2019 - Member of the National Academy of Engineering For contributions to the design of advanced compiler and analysis systems for high-performance computers.
2007 - ACM Fellow For contributions to compilers and program analysis.
Monica S. Lam mainly focuses on Compiler, Programming language, Parallel computing, Operating system and Software. The study incorporates disciplines such as Algorithm, Computer architecture, Computer hardware and Loop scheduling in addition to Compiler. Her Parallel computing research incorporates elements of Synchronization and Address space.
Monica S. Lam focuses mostly in the field of Operating system, narrowing it down to topics relating to Embedded system and, in certain cases, Server, Hash function, Unix operating system and Collaborative software. Her Software research incorporates themes from Guard, Buffer overflow, Heap overflow, Bounds checking and Testbed. Monica S. Lam has included themes like Multiprocessor performance, Automatic parallelization, Face and Benchmark in her Compiler construction study.
Monica S. Lam mostly deals with Parallel computing, Programming language, Compiler, Operating system and World Wide Web. Parallel computing is closely attributed to Systolic array in her work. In her study, which falls under the umbrella issue of Programming language, Algorithm is strongly linked to Theoretical computer science.
Monica S. Lam interconnects Software and Benchmark in the investigation of issues within Compiler. Monica S. Lam has researched Benchmark in several fields, including Multiprocessor performance and Face. Her study looks at the relationship between Operating system and fields such as Embedded system, as well as how they intersect with chemical problems.
Monica S. Lam spends much of her time researching Natural language, Artificial intelligence, Parsing, Set and Database schema. Her research in Natural language intersects with topics in Remote assistance, Human–computer interaction, Access control, World Wide Web and Task. Her biological study spans a wide range of topics, including Domain, Machine learning and Natural language processing.
Her study on Parsing is covered under Programming language. Her research is interdisciplinary, bridging the disciplines of Code and Programming language. Her study looks at the relationship between Set and topics such as Question answering, which overlap with Machine translation.
Her primary areas of investigation include Natural language, World Wide Web, Artificial intelligence, Training set and State. Her work deals with themes such as Parsing, Access control, Interface, Usability and Interoperability, which intersect with Natural language. To a larger extent, Monica S. Lam studies Programming language with the aim of understanding Parsing.
In the subject of general World Wide Web, her work in Crowdsourcing is often linked to Phone, Medical literature and Natural language programming, thereby combining diverse domains of study. In general Artificial intelligence, her work in Ontology, Tracking and Transfer of learning is often linked to Zero linking many areas of study. Her studies in Training set integrate themes in fields like Paraphrase, Machine learning and Shot.
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A data locality optimizing algorithm
Michael E. Wolf;Monica S. Lam.
programming language design and implementation (1991)
Software pipelining: an effective scheduling technique for VLIW machines
Monica S. Lam.
programming language design and implementation (1988)
The Stanford Dash multiprocessor
D. Lenoski;J. Laudon;K. Gharachorloo;W.-D. Weber.
IEEE Computer (1992)
Design and evaluation of a compiler algorithm for prefetching
Todd C. Mowry;Monica S. Lam;Anoop Gupta.
architectural support for programming languages and operating systems (1992)
Tracking down software bugs using automatic anomaly detection
Sudheendra Hangal;Monica S. Lam.
international conference on software engineering (2002)
A loop transformation theory and an algorithm to maximize parallelism
M.E. Wolf;M.S. Lam.
IEEE Transactions on Parallel and Distributed Systems (1991)
Automated processor generation system for designing a configurable processor and method for the same
Earl A. Killian;Ricardo E. Gonzalez;Ashish B. Dixit;Monica Lam.
Efficient, context-sensitive pointer analysis for C programs
Robert P. Wilson;Monica S. Lam.
Maximizing multiprocessor performance with the SUIF compiler
Mary W. Hall;Jennifer-Ann M. Anderson;Saman P. Amarasinghe;Brian R. Murphy.
IEEE Computer (1996)
SUIF: an infrastructure for research on parallelizing and optimizing compilers
Robert P. Wilson;Robert S. French;Christopher S. Wilson;Saman P. Amarasinghe.
Sigplan Notices (1994)
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