2018 - ACM Distinguished Member
His primary scientific interests are in Parallel computing, Cache, Locality of reference, CUDA and General-purpose computing on graphics processing units. His studies in Parallel computing integrate themes in fields like Data flow diagram, Algorithm design and Software. The various areas that Xipeng Shen examines in his Cache study include Scalability, Thread, Distributed computing and Shared memory.
His Locality of reference research is multidisciplinary, incorporating perspectives in Program optimization and Cache miss. His Data mining research includes elements of Machine learning and Artificial intelligence. His Feature vector study, which is part of a larger body of work in Machine learning, is frequently linked to Jaccard index, bridging the gap between disciplines.
Xipeng Shen focuses on Parallel computing, Compiler, Artificial intelligence, Speedup and Distributed computing. He has researched Parallel computing in several fields, including Computer architecture and General-purpose computing on graphics processing units. His Compiler research is multidisciplinary, relying on both Software, Set, Profiling and Code.
His research in Artificial intelligence intersects with topics in Machine learning, Block and Natural language processing. Xipeng Shen focuses mostly in the field of Speedup, narrowing it down to topics relating to Theoretical computer science and, in certain cases, Heuristics. His research integrates issues of Schedule and Scheduling in his study of Distributed computing.
Artificial intelligence, Set, Machine learning, Computer engineering and Speedup are his primary areas of study. His Artificial intelligence study integrates concerns from other disciplines, such as Program synthesis and Code. Xipeng Shen interconnects Software analytics and Data set in the investigation of issues within Machine learning.
His Speedup study combines topics from a wide range of disciplines, such as Optimizing compiler, Program optimization, Compiler and Kernel. His Optimizing compiler study incorporates themes from Field-programmable gate array, High-level synthesis and Parallel computing. The Compiler study combines topics in areas such as Theoretical computer science and Programming paradigm.
His primary areas of investigation include Artificial intelligence, Computation, Speedup, Hyperparameter optimization and Machine learning. His study brings together the fields of Theoretical computer science and Artificial intelligence. His Computation research incorporates elements of Backpropagation, On the fly and Relaxation.
His Speedup research includes themes of Inference, Set, Data mining and Cluster analysis. His Set study which covers Deep learning that intersects with Convolutional neural network. His studies in Convolutional neural network integrate themes in fields like Compiler, Computer engineering, Pruning, Field and Composability.
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Learning multi-label scene classification
Matthew R. Boutell;Jiebo Luo;Xipeng Shen;Christopher M. Brown.
Pattern Recognition (2004)
Locality phase prediction
Xipeng Shen;Yutao Zhong;Chen Ding.
architectural support for programming languages and operating systems (2004)
Tuning for software analytics
Wei Fu;Tim Menzies;Xipeng Shen.
Information & Software Technology (2016)
On-the-fly elimination of dynamic irregularities for GPU computing
Eddy Z. Zhang;Yunlian Jiang;Ziyu Guo;Kai Tian.
architectural support for programming languages and operating systems (2011)
Analysis and approximation of optimal co-scheduling on chip multiprocessors
Yunlian Jiang;Xipeng Shen;Jie Chen;Rahul Tripathi.
international conference on parallel architectures and compilation techniques (2008)
Software behavior oriented parallelization
Chen Ding;Xipeng Shen;Kirk Kelsey;Chris Tice.
programming language design and implementation (2007)
Array regrouping and structure splitting using whole-program reference affinity
Yutao Zhong;Maksim Orlovich;Xipeng Shen;Chen Ding.
programming language design and implementation (2004)
Program locality analysis using reuse distance
Yutao Zhong;Xipeng Shen;Chen Ding.
ACM Transactions on Programming Languages and Systems (2009)
A cross-input adaptive framework for GPU program optimizations
Yixun Liu;Eddy Z. Zhang;Xipeng Shen.
international parallel and distributed processing symposium (2009)
Does cache sharing on modern CMP matter to the performance of contemporary multithreaded programs
Eddy Z. Zhang;Yunlian Jiang;Xipeng Shen.
acm sigplan symposium on principles and practice of parallel programming (2010)
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