Zobrazeno 1 - 10
of 306
pro vyhledávání: '"Rami Melhem"'
Publikováno v:
Journal of Universal Computer Science, Vol 29, Iss 8, Pp 892-910 (2023)
At extreme scale, the frequency of silent errors – a class of errors that remain undetected by low-level error detection mechanisms – increases significantly with the computational complexity of the application and the scale of the computing infr
Externí odkaz:
https://doaj.org/article/7aa2f439398e4e409adf2d4281c01dcd
Publikováno v:
Energies, Vol 7, Iss 8, Pp 5151-5176 (2014)
As the demand for cloud computing continues to increase, cloud service providers face the daunting challenge to meet the negotiated SLA agreement, in terms of reliability and timely performance, while achieving cost-effectiveness. This challenge is i
Externí odkaz:
https://doaj.org/article/b8dd1f5295e64efba86241c5c5f64e5b
Publikováno v:
Proceedings of the VLDB Endowment. 13:783-797
In this paper, we propose a new parallelism model denoted as MPI * X and suggest a linear algebra-based graph analytics system, namely, Graphite, which effectively employs it. MPI * X promotes thread-based partitioning to distribute computation and c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c9ef485302858349c50c9fc12fd23073
https://doi.org/10.14778/3380750.3380751
https://doi.org/10.14778/3380750.3380751
Publikováno v:
2021 IEEE International Performance, Computing, and Communications Conference (IPCCC).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bc3b7d261d7e508438f3e51e36f15af1
https://doi.org/10.1109/ipccc51483.2021.9679435
https://doi.org/10.1109/ipccc51483.2021.9679435
Autor:
Panos K. Chrysanthis, Nikolas Parshook, Xiaoyu Ge, Erik Brunvand, Donald Kline, Rami Melhem, Alex K. Jones
Publikováno v:
Sustainable Computing: Informatics and Systems. 22:322-332
There is mounting evidence that manufacturing energy and environmental costs are a growing factor in the overall energy footprint of computing systems. The quantification of these impacts requires the evaluation of both the manufacturing and use phas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b9dc78f1a7062db75ae4985352679d9d
https://doi.org/10.1016/j.suscom.2017.10.001
https://doi.org/10.1016/j.suscom.2017.10.001
Publikováno v:
Integration. 64:105-113
Phase change memory (PCM) is a promising alternative to conventional DRAM main memories, due to its read performance, density, and nonvolatility and resulting low static energy. Unfortunately, reliability is still a significant challenge as limited w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c39d6ad692ba3ca684cb3b54949843cb
https://doi.org/10.1016/j.vlsi.2018.09.002
https://doi.org/10.1016/j.vlsi.2018.09.002
Publikováno v:
DATE
Hardware support for fault-driven page migration and on-demand memory allocation along with the advancements in unified memory runtime in modern graphics processing units (GPUs) simplify the memory management in discrete CPU-GPU heterogeneous memory
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4a5a4708837ca89a14e711a6c958a4c2
https://doi.org/10.23919/date51398.2021.9473982
https://doi.org/10.23919/date51398.2021.9473982
Publikováno v:
PACT
Efficient memory sharing among multiple compute engines plays an important role in shaping the overall application performance on CPU-GPU heterogeneous platforms. Unified Virtual Memory (UVM) is a promising feature that allows globally-visible data s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d137b50319f3a70e3c04e8b74161141b
https://doi.org/10.1145/3410463.3414633
https://doi.org/10.1145/3410463.3414633
Publikováno v:
HPEC
Once a Deep Neural Network (DNN) is trained, an inference algorithm retains the learning and applies it to batches of data. The trained DNN can be sparse because of pruning or following a preset sparse connectivity pattern. Inference in such sparse n
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c10c4426f49290faac13c4fd1026fb63
https://doi.org/10.1109/hpec43674.2020.9286189
https://doi.org/10.1109/hpec43674.2020.9286189
Publikováno v:
HPEC
Deep Neural Network (DNN) training and inference are two resource-intensive tasks that are usually scaled out using data or model parallelism where data parallelism parallelizes over the input data and model parallelism parallelizes over the network.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ae041901a82761f7f3efc29a75f8323e
https://doi.org/10.1109/hpec43674.2020.9286195
https://doi.org/10.1109/hpec43674.2020.9286195