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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Proceedings ArticleDOI
Surface simplification using quadric error metrics
Michael Garland,Paul S. Heckbert +1 more
TL;DR: This work has developed a surface simplification algorithm which can rapidly produce high quality approximations of polygonal models, and which also supports non-manifold surface models.
Proceedings ArticleDOI
Scalable parallel programming with CUDA
TL;DR: Presents a collection of slides covering the following topics: CUDA parallel programming model; CUDA toolkit and libraries; performance optimization; and application development.
Journal ArticleDOI
Scalable Parallel Programming with CUDA: Is CUDA the parallel programming model that application developers have been waiting for?
TL;DR: In this article, the authors present a framework 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 on manycore GPUs with widely varying numbers of cores.
Proceedings ArticleDOI
Implementing sparse matrix-vector multiplication on throughput-oriented processors
Nathan Bell,Michael Garland +1 more
TL;DR: This work explores SpMV methods that are well-suited to throughput-oriented architectures like the GPU and which exploit several common sparsity classes, including structured grid and unstructured mesh matrices.
Ecient Sparse Matrix-Vector Multiplication on CUDA
Nathan Bell,Michael Garland +1 more
TL;DR: Data structures and algorithms for SpMV that are eciently implemented on the CUDA platform for the ne-grained parallel architecture of the GPU and develop methods to exploit several common forms of matrix structure while oering alternatives which accommodate greater irregularity are developed.