M
Michael Garland
Researcher at Nvidia
Publications - 131
Citations - 18564
Michael Garland is an academic researcher from Nvidia. The author has contributed to research in topics: CUDA & Polygon mesh. The author has an hindex of 50, co-authored 120 publications receiving 17536 citations. Previous affiliations of Michael Garland include Carnegie Mellon University & University of Virginia.
Papers
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Journal ArticleDOI
High-Performance and Scalable GPU Graph Traversal
TL;DR: This work presents a BFS parallelization focused on fine-grained task management constructed from efficient prefix sum computations that achieves an asymptotically optimal O(|V| + |E|) gd work complexity.
Proceedings ArticleDOI
User-guided simplification
Youngihn Kho,Michael Garland +1 more
TL;DR: This paper presents a user-guided approach for mesh simplification that allows users to selectively control the relative importance of different surface regions and preserve various features through the imposition of geometric constraints.
Proceedings ArticleDOI
Spacetime meshing with adaptive refinement and coarsening
Reza Abedi,Shuo-Heng Chung,Jeff Erickson,Yong Fan,Michael Garland,Damrong Guoy,Robert B. Haber,John M. Sullivan,Shripad Thite,Yuan Zhou +9 more
TL;DR: This work proposes a new algorithm for constructing finite-element meshes suitable for spacetime discontinuous Galerkin solutions of linear hyperbolic PDEs and employs new mechanisms for adaptively coarsening and refining the front in response to a posteriori error estimates returned by the numerical code.
Proceedings ArticleDOI
Red Fox: An Execution Environment for Relational Query Processing on GPUs
Haicheng Wu,Gregory Diamos,Tim Sheard,Molham Aref,Sean Baxter,Michael Garland,Sudhakar Yalamanchili +6 more
TL;DR: This paper introduces the design, implementation, and evaluation of Red Fox, a compiler and runtime infrastructure for executing relational queries on GPUs and is the first reported end-to-end compilation and execution infrastructure that supports the full set of TPC-H queries on commodity GPUs.
Proceedings ArticleDOI
Scalable parallel programming
TL;DR: The challenge is to develop mainstream application software that transparently scales its parallelism to leverage the increasing number of processor cores, much as 3D graphics applications transparently scale their Parallelism to manycore GPUs with widely varying numbers of cores.