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
More filters
Proceedings ArticleDOI
Designing efficient sorting algorithms for manycore GPUs
TL;DR: The design of high-performance parallel radix sort and merge sort routines for manycore GPUs, taking advantage of the full programmability offered by CUDA, are described, which are the fastest GPU sort and the fastest comparison-based sort reported in the literature.
Journal ArticleDOI
GPUs and the Future of Parallel Computing
TL;DR: The capabilities of state-of-the art GPU-based high-throughput computing systems are discussed and the challenges to scaling single-chip parallel-computing systems are considered, highlighting high-impact areas that the computing research community can address.
Survey of Polygonal Surface Simplification Algorithms
Paul S. Heckbert,Michael Garland +1 more
TL;DR: Methods for simplifying and approximating polygonal surfaces from computer graphics, computer vision, cartography, computational geometry, and other fields are classified, summarized, and compared both practically and theoretically.
Proceedings ArticleDOI
Scalable GPU graph traversal
TL;DR: This work presents a BFS parallelization focused on fine-grained task management constructed from efficient prefix sum that achieves an asymptotically optimal O(|V|+|E|) work complexity.
Journal ArticleDOI
Parallel Computing Experiences with CUDA
Michael Garland,S. Le Grand,John R. Nickolls,Joshua A. Anderson,J. Hardwick,S. Morton,E. Phillips,Yao Zhang,Vasily Volkov +8 more
TL;DR: Experiences gained in applying CUDA to a diverse set of problems are surveyed and the parallel speedups over sequential codes running on traditional CPU architectures attained by executing key computations on the GPU are surveyed.