A best-features based digital rotoscope

Murphy, Iain; Norlund, Tyler; Pallipuram, Vivek K.

doi:10.1109/acssc.2017.8335175

Cited by 5 publications

(5 citation statements)

References 7 publications

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“…Best-Features Digital Rotoscope-The best-features digital rotoscope 11 is a computer vision application that automatically adds animation to a video sequence in order to produce artistic videos. The application uses tasks that are both computationally-intensive and memory-intensive.…”

Section: Tested Applicationsmentioning

confidence: 99%

“…Due to frequent memory operations such as accessing neighborhood pixels, the application is predominantly memory-intensive. 11 Data migration scheduler-Data-migration is a crucial scheduling step in large data centers wherein the data layout is frequently updated to maintain the data availability. To evaluate the framework, we simulate the data-migration problem using three effective and yet memory-intensive schedulers including greedy matching, flatten-and-color, and edge ranking; all of them are highly memory-intensive.…”

Section: Tested Applicationsmentioning

confidence: 99%

“…The second test is the well-known n−1 2 (where n − 1 is the number of degrees of freedom) test 38 We evaluate the 2 statistic using Equation (11). In this study, the number of degrees of freedom is 11 (n = 12 Cloud instances) and the critical value from the chi-square table 38 at ( = 0.05) is 19.6.…”

Section: A2cloud-rf Framework Verificationmentioning

confidence: 99%

“…These tasks include finding image gradients, detecting corners, and performing watershed segmentation with the detected corners as markers. Due to frequent memory operations such as accessing neighborhood pixels, the application is predominantly memory‐intensive 11 …”

Section: Framework In Actionmentioning

confidence: 99%

“…We test the A2Cloud‐RF framework using eight applications including: Livermore Unstructured Lagrangian Explicit Shock Hydrodynamics (LULESH), 9 three spiking neural networks (SNNs), 10 best‐features based digital rotoscope, 11 and three data‐migration schedulers 12 . To verify the framework, we compare the A2Cloud forest generated for the two recommendation levels with the corresponding execution time random forests.…”

Section: Introductionmentioning

confidence: 99%

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A2Cloud‐RF: A random forest based statistical framework to guide resource selection forhigh‐performancescientific computing on the cloud

Samuel

Khan

Balos

et al. 2020

Concurrency and Computation

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This article proposes a random-forest based A2Cloud framework to match scientific applications with Cloud providers and their instances for high performance. The framework leverages four engines for this task: PERF engine, Cloud trace engine, A2Cloud-ext engine, and the random forest classifier (RFC) engine. The PERF engine profiles the application to obtain performance characteristics, including the number of single-precision (SP) floating-point operations (FLOPs), double-precision (DP) FLOPs, x87 operations, memory accesses, and disk accesses. The Cloud trace engine obtains the corresponding performance characteristics of the selected Cloud instances including: SP floating point operations per second (FLOPS), DP FLOPS, x87 operations per second, memory bandwidth, and disk bandwidth. The A2Cloud-ext engine uses the application and Cloud instance characteristics to generate objective scores that represent the application-to-Cloud match. The RFC engine uses these objective scores to generate two types of random forests to assist users with rapid analysis: application-specific random forests (ARF) and application-class based random forests. The ARF consider only the input application's characteristics to generate a random forest and provide numerical ratings to the selected Cloud instances. To generate the application-class based random forests, the RFC engine downloads the application profiles and scores of previously tested applications that perform similar to the input application. Using these data, the RFC engine creates a random forest for instance recommendation. We exhaustively test this framework using eight real-world applications across 12 instances from different Cloud providers. Our tests show significant statistical agreement between the instance ratings given by the framework and the ratings obtained via actual Cloud executions.

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Section: Tested Applicationsmentioning

confidence: 99%

Section: Tested Applicationsmentioning

confidence: 99%

Section: A2cloud-rf Framework Verificationmentioning

confidence: 99%

Section: Framework In Actionmentioning

confidence: 99%