Kunle Olukotun focuses on Parallel computing, Software, Operating system, Multiprocessing and Transactional memory. His Parallel computing research is multidisciplinary, incorporating perspectives in Domain-specific language, Compiler, Speculative multithreading and Programmer. His Operating system study integrates concerns from other disciplines, such as Instruction set and Database.
His study in Multiprocessing is interdisciplinary in nature, drawing from both Virtual machine, Microarchitecture, Java, Parallelism and Synchronization. His study looks at the relationship between Transactional memory and fields such as Atomicity, as well as how they intersect with chemical problems. He interconnects Multi-core processor and Cache in the investigation of issues within Thread.
His primary areas of investigation include Parallel computing, Computer architecture, Software, Compiler and Transactional memory. His study looks at the intersection of Parallel computing and topics like Programming paradigm with Domain-specific language. His Computer architecture study deals with Multi-core processor intersecting with Cache and Thread.
As a part of the same scientific study, he usually deals with the Cache, concentrating on Multiprocessing and frequently concerns with Speculative multithreading and Parallelism. His work carried out in the field of Transactional memory brings together such families of science as Distributed computing, Atomicity, Transaction processing and Operating system. His research integrates issues of Machine learning and Artificial intelligence in his study of Speedup.
Kunle Olukotun mainly focuses on Field-programmable gate array, Artificial intelligence, Speedup, Machine learning and Software. The concepts of his Field-programmable gate array study are interwoven with issues in Symmetric multiprocessor system, Distributed computing, Data modeling, Computer architecture and Graph. His work on Deep learning as part of general Artificial intelligence study is frequently linked to Noise, bridging the gap between disciplines.
His Speedup study deals with the bigger picture of Parallel computing. His work in Parallel computing addresses issues such as Stratix, which are connected to fields such as Abstraction layer. His Software research includes themes of Systems design, Pareto principle, Design space exploration and Computer engineering.
The scientist’s investigation covers issues in Artificial intelligence, Software, Machine learning, Deep learning and Computation. In general Artificial intelligence study, his work on Range often relates to the realm of Frontier, thereby connecting several areas of interest. Kunle Olukotun brings together Software and Software framework to produce work in his papers.
The study incorporates disciplines such as Semantics and Benchmark in addition to Machine learning. He works mostly in the field of Python, limiting it down to topics relating to Scala and, in certain cases, File format, Computer architecture, Compiler, High-level synthesis and Programmer, as a part of the same area of interest. He has researched Computer architecture in several fields, including Field-programmable gate array and Speedup.
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.
Map-Reduce for Machine Learning on Multicore
Cheng-tao Chu;Sang K. Kim;Yi-an Lin;Yuanyuan Yu.
neural information processing systems (2006)
Niagara: a 32-way multithreaded Sparc processor
P. Kongetira;K. Aingaran;K. Olukotun.
IEEE Micro (2005)
STAMP: Stanford Transactional Applications for Multi-Processing
Chi Cao Minh;JaeWoong Chung;C. Kozyrakis;K. Olukotun.
ieee international symposium on workload characterization (2008)
The case for a single-chip multiprocessor
Kunle Olukotun;Basem A. Nayfeh;Lance Hammond;Ken Wilson.
architectural support for programming languages and operating systems (1996)
Transactional Memory Coherence and Consistency
Lance Hammond;Vicky Wong;Mike Chen;Brian D. Carlstrom.
international symposium on computer architecture (2004)
A single-chip multiprocessor
B.A. Nayfeh;K. Olukotun.
IEEE Computer (1997)
Data speculation support for a chip multiprocessor
Lance Hammond;Mark Willey;Kunle Olukotun.
architectural support for programming languages and operating systems (1998)
An effective hybrid transactional memory system with strong isolation guarantees
Chi Cao Minh;Martin Trautmann;JaeWoong Chung;Austen McDonald.
international symposium on computer architecture (2007)
Accelerating CUDA graph algorithms at maximum warp
Sungpack Hong;Sang Kyun Kim;Tayo Oguntebi;Kunle Olukotun.
acm sigplan symposium on principles and practice of parallel programming (2011)
The Future of Microprocessors: Chip multiprocessors’ promise of huge performance gains is now a reality.
Kunle Olukotun;Lance Hammond.
ACM Queue (2005)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: