What is he best known for?
The fields of study he is best known for:
- Operating system
- Central processing unit
- Programming language
Scott Mahlke mostly deals with Parallel computing, Compiler, Computer architecture, Embedded system and Instruction set.
His Parallel computing research incorporates elements of Scheduling and Thread.
Scott Mahlke interconnects Speculative execution and Basic block in the investigation of issues within Scheduling.
The Compiler study combines topics in areas such as Instruction-level parallelism, Multi-core processor and Dataflow.
In the field of Embedded system, his study on Application-specific integrated circuit overlaps with subjects such as Throughput.
His Instruction set research incorporates themes from Domain, Application domain and Computer engineering.
His most cited work include:
- Effective compiler support for predicated execution using the hyperblock (541 citations)
- The superblock: an effective technique for VLIW and superscalar compilation (534 citations)
- COMET: code offload by migrating execution transparently (296 citations)
What are the main themes of his work throughout his whole career to date?
Scott Mahlke mainly focuses on Parallel computing, Compiler, Embedded system, Computer architecture and Scheduling.
Speedup, Very long instruction word, Instruction-level parallelism, Superscalar and Speculative execution are the subjects of his Parallel computing studies.
His studies deal with areas such as Software, Instruction set and Code generation as well as Compiler.
His Embedded system research includes themes of Wireless, Software-defined radio, Redundancy, Multi-core processor and SIMD.
He has researched Computer architecture in several fields, including Scalability and Dataflow.
His study in the field of Software pipelining also crosses realms of Modulo.
He most often published in these fields:
- Parallel computing (45.73%)
- Compiler (32.08%)
- Embedded system (28.33%)
What were the highlights of his more recent work (between 2014-2021)?
- Parallel computing (45.73%)
- Speedup (10.58%)
- Real-time computing (5.12%)
In recent papers he was focusing on the following fields of study:
Scott Mahlke spends much of his time researching Parallel computing, Speedup, Real-time computing, Computation and Compiler.
His research in Parallel computing tackles topics such as Software which are related to areas like Scheduling.
The various areas that he examines in his Speedup study include Symmetric multiprocessor system, Deep learning, Kernel, Artificial intelligence and Memory bandwidth.
His Real-time computing study combines topics in areas such as Reduction, Set, Error detection and correction and Preemption.
His studies in Computation integrate themes in fields like Replication, Overhead, Computer engineering and Mobile device.
As a part of the same scientific family, he mostly works in the field of Compiler, focusing on Range and, on occasion, Optimizing compiler and Control.
Between 2014 and 2021, his most popular works were:
- Scalpel: Customizing DNN Pruning to the Underlying Hardware Parallelism (235 citations)
- Chimera: Collaborative Preemption for Multitasking on a Shared GPU (113 citations)
- Rumba: an online quality management system for approximate computing (91 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Central processing unit
- Programming language
Parallel computing, Speedup, Compiler, Graphics processing unit and Operating system are his primary areas of study.
His Parallel computing research is multidisciplinary, incorporating perspectives in Interleaved memory, Kernel and Pruning.
His research in Speedup intersects with topics in Vector processor, Instruction set, Memory bandwidth and Massively parallel.
His biological study spans a wide range of topics, including Range, Control and Approximate computing.
His work carried out in the field of Graphics processing unit brings together such families of science as Multiprocessing, Thread and Operand forwarding.
His work on Cache and Multithreading as part of general Operating system study is frequently linked to Register file, Memory data register and Index register, therefore connecting diverse disciplines of science.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
