What is he best known for?
The fields of study he is best known for:
- Operating system
- Computer network
- Software
His scientific interests lie mostly in Operating system, Distributed computing, Versioning file system, Embedded system and Computer data storage.
His work deals with themes such as Trojan and Information assurance, which intersect with Operating system.
The concepts of his Distributed computing study are interwoven with issues in Scheduling, Cloud computing, Protocol and Implementation.
His Versioning file system research integrates issues from Unix file types and File system.
He has included themes like Disk formatting, SCSI, Converged storage, I/O scheduling and Software in his Embedded system study.
In the subject of general Computer data storage, his work in Information repository is often linked to Throughput and Cache management, thereby combining diverse domains of study.
His most cited work include:
- Heterogeneity and dynamicity of clouds at scale: Google trace analysis (735 citations)
- Object-based storage (501 citations)
- Safe and effective fine-grained TCP retransmissions for datacenter communication (397 citations)
What are the main themes of his work throughout his whole career to date?
Operating system, Distributed computing, Server, Embedded system and Computer network are his primary areas of study.
His work on Computer appliance, Versioning file system and Computer file as part of general Operating system research is often related to IBM, thus linking different fields of science.
His Distributed computing research incorporates themes from Scalability, Workload, Exploit, Scheduling and Cloud computing.
Within one scientific family, Gregory R. Ganger focuses on topics pertaining to Real-time computing under Workload, and may sometimes address concerns connected to Tracing, Data mining and Distributed data store.
His Server study incorporates themes from File server, Byzantine fault tolerance, Protocol, Communications protocol and Computer data storage.
His study in Embedded system is interdisciplinary in nature, drawing from both Host-based intrusion detection system, Converged storage, Intrusion detection system, Storage area network and Firmware.
He most often published in these fields:
- Operating system (25.08%)
- Distributed computing (21.45%)
- Server (14.52%)
What were the highlights of his more recent work (between 2012-2021)?
- Distributed computing (21.45%)
- Parallel computing (6.27%)
- Distributed data store (5.28%)
In recent papers he was focusing on the following fields of study:
Gregory R. Ganger focuses on Distributed computing, Parallel computing, Distributed data store, Database and Cloud computing.
His Distributed computing study combines topics from a wide range of disciplines, such as Exploit, Consistency model, Scheduling and Resource.
His Parallel computing research is multidisciplinary, incorporating perspectives in Artificial neural network, Scalability, Thread and Computation.
His Distributed data store study combines topics in areas such as File system, Operating system, Distributed File System, Embedded system and Node.
While working in this field, Gregory R. Ganger studies both Operating system and Free space.
His research investigates the connection between Cloud computing and topics such as Workload that intersect with problems in Performance prediction.
Between 2012 and 2021, his most popular works were:
- GeePS: scalable deep learning on distributed GPUs with a GPU-specialized parameter server (192 citations)
- Gaia: geo-distributed machine learning approaching LAN speeds (157 citations)
- PipeDream: generalized pipeline parallelism for DNN training (116 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Computer network
- Programming language
His primary areas of investigation include Distributed computing, Parallel computing, Workload, Thread and Scheduling.
His Distributed computing research incorporates themes from Aggregate, Transfer of learning, Consistency model, Exploit and Event.
His study in Parallel computing is interdisciplinary in nature, drawing from both Scalability and Computation.
His study looks at the intersection of Workload and topics like Latency with Quality of service and Web service.
In his study, which falls under the umbrella issue of Thread, Model of computation, Cache and PageRank is strongly linked to Bulk synchronous parallel.
His Scheduling research integrates issues from Reservation system, Reservation and Cloud computing.
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