution (DE), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC). The solution convergence is balanced between exploration of PSO and exploitation from DE. The suboptimal solution has reduced by the scout bee property of ABC.
Prasitchai Boonserm +1 more
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Incremental flattening for nested data parallelism
ACM SIGPLAN Symposium on Principles & Practice of Parallel Programming, 2019Compilation techniques for nested-parallel applications that can adapt to hardware and dataset characteristics are vital for unlocking the power of modern hardware.
Troels Henriksen +3 more
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Measurement-Based Evaluation of Data-Parallelism for OpenCV Feature-Detection Algorithms
Annual International Computer Software and Applications Conference, 2018We investigate the effects on the execution time, shared cache usage and speed-up gains when using datapartitioned parallelism for the feature detection algorithms available in the OpenCV library.
Jakob Danielsson +4 more
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MALT: distributed data-parallelism for existing ML applications
European Conference on Computer Systems, 2015Machine learning methods, such as SVM and neural networks, often improve their accuracy by using models with more parameters trained on large numbers of examples.
Hao Li +3 more
semanticscholar +1 more source
Parallelizing data race detection
ACM SIGARCH Computer Architecture News, 2013Detecting data races in multithreaded programs is a crucial part of debugging such programs, but traditional data race detectors are too slow to use routinely. This paper shows how to speed up race detection by spreading the work across multiple cores.
Peter M. Chen +4 more
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Function-Parallel Computation in a Data-Parallel Environment
1993 International Conference on Parallel Processing - ICPP'93 Vol2, 1993Asynchromus problems are those which may be decomposed into a set of independenr sub-tasks which are suitable for concurrent execution. Th function paraIIeIism of these problems cannot normally be direcrly expressed using the data-parallel programming model.
Anthony P. Reeves, Alex L. Cheung
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Data-Parallel Programming on A Reconfigurable Parallel Computer
IETE Technical Review, 1998We present a preprocessor which converts programs written in a data parallel version of C into standard C to run on CDOT CHiPPS, a reconfigurable parallel computer. The main contribution is the development of rewriting methods and an optimizing protocol for inter-processor communication during barrier synchronization.
Ranjan K Sen +3 more
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Accelerating Federated Learning With Data and Model Parallelism in Edge Computing
IEEE/ACM Transactions on NetworkingRecently, edge AI has been launched to mine and discover valuable knowledge at network edge. Federated Learning, as an emerging technique for edge AI, has been widely deployed to collaboratively train models on many end devices in data-parallel fashion ...
Yunming Liao +5 more
semanticscholar +1 more source