S
Sam S. Stone
Researcher at University of Illinois at Urbana–Champaign
Publications - 15
Citations - 2329
Sam S. Stone is an academic researcher from University of Illinois at Urbana–Champaign. The author has contributed to research in topics: CUDA & General-purpose computing on graphics processing units. The author has an hindex of 12, co-authored 15 publications receiving 2289 citations.
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Proceedings ArticleDOI
Optimization principles and application performance evaluation of a multithreaded GPU using CUDA
Shane Ryoo,Christopher I. Rodrigues,Sara S. Baghsorkhi,Sam S. Stone,David B. Kirk,Wen-mei W. Hwu +5 more
TL;DR: This work discusses the GeForce 8800 GTX processor's organization, features, and generalized optimization strategies, and achieves increased performance by reordering accesses to off-chip memory to combine requests to the same or contiguous memory locations and apply classical optimizations to reduce the number of executed operations.
Proceedings ArticleDOI
Program optimization space pruning for a multithreaded gpu
Shane Ryoo,Christopher I. Rodrigues,Sam S. Stone,Sara S. Baghsorkhi,Sain-Zee Ueng,John A. Stratton,Wen-mei W. Hwu +6 more
TL;DR: The complexity involved in optimizing applications for one highly-parallel system and one relatively simple methodology for reducing the workload involved in the optimization process are shown.
Journal ArticleDOI
Accelerating advanced MRI reconstructions on GPUs
Sam S. Stone,Justin P. Haldar,Stephanie C. Tsao,Wen-mei W. Hwu,Bradley P. Sutton,Zhi-Pei Liang +5 more
TL;DR: The acceleration of an advanced magnetic resonance imaging reconstruction algorithm on NVIDIA's Quadro FX 5600 achieves up to 180 GFLOPS and requires just over one minute on the Quadro, while reconstruction on a quad-core CPU is twenty-one times slower.
Book ChapterDOI
MCUDA: An Efficient Implementation of CUDA Kernels for Multi-core CPUs
TL;DR: A framework called MCUDA is described, which allows CUDA programs to be executed efficiently on shared memory, multi-core CPUs and argues that CUDA can be an effective data-parallel programming model for more than just GPU architectures.
Journal ArticleDOI
Program optimization carving for GPU computing
Shane Ryoo,Christopher I. Rodrigues,Sam S. Stone,John A. Stratton,Sain-Zee Ueng,Sara S. Baghsorkhi,Wen-mei W. Hwu +6 more
TL;DR: This work proposes program optimization carving, a technique that begins with a complete optimization space and prunes it down to a set of configurations that are likely to contain the global maximum, and shows that this approach is significantly superior to random sampling of the search space.