Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Pan, Yuechao"'
Autor:
Tschand, Arya, Rajan, Arun Tejusve Raghunath, Idgunji, Sachin, Ghosh, Anirban, Holleman, Jeremy, Kiraly, Csaba, Ambalkar, Pawan, Borkar, Ritika, Chukka, Ramesh, Cockrell, Trevor, Curtis, Oliver, Fursin, Grigori, Hodak, Miro, Kassa, Hiwot, Lokhmotov, Anton, Miskovic, Dejan, Pan, Yuechao, Manmathan, Manu Prasad, Raymond, Liz, John, Tom St., Suresh, Arjun, Taubitz, Rowan, Zhan, Sean, Wasson, Scott, Kanter, David, Reddi, Vijay Janapa
Rapid adoption of machine learning (ML) technologies has led to a surge in power consumption across diverse systems, from tiny IoT devices to massive datacenter clusters. Benchmarking the energy efficiency of these systems is crucial for optimization
Externí odkaz:
http://arxiv.org/abs/2410.12032
Autor:
Zou, Xiaoqiang, Khan, Imad, Wang, Yanxi, Hussain, Mudassar, Jiang, Bangzhi, Zheng, Lei, Pan, Yuechao, Hu, Jijie, Khalid, Muhammad Umair
Publikováno v:
In Food Chemistry 15 October 2024 455
Autor:
Hussain, Mudassar, Khan, Imad, Chaudhary, Muneeba Naseer, Ali, Khubaib, Mushtaq, Anam, Jiang, Bangzhi, Zheng, Lei, Pan, Yuechao, Hu, Jijie, Zou, Xiaoqiang
Publikováno v:
In Chemistry and Physics of Lipids October 2024 264
Autor:
Zou, Xiaoqiang, Hussain, Mudassar, Khan, Imad, Wang, Yanxi, Jiang, Bangzhi, Zheng, Lei, Pan, Yuechao, Hu, Jijie, Ashraf, Azqa
Publikováno v:
In Food Bioscience June 2024 59
Publikováno v:
In Food Hydrocolloids March 2024 148 Part A
Autor:
Khan, Imad, Hussain, Mudassar, Jiang, Bangzhi, Zheng, Lei, Pan, Yuechao, Hu, Jijie, Khan, Adil, Ashraf, Azqa, Zou, Xiaoqiang
Publikováno v:
In Progress in Lipid Research November 2023 92
On a GPU cluster, the ratio of high computing power to communication bandwidth makes scaling breadth-first search (BFS) on a scale-free graph extremely challenging. By separating high and low out-degree vertices, we present an implementation with sca
Externí odkaz:
http://arxiv.org/abs/1803.03922
Autor:
Wang, Yangzihao, Pan, Yuechao, Davidson, Andrew, Wu, Yuduo, Yang, Carl, Wang, Leyuan, Osama, Muhammad, Yuan, Chenshan, Liu, Weitang, Riffel, Andy T., Owens, John D.
For large-scale graph analytics on the GPU, the irregularity of data access and control flow, and the complexity of programming GPUs, have presented two significant challenges to developing a programmable high-performance graph library. "Gunrock", ou
Externí odkaz:
http://arxiv.org/abs/1701.01170
We present a single-node, multi-GPU programmable graph processing library that allows programmers to easily extend single-GPU graph algorithms to achieve scalable performance on large graphs with billions of edges. Directly using the single-GPU imple
Externí odkaz:
http://arxiv.org/abs/1504.04804
For large-scale graph analytics on the GPU, the irregularity of data access and control flow, and the complexity of programming GPUs have been two significant challenges for developing a programmable high-performance graph library. "Gunrock", our gra
Externí odkaz:
http://arxiv.org/abs/1501.05387