T
Tom Forsyth
Researcher at Intel
Publications - 4
Citations - 1172
Tom Forsyth is an academic researcher from Intel. The author has contributed to research in topics: Software rendering & SIMD. The author has an hindex of 4, co-authored 4 publications receiving 1163 citations.
Papers
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Journal ArticleDOI
Larrabee: a many-core x86 architecture for visual computing
Larry D. Seiler,Doug Carmean,Eric Sprangle,Tom Forsyth,Michael Abrash,Pradeep Dubey,Stephen Junkins,Adam T. Lake,Jeremy Sugerman,Robert Dale Cavin,Roger Espasa,Ed Grochowski,Toni Juan,Pat Hanrahan +13 more
TL;DR: This article consists of a collection of slides from the author's conference presentation, some of the topics discussed include: architecture convergence; Larrabee architecture; and graphics pipeline.
Journal ArticleDOI
Larrabee: A Many-Core x86 Architecture for Visual Computing
Larry D. Seiler,Douglas M. Carmean,Eric Sprangle,Tom Forsyth,Pradeep Dubey,Stephen Junkins,Adam T. Lake,Robert Dale Cavin,Roger Espasa,Edward T. Grochowski,Toni Juan,Michael Abrash,Jeremy Sugerman,Pat Hanrahan +13 more
TL;DR: The Larrabee many-core visual computing architecture uses multiple in-order x86 cores augmented by wide vector processor units, together with some fixed-function logic, which increases the architecture's programmability as compared to standard GPUs.
Proceedings ArticleDOI
Beyond programmable shading: fundamentals
Aaron Lefohn,Mike Houston,Chas Boyd,Kayvon Fatahalian,Tom Forsyth,David Luebke,John D. Owens +6 more
TL;DR: This course gives an introduction to several parallel graphics architectures, programming environments, and a introduction to the new types of graphics algorithms that will be possible.
Patent
Device, system, and method for improving processing efficiency by collectively applying operations
TL;DR: In this paper, a system and method for generating a single compressed vector including two or more predetermined attribute values is described, where if a first and a second attribute value of the data point are equal to a first or a second, respectively, of the two predetermined attribute value, the compressed vector is used to operate on the data points.