Zobrazeno 1 - 10
of 47
pro vyhledávání: '"YOUWEI ZHUO"'
Autor:
YOUWEI ZHUO1, JINGJI CHEN1, GENGYU RAO1, QINYI LUO1, YANZHI WANG2, HAILONG YANG3, DEPEI QIAN3, XUEHAI QIAN1
Publikováno v:
ACM Transactions on Computer Systems. Jan2021, Vol. 37 Issue 1-4, p1-37. 37p.
Autor:
Qinyi Luo, Yanzhi Wang, Depei Qian, Youwei Zhuo, Xuehai Qian, Jingji Chen, Hailong Yang, Gengyu Rao
Publikováno v:
ACM Transactions on Computer Systems. 37:1-37
To hide the complexity of the underlying system, graph processing frameworks ask programmers to specify graph computations in user-defined functions (UDFs) of graph-oriented programming model. Due to the nature of distributed execution, current frame
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cb6ec234eb0d5fe56417e470026dd6a6
https://doi.org/10.1145/3453681
https://doi.org/10.1145/3453681
Publikováno v:
PLDI
Graph analytics is an important way to understand relationships in real-world applications. At the age of big data, graphs have grown to billions of edges. This motivates distributed graph processing. Graph processing frameworks ask programmers to sp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7d77720d970393b79478b0ebe3918e9a
https://doi.org/10.1145/3385412.3385961
https://doi.org/10.1145/3385412.3385961
Publikováno v:
ASPLOS
Distributed deep learning training usually adopts All-Reduce as the synchronization mechanism for data parallel algorithms due to its high performance in homogeneous environment. However, its performance is bounded by the slowest worker among all wor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2487f7248152f197eda6b53919f063de
https://doi.org/10.1145/3373376.3378499
https://doi.org/10.1145/3373376.3378499
Publikováno v:
HPCA
Deep neural network (DNN) accelerators as an example of domain-specific architecture have demonstrated great success in DNN inference. However, the architecture acceleration for equally important DNN training has not yet been fully studied. With data
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4a0b7b149cb3baf52497fac09c43a759
https://doi.org/10.1109/hpca47549.2020.00036
https://doi.org/10.1109/hpca47549.2020.00036
Publikováno v:
MICRO
Processing-In-Memory (PIM) architectures based on recent technology advances (e.g., Hybrid Memory Cube) demonstrate great potential for graph processing. However, existing solutions did not address the key challenge of graph processing---irregular da
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d93468d6500017784db02f428e2a245d
https://doi.org/10.1145/3352460.3358256
https://doi.org/10.1145/3352460.3358256
Autor:
Qinru Qiu, Yanzhi Wang, Zhe Li, Caiwen Ding, Wenyao Xu, Wujie Wen, Youwei Zhuo, Xuehai Qian, Siyue Wang, Chang Liu, Xue Lin
Publikováno v:
HPCA
Recurrent Neural Networks (RNNs) are becoming increasingly important for time series-related applications which require efficient and real-time implementations. The two major types are Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) netw
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::919904bc1f788bce174b33af791fa094
https://doi.org/10.1109/hpca.2019.00028
https://doi.org/10.1109/hpca.2019.00028
Publikováno v:
HPCA
With the rise of artificial intelligence in recent years, Deep Neural Networks (DNNs) have been widely used in many domains. To achieve high performance and energy efficiency, hardware acceleration (especially inference) of DNNs is intensively studie
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8287e1c28bffd7862caa97ae301da201
http://arxiv.org/abs/1901.02067
http://arxiv.org/abs/1901.02067
Publikováno v:
MICRO
Finite State Machine (FSM) is known to be embarrassingly sequential because the next state depends on the current state and input symbol. Enumerative FSM breaks the data dependencies by cutting the input symbols into segments and processing all segme
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2253fd94d032acda039f3b846e4d2b5a
https://doi.org/10.1109/micro.2018.00012
https://doi.org/10.1109/micro.2018.00012
Publikováno v:
ASPLOS
Many important graph applications are iterative algorithms that repeatedly process the input graph until convergence. For such algorithms, graph abstraction is an important technique: although much smaller than the original graph, it can bootstrap an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::97f10e0a6f34bc377c07ea44e07ef0ac
https://doi.org/10.1145/3173162.3173208
https://doi.org/10.1145/3173162.3173208