An Intermediate Library for Multi-GPUs Computing Skeletons

Le, Duc Tung; Nguyen, Huu Duc; Pham, Tuan Anh; Ngo, Huy Hoang; Nguyen, Minh Thap

doi:10.1109/rivf.2012.6169863

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…The utilization of techniques such as recursive feature adding (RFA) and recursive feature updating (RFU) provides opportunities to adjust machine learning models in response to evolving patterns in fraudulent conduct [26]. Through the process of continuously assessing the impact of different aspects over a period of time, these methods guarantee that the fraud detection system stays flexible and capable of adapting to the changing characteristics of financial fraud.…”

Section: Related Workmentioning

confidence: 99%

Credit Card Fraud Detection: Comparison of Different Machine Learning Techniques

Kilickaya

2024

IJLEMR

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Credit Card Fraud can be defined as the situation in which a person uses someone else's credit card for personal reasons and the cardholder and the card issuer are unaware of the use of the relevant credit card. In this study, credit card fraud detection models were developed. R programming language was used. During the engineering applications of this model, different machine learning algorithms were used for the same data set. Relevant performance curves were drawn for the models. Data has been analyzed and visualized to distinguish credit card fraud from other data types. In this context, different machine learning techniques used. These are logistic regression, decision tree, neural networks and gradient boosting. The aforementioned techniques were used to solve the problem of fraud/not fraud within the scope of the study. However, the results obtained are generally give idea for all classification problems. According the findings of this study, the fastest results among these techniques were found with artificial neural networks. Gradient boosting and decision tree technique are related to each other. It is a collection of weak prediction models with gradient boosting decision trees. The most effective and well-known technique from these four techniques is logistic regression.

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Section: Related Workmentioning

confidence: 99%

Credit Card Fraud Detection: Comparison of Different Machine Learning Techniques

Kilickaya

2024

IJLEMR

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“…Skeletons, moreover, have the advantage of representing language‐independent patterns of parallelism, which can more easily be transferred to other language settings. Research into skeletons for GPU programming, while limited, has produced a number of skeleton libraries for a few languages, including C++, 30 F#, 31 and Haskell 32 . However, all of these libraries only support GPUs.…”

Section: Related Workmentioning

confidence: 99%

Refactoring for introducing and tuning parallelism for heterogeneous multicore machines in Erlang

Janjić

Brown

Barwell

et al. 2019

Concurrency and Computation

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Summary This paper presents semi‐automatic software refactorings to introduce and tune structured parallelism in sequential Erlang code, as well as to generate code for running computations on GPUs and possibly other accelerators. Our refactorings are based on the lapedo framework for programming heterogeneous multi‐core systems in Erlang. lapedo is based on the PaRTE refactoring tool and also contains (1) a set of hybrid skeletons that target both CPU and GPU processors, (2) novel refactorings for introducing and tuning parallelism, and (3) a tool to generate the GPU offloading and scheduling code in Erlang, which is used as a component of hybrid skeletons. We demonstrate, on four realistic use‐case applications, that we are able to refactor sequential code and produce heterogeneous parallel versions that can achieve significant and scalable speedups of up to 220 over the original sequential Erlang program on a 24‐core machine with a GPU.

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An Intermediate Library for Multi-GPUs Computing Skeletons

Cited by 2 publications

References 3 publications

Credit Card Fraud Detection: Comparison of Different Machine Learning Techniques

Credit Card Fraud Detection: Comparison of Different Machine Learning Techniques

Refactoring for introducing and tuning parallelism for heterogeneous multicore machines in Erlang

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