Zobrazeno 1 - 10
of 54
pro vyhledávání: '"Surendra Byna"'
Autor:
Surendra Byna
Publikováno v:
Fifth International Workshop on Systems and Network Telemetry and Analytics.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b17630654beba66073430f1ecd29b096
https://doi.org/10.1145/3526064.3534114
https://doi.org/10.1145/3526064.3534114
Autor:
Surendra Byna
Publikováno v:
2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::960b3a18842023d8ef30a0f8a5d586fd
https://doi.org/10.1109/ipdpsw55747.2022.00006
https://doi.org/10.1109/ipdpsw55747.2022.00006
Autor:
Surendra Byna, D. Robinson, Elena Pourmal, Quincey Koziol, Bin Dong, Jerome Soumagne, Venkatram Vishwanath, Scot Breitenfeld, Richard Warren, Houjun Tang
Publikováno v:
Journal of Computer Science and Technology, vol 35, iss 1
Scientific applications at exascale generate and analyze massive amounts of data. A critical requirement of these applications is the capability to access and manage this data efficiently on exascale systems. Parallel I/O, the key technology enables
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::edb88ffbbbcf5b50bfc003a572f4da58
https://escholarship.org/uc/item/9x80d9n2
https://escholarship.org/uc/item/9x80d9n2
Publikováno v:
ACM Transactions on Parallel Computing, vol 5, iss 4
Parallel Input output is an essential component of modern high-performance computing (HPC). Obtaining good I/O performance for a broad range of applications on diverse HPC platforms is a major challenge, in part, because of complex inter dependencies
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ab01a02ffc5c46299bd8dd08f05d993b
https://escholarship.org/uc/item/2qw4b7sr
https://escholarship.org/uc/item/2qw4b7sr
Publikováno v:
HPDC
User-Defined Functions (UDF) allow application programmers to specify analysis operations on data, while leaving the data management tasks to the system. This general approach enables numerous custom analysis functions and is at the heart of the mode
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b20a901ebdd841d615404a8029adefde
https://doi.org/10.1145/3078597.3078599
https://doi.org/10.1145/3078597.3078599
Autor:
Kesheng Wu, Surendra Byna, Houjun Tang, Stephen Ranshous, Daniel F. Martin, Nagiza F. Samatova, Wenzhao Zhang, Bin Dong, Scott Klasky
Publikováno v:
IEEE BigData
Runtime data sharing across applications is of great importance for avoiding high I/O overhead for scientific data analytics. Sharing data on a staging space running on a set of dedicated compute nodes is faster than writing data to a slow disk-based
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0ea4fb7eac9eff2c06bc6429ade01e59
https://doi.org/10.1109/bigdata.2016.7840743
https://doi.org/10.1109/bigdata.2016.7840743
Publikováno v:
HPDC
Parallel sorting is an essential algorithm in large-scale data analytics using distributed memory systems. As the number of processes increases, existing parallel sorting algorithms could become inefficient because of the unbalanced workload. A commo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f3b6c3b16cf773cbebdc8a538ececc61
https://doi.org/10.1145/2907294.2907300
https://doi.org/10.1145/2907294.2907300
Autor:
Xiaocheng Zou, Surendra Byna, Nagiza F. Samatova, Wenzhao Zhang, Daniel F. Martin, Steve Harenberg, Bin Dong, Dharshi Devendran, Houjun Tang, Kesheng Wu, Scott Klasky
Publikováno v:
CCGrid
Zhang, Wenzhao; Tang, Houjun; Harenberg, Steven; Byna, Suren; Zou, Xiaocheng; Devendran, Dharshi; et al.(2016). AMRZone: A Runtime AMR Data Sharing Framework For Scientific Applications:. Lawrence Berkeley National Laboratory: Lawrence Berkeley National Laboratory. Retrieved from: http://www.escholarship.org/uc/item/2039k2m6
Zhang, Wenzhao; Tang, Houjun; Harenberg, Steven; Byna, Suren; Zou, Xiaocheng; Devendran, Dharshi; et al.(2016). AMRZone: A Runtime AMR Data Sharing Framework For Scientific Applications:. Lawrence Berkeley National Laboratory: Lawrence Berkeley National Laboratory. Retrieved from: http://www.escholarship.org/uc/item/2039k2m6
Frameworks that facilitate runtime data sharing across multiple applications are of great importance for scientific data analytics. Although existing frameworks work well over uniform mesh data, they can not effectively handle adaptive mesh refinemen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::86f4e67f4868b9f010ff475d75419f99
https://doi.org/10.1109/ccgrid.2016.62
https://doi.org/10.1109/ccgrid.2016.62
Publikováno v:
ICCS
We present TECA, a parallel toolkit for detecting extreme events in large climate datasets. Modern climate datasets expose parallelism across a number of dimensions: spatial locations, timesteps and ensemble members. We design TECA to exploit these m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e2a64afaced24c9a47a6b137bed42dfc
https://doi.org/10.1016/j.procs.2012.04.093
https://doi.org/10.1016/j.procs.2012.04.093
Publikováno v:
Journal of Computer Science and Technology. 22:641-652
Data access delay is a major bottleneck in utilizing current high-end computing (HEC) machines. Prefetching, where data is fetched before CPU demands for it, has been considered as an effective solution to masking data access delay. However, current
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::09055f2515c26354a43da674584f16a4
https://doi.org/10.1007/s11390-007-9090-y
https://doi.org/10.1007/s11390-007-9090-y