B
Brian T. Lewis
Researcher at Intel
Publications - 55
Citations - 3179
Brian T. Lewis is an academic researcher from Intel. The author has contributed to research in topics: Object (computer science) & Garbage collection. The author has an hindex of 22, co-authored 55 publications receiving 3156 citations. Previous affiliations of Brian T. Lewis include Sun Microsystems Laboratories & Sun Microsystems.
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Patent
Representation of collaborative multi-user activities relative to shared structured data objects in a networked workstation environment
Sara Bly,Brady A. Farrand,Jeff Hodges,Michael D. Kupfer,Brian T. Lewis,William J. Maybury,Michael L. Tallan,Stephen B. Tom +7 more
TL;DR: In this article, the authors present a multi-user collaborative system in which the contents as well as the current status of other user activity of a shared structured data object representing one or more related structured data objects in the form of data entries can be concurrently accessed by different users respectively at different workstations connected to a common link.
Patent
Updating local copy of shared data in a collaborative system
TL;DR: In this paper, the authors present a multi-user collaborative system in which the contents as well as the current status of other user activity of a shared structured data object representing one or more related structured data objects in the form of data entries can be concurrently accessed by different users respectively at different workstations connected to a common link.
Journal ArticleDOI
Compiler and runtime support for efficient software transactional memory
Ali-Reza Adl-Tabatabai,Brian T. Lewis,Vijay Menon,Brian R. Murphy,Bratin Saha,Tatiana Shpeisman +5 more
TL;DR: A high-performance software transactional memory system (STM) integrated into a managed runtime environment is presented and the JIT compiler is the first to optimize the overheads of STM, and novel techniques for enabling JIT optimizations on STM operations are shown.
Proceedings ArticleDOI
Adaptive heterogeneous scheduling for integrated GPUs
TL;DR: The asymmetric scheduling algorithm uses low-overhead online profiling to automatically partition the work of dataparallel kernels between the CPU and GPU without input from application developers, underscoring the feasibility of online profile-based heterogeneous scheduling on integrated CPU-GPU processors.
Patent
Method and apparatus for generating platform-standard object files containing machine-independent code
TL;DR: A method and apparatus for generating a platform-standard object file containing machine-independent abstract code is described in this article. The abstract code can be decomposed before it is stored in the abstract code platform standard object file and can be dynamically linked to the execution routine.