What is he best known for?
The fields of study he is best known for:
- Operating system
- Central processing unit
- Programming language
The scientist’s investigation covers issues in Parallel computing, Distributed computing, Microarchitecture, Phase-change memory and Reliability engineering.
His Parallel computing research incorporates themes from Registered memory, Uniform memory access and Processor register.
As part of one scientific family, Tao Li deals mainly with the area of Distributed computing, narrowing it down to issues related to the Cloud computing, and often The Internet.
The Microarchitecture study combines topics in areas such as Multithreading, Soft error, Computer architecture, CUDA and General-purpose computing on graphics processing units.
His research investigates the link between Reliability engineering and topics such as Real-time computing that cross with problems in Power management, Information technology, Load following power plant and Power usage effectiveness.
His Paging research incorporates elements of Scalability, Embedded system and Speedup.
His most cited work include:
- Informed Microarchitecture Design Space Exploration Using Workload Dynamics (216 citations)
- Exploring Phase Change Memory and 3D Die-Stacking for Power/Thermal Friendly, Fast and Durable Memory Architectures (211 citations)
- Run-time modeling and estimation of operating system power consumption (210 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Parallel computing, Embedded system, Microarchitecture, Distributed computing and Operating system.
His studies in Parallel computing integrate themes in fields like Java and Scalability.
His research investigates the connection between Embedded system and topics such as Efficient energy use that intersect with problems in Energy consumption and Electronic engineering.
His Microarchitecture research includes elements of Soft error, Computer architecture, Cache, Reliability engineering and General-purpose computing on graphics processing units.
His Distributed computing study incorporates themes from Resource allocation, Workload, Bottleneck, Server and Renewable energy.
His research in Server intersects with topics in Power management and Cloud computing.
He most often published in these fields:
- Parallel computing (22.22%)
- Embedded system (21.16%)
- Microarchitecture (15.34%)
What were the highlights of his more recent work (between 2015-2021)?
- Embedded system (21.16%)
- Distributed computing (13.76%)
- Parallel computing (22.22%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Embedded system, Distributed computing, Parallel computing, Server and Speedup.
His Embedded system research is multidisciplinary, incorporating elements of Interleaved memory, Memory management, Energy storage and Cache.
His study focuses on the intersection of Distributed computing and fields such as Renewable energy with connections in the field of Network switch and Solar energy.
Tao Li has researched Parallel computing in several fields, including Synchronization and Scalability.
The study incorporates disciplines such as Workload, Power management, Data center, Scheduling and Cloud computing in addition to Server.
He focuses mostly in the field of Speedup, narrowing it down to matters related to Artificial intelligence and, in some cases, Machine learning.
Between 2015 and 2021, his most popular works were:
- A Survey on End-Edge-Cloud Orchestrated Network Computing Paradigms: Transparent Computing, Mobile Edge Computing, Fog Computing, and Cloudlet (159 citations)
- Edge-Oriented Computing Paradigms: A Survey on Architecture Design and System Management (76 citations)
- Performance-Aware Model for Sparse Matrix-Matrix Multiplication on the Sunway TaihuLight Supercomputer (49 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Central processing unit
- Programming language
His scientific interests lie mostly in Speedup, Distributed computing, Parallel computing, Server and Computation.
He combines subjects such as Deep learning, Unsupervised learning and Artificial intelligence with his study of Speedup.
His work deals with themes such as Synchronization, Backpropagation, Computer architecture and Microarchitecture, which intersect with Unsupervised learning.
His study looks at the relationship between Distributed computing and topics such as Cloud computing, which overlap with The Internet and Data modeling.
Many of his studies involve connections with topics such as Scalability and Parallel computing.
His Server research integrates issues from Workload, Power management, Real-time computing, Scheduling and Differentiated services.
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