The scientist’s investigation covers issues in Artificial intelligence, Parallel computing, Machine learning, Compiler and Feature extraction. The concepts of his Artificial intelligence study are interwoven with issues in Computer security, Threat model and Computation. His Parallel computing research is multidisciplinary, incorporating perspectives in Scheduling and Kernel.
In his study, Instruction-level parallelism, Software deployment and Data modeling is inextricably linked to Optimizing compiler, which falls within the broad field of Machine learning. His Compiler research is multidisciplinary, incorporating elements of Xeon, Shared memory, Multiprocessing, Data parallelism and Multi-core processor. His research in Feature extraction intersects with topics in Variety and Feature.
His primary areas of study are Artificial intelligence, Parallel computing, Machine learning, Compiler and Speedup. His work deals with themes such as Program optimization, Software portability and Kernel, which intersect with Parallel computing. His work in Machine learning addresses issues such as Heuristics, which are connected to fields such as Heuristic and Active learning.
His Compiler study combines topics in areas such as Instruction-level parallelism, Shared memory and Benchmark. His study in Speedup is interdisciplinary in nature, drawing from both Software, Task parallelism and Partition. His Task parallelism research focuses on subjects like Data parallelism, which are linked to Xeon.
Zheng Wang focuses on Parallel computing, Artificial intelligence, Program optimization, Overhead and Speedup. His Parallel computing study combines topics from a wide range of disciplines, such as Memory footprint, Filter and Row. His Artificial intelligence research incorporates themes from Java and Machine learning.
The Program optimization study combines topics in areas such as Symmetric multiprocessor system, Software portability, Variety, Overhead and Hardware architecture. Zheng Wang has included themes like Scheme, Iterative method, Optimization problem and Shared memory in his Overhead study. His studies in Speedup integrate themes in fields like Compiler, Greedy algorithm, Image tracing, Partition and SIMD.
His primary areas of study are Program optimization, Artificial intelligence, Deep learning, Parallel computing and Distributed computing. His research integrates issues of Java and Reduction in his study of Artificial intelligence. His work carried out in the field of Deep learning brings together such families of science as Recurrent neural network, Graph, Data compression, Embedded system and Machine translation.
Zheng Wang combines subjects such as Variety, Software portability and Overhead with his study of Parallel computing. His Distributed computing research focuses on Partition and how it relates to Speedup. He focuses mostly in the field of Multi-core processor, narrowing it down to topics relating to Software and, in certain cases, Source code, Code, Machine learning and Data modeling.
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Towards a holistic approach to auto-parallelization: integrating profile-driven parallelism detection and machine-learning based mapping
Georgios Tournavitis;Zheng Wang;Björn Franke;Michael F.P. O'Boyle.
programming language design and implementation (2009)
Mapping parallelism to multi-cores: a machine learning based approach
Zheng Wang;Michael F.P. O'Boyle.
acm sigplan symposium on principles and practice of parallel programming (2009)
Portable mapping of data parallel programs to OpenCL for heterogeneous systems
D. Grewe;Zheng Wang;M. F. P. O'Boyle.
symposium on code generation and optimization (2013)
Machine Learning in Compiler Optimization
Zheng Wang;Michael O'Boyle.
Proceedings of the IEEE (2018)
Smart multi-task scheduling for OpenCL programs on CPU/GPU heterogeneous platforms
Yuan Wen;Zheng Wang;Michael F. P. O'Boyle.
ieee international conference on high performance computing, data, and analytics (2014)
End-to-End Deep Learning of Optimization Heuristics
Chris Cummins;Pavlos Petoumenos;Zheng Wang;Hugh Leather.
international conference on parallel architectures and compilation techniques (2017)
Partitioning streaming parallelism for multi-cores: a machine learning based approach
Zheng Wang;Michael F.P. O'Boyle.
international conference on parallel architectures and compilation techniques (2010)
Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs
Yuting Wu;Xiao Liu;Yansong Feng;Zheng Wang.
international joint conference on artificial intelligence (2019)
CrossSense: Towards Cross-Site and Large-Scale WiFi Sensing
Jie Zhang;Zhanyong Tang;Meng Li;Dingyi Fang.
acm/ieee international conference on mobile computing and networking (2018)
Cracking Android pattern lock in five attempts
Guixin Ye;Zhanyong Tang;Dingyi Fang;Xiaojiang Chen.
network and distributed system security symposium (2017)
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