Zobrazeno 1 - 10
of 94
pro vyhledávání: '"Atanas Rountev"'
Publikováno v:
2022 IEEE/ACM International Symposium on Code Generation and Optimization (CGO).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::20ca076d80c311d01099b45a84816efa
https://doi.org/10.1109/cgo53902.2022.9741281
https://doi.org/10.1109/cgo53902.2022.9741281
Publikováno v:
Proceedings of the ACM on Programming Languages. 4:1-24
Differential privacy has emerged as a leading theoretical framework for privacy-preserving data gathering and analysis. It allows meaningful statistics to be collected for a population without revealing ``too much'' information about any individual m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4f01c669907d49eae9c9858fffae664e
https://doi.org/10.1145/3428271
https://doi.org/10.1145/3428271
Publikováno v:
Proceedings of the 31st ACM SIGPLAN International Conference on Compiler Construction.
Training models with massive inputs is a significant challenge in the development of Deep Learning pipelines to process very large digital image datasets as required by Whole Slide Imaging (WSI) in computational pathology and analysis of brain fMRI i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6b83292740c6bb83f04f6c3c50e97c8b
https://doi.org/10.1145/3497776.3517766
https://doi.org/10.1145/3497776.3517766
Publikováno v:
Software Quality Journal. 28:335-367
Due to the widespread use of Android devices and apps, it is important to develop tools and techniques to improve app quality and performance. Our work focuses on a problem related to hardware sensors on Android devices: the failure to disable unneed
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fc3d77458e5c3814a720f53dc053cbd4
https://doi.org/10.1007/s11219-019-09484-z
https://doi.org/10.1007/s11219-019-09484-z
Autor:
Guillaume Iooss, Atanas Rountev, P. Sadayappan, Fabrice Rastello, Nicolas Tollenaere, Auguste Olivry
Publikováno v:
PLDI 2021-42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation
PLDI 2021-42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation, Jun 2021, Virtual, Canada. ⟨10.1145/3453483⟩
PLDI
PLDI 2021-42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation, Jun 2021, Virtual, Canada
PLDI 2021-42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation, Jun 2021, Virtual, Canada. ⟨10.1145/3453483⟩
PLDI
PLDI 2021-42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation, Jun 2021, Virtual, Canada
International audience; Evaluating the complexity of an algorithm is an important step when developing applications, as it impacts both its time and energy performance. Computational complexity, which is the number of dynamic operations regardless of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3bf09aaaff5eea355f085314494eef67
https://inria.hal.science/hal-03200539v2/file/main.pdf
https://inria.hal.science/hal-03200539v2/file/main.pdf
Publikováno v:
SPAA
Several efficient distributed algorithms have been developed for matrix-matrix multiplication: the 3D algorithm, the 2D SUMMA algorithm, and the 2.5D algorithm. Each of these algorithms was independently conceived and they trade-off memory needed per
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::71409b21f1edcbb051b5f51625968ffe
http://arxiv.org/abs/2105.13480
http://arxiv.org/abs/2105.13480
Publikováno v:
ASPLOS
Moving data through the memory hierarchy is a fundamental bottleneck that can limit the performance of core algorithms of machine learning, such as convolutional neural networks (CNNs). Loop-level optimization, including loop tiling and loop permutat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6f67d42a764b398c5bce8e05e4999687
https://doi.org/10.1145/3445814.3446759
https://doi.org/10.1145/3445814.3446759
Publikováno v:
ICSME
Mobile app analytics gathers detailed data about millions of app users. Both customers and governments are becoming increasingly concerned about the privacy implications of such data gathering. Thus, it is highly desirable to design privacy-preservin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3f80fac26a73bf4a08dbf57288c60386
https://doi.org/10.1109/icsme46990.2020.00034
https://doi.org/10.1109/icsme46990.2020.00034
Publikováno v:
CC
Program profiling is widely used to measure run-time execution properties---for example, the frequency of method and statement execution. Such profiling could be applied to deployed software to gain performance insights about the behavior of many ins
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::84f54c72d727b7ff69483c78b1758e6c
https://doi.org/10.1145/3377555.3377887
https://doi.org/10.1145/3377555.3377887
Autor:
Prashant Singh Rawat, Atanas Rountev, Mahesh Ravishankar, P. Sadayappan, Vinod Grover, Aravind Sukumaran-Rajam, Miheer Vaidya, Louis-Noël Pouchet
Publikováno v:
Proceedings of the IEEE. 106:1902-1920
Stencil computations arise in a number of computational domains. They exhibit significant data parallelism and are thus well suited for execution on graphical processing units (GPUs), but can be memory-bandwidth limited unless temporal locality is ut
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::acd932280e1a5a4c3fb11baf6de31ddd
https://doi.org/10.1109/jproc.2018.2862896
https://doi.org/10.1109/jproc.2018.2862896