Zobrazeno 1 - 10
of 69
pro vyhledávání: '"Kevin J. Barker"'
Publikováno v:
IEEE Internet Computing. 27:7-14
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3ebd4b8fd0be3158a75be6bc62426004
https://doi.org/10.1109/mic.2022.3217423
https://doi.org/10.1109/mic.2022.3217423
Autor:
Cheng Tan, Nicolas Bohm Agostini, Tong Geng, Chenhao Xie, Jiajia Li, Ang Li, Kevin J. Barker, Antonino Tumeo
Publikováno v:
2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f2801a28f5bc9b9b81a2fd118ff7e409
https://doi.org/10.1109/hpca53966.2022.00030
https://doi.org/10.1109/hpca53966.2022.00030
Autor:
Shuaiwen Leon Song, Kevin J. Barker, Jiajia Li, Nathan R. Tallent, Xu Liu, Jieyang Chen, Ang Li
Publikováno v:
IEEE Transactions on Parallel and Distributed Systems. 31:94-110
High performance multi-GPU computing becomes an inevitable trend due to the ever-increasing demand on computation capability in emerging domains such as deep learning, big data and planet-scale simulations. However, the lack of deep understanding on
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7e744ebdb46ce1d11b979c42d1a458e1
https://doi.org/10.1109/tpds.2019.2928289
https://doi.org/10.1109/tpds.2019.2928289
Autor:
Jieyang Chen, Mark Raugas, Jesun Sahariar Firoz, Ang Li, Shuaiwen Leon Song, Chenhao Xie, Kevin J. Barker, Jiajia Li
Publikováno v:
ICPP
Designing efficient and scalable sparse linear algebra kernels on modern multi-GPU based HPC systems is a challenging task due to significant irregular memory references and workload imbalance across GPUs. These challenges are particularly compounded
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cbb54f5c54934ee0e6bc9f7425cbd90f
https://doi.org/10.1145/3472456.3472478
https://doi.org/10.1145/3472456.3472478
Autor:
Marco Minutoli, Cheng Tan, Nicolas Bohm Agostini, Chenhao Xie, Ang Li, Tong Geng, Vito Giovanni Castellana, Antonino Tumeo, Jeff L. Zhang, Kevin J. Barker
Publikováno v:
ASAP
Reconfigurable architectures are today experiencing a renewed interest for their ability to provide specialization without sacrificing the capability to adapt to disparate workloads. Coarse-grained reconfigurable arrays (CGRAs) provide higher flexibi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::04163108933f40f11dc21136ace37234
https://doi.org/10.1109/asap52443.2021.00029
https://doi.org/10.1109/asap52443.2021.00029
Publikováno v:
DATE
Coarse-grained reconfigurable arrays (CGRAs), loosely defined as arrays of functional units interconnected through a network-on-chip (NoC), provide higher flexibility than domain-specific ASIC accelerators while offering increased hardware efficiency
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::360dfc56fee180df4dfb77b7bac35903
https://doi.org/10.23919/date51398.2021.9473955
https://doi.org/10.23919/date51398.2021.9473955
Autor:
Mahesh Lakshminarasimhan, Xiaolong Wu, Catherine Olschanowsky, Kevin J. Barker, Jiajia Li, Ang Li
Publikováno v:
IISWC
Tensor computations present significant performance challenges that impact a wide spectrum of applications ranging from machine learning, healthcare analytics, social network analysis, data mining to quantum chemistry and signal processing. Efforts t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b9908e896546c5df62072506a6ae5cbf
https://doi.org/10.1109/iiswc50251.2020.00027
https://doi.org/10.1109/iiswc50251.2020.00027
Publikováno v:
ICCD
Coarse-grained reconfigurable arrays (CGRAs), loosely defined as arrays of functional units (e.g., adder, subtractor, multiplier, divider, or larger multi-operation units, but smaller than a general-purpose core) interconnected through a Network-on-C
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::74fac462415f54af12d558bd4999cccd
https://doi.org/10.1109/iccd50377.2020.00070
https://doi.org/10.1109/iccd50377.2020.00070
Publikováno v:
HPEC
Today's data-driven analytics and machine learning workload have been largely driven by the General-Purpose Graphics Processing Units (GPGPUs). To accelerate dense matrix multiplications on the GPUs, Tensor Core Units (TCUs) have been introduced in r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5318ac81339a4fa843508768a86abfdb
https://doi.org/10.1109/hpec43674.2020.9286152
https://doi.org/10.1109/hpec43674.2020.9286152
Publikováno v:
ICPP
This paper presents a workload classification framework that accurately discriminates illicit computation from authorized workloads on GPU-accelerated HPC systems at runtime. As such systems become increasingly powerful and widely-adopted, attackers
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cbe76ea4a51e407da88d947e441daba2
https://doi.org/10.1145/3404397.3404435
https://doi.org/10.1145/3404397.3404435