Pavan Balaji

What is he best known for?

The fields of study he is best known for:

• Operating system
• Programming language
• Computer network

His main research concerns Parallel computing, Distributed computing, Programming paradigm, Operating system and Scalability. His work on Petascale computing as part of general Parallel computing research is frequently linked to Scale, bridging the gap between disciplines. Pavan Balaji interconnects Scheduling, Server and Interoperability in the investigation of issues within Distributed computing.

Pavan Balaji has included themes like Thread, POSIX Threads and Message Passing Interface in his Programming paradigm study. Pavan Balaji combines subjects such as Computer network and RDMA over Converged Ethernet with his study of Operating system. His biological study spans a wide range of topics, including Parallel programming model and Interface.

His most cited work include:

• Addressing failures in exascale computing (277 citations)
• A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems (209 citations)
• Meeting Report: The Terabase Metagenomics Workshop and the Vision of an Earth Microbiome Project (189 citations)

What are the main themes of his work throughout his whole career to date?

Parallel computing, Distributed computing, Scalability, Programming paradigm and Computer network are his primary areas of study. His Parallel computing study combines topics from a wide range of disciplines, such as Thread and Global Arrays. His Distributed computing research focuses on Multi-core processor and how it relates to Computer architecture.

His work in Scalability covers topics such as Interface which are related to areas like Semantics. His Programming paradigm research integrates issues from Software engineering and Interoperability. Many of his studies on Supercomputer apply to Software as well.

He most often published in these fields:

• Parallel computing (35.80%)
• Distributed computing (35.41%)
• Scalability (21.40%)

What were the highlights of his more recent work (between 2015-2021)?

• Parallel computing (35.80%)
• Scalability (21.40%)
• Thread (10.89%)

In recent papers he was focusing on the following fields of study:

Pavan Balaji focuses on Parallel computing, Scalability, Thread, Supercomputer and Distributed computing. His Parallel computing research incorporates elements of Multithreading, Scheduling, Programming paradigm and Programmer. His work deals with themes such as InfiniBand, Memory management, Deep learning, Artificial intelligence and Bottleneck, which intersect with Scalability.

The various areas that he examines in his Thread study include Instruction set, Software, Computer network, Multi-core processor and Implementation. His work on SW26010 is typically connected to Sequence assembly as part of general Supercomputer study, connecting several disciplines of science. Pavan Balaji has researched Distributed computing in several fields, including Input/output, Computation, Interface and Asynchronous communication.

Between 2015 and 2021, his most popular works were:

• A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems (209 citations)
• Survey of Techniques and Architectures for Designing Energy-Efficient Data Centers (120 citations)
• Argobots: A Lightweight Low-Level Threading and Tasking Framework (46 citations)

In his most recent research, the most cited papers focused on:

• Operating system
• Programming language
• Computer network

Pavan Balaji mostly deals with Distributed computing, Scalability, Parallel computing, Supercomputer and Thread. His research in Distributed computing intersects with topics in Input/output, Boosting and Interoperability. The Scalability study combines topics in areas such as Graph, Memory management, Deep learning, Artificial intelligence and Bottleneck.

A large part of his Parallel computing studies is devoted to Concurrent computing. His Supercomputer study combines topics in areas such as Address space, User space and Virtual address space. His study in Thread is interdisciplinary in nature, drawing from both Synchronization, Programming paradigm and Implementation.