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John Owens's calculated h-index is 48. This page was automatically generated on 2019-01-06.

2144Owens:2007:ASOA Survey of General-Purpose Computation on Graphics Hardware
2092Owens:2008:GCGPU Computing
1001Rixner:2000:MASMemory Access Scheduling
777Harris:2007:PPSParallel Prefix Sum (Scan) with CUDA
686Sengupta:2007:SPFScan Primitives for GPU Computing
525Owens:2007:RCFResearch Challenges for On-Chip Interconnection Networks
448Khailany:2001:IMPImagine: Media Processing with Streams
415Kapasi:2003:PSPProgrammable Stream Processors
365Rixner:2000:ROFRegister Organization for Media Processing
335Rixner:1998:ABAA Bandwidth-Efficient Architecture for Media Processing
313Kapasi:2002:TISThe Imagine Stream Processor
267Zhang:2011:AQPA Quantitative Performance Analysis Model for GPU Architectures
223Zhang:2010:FTSFast Tridiagonal Solvers on the GPU
213Stuart:2011:MMOMulti-GPU MapReduce on GPU Clusters
205Lefohn:2006:GGEGlift: Generic, Efficient, Random-Access GPU Data Structures
174Alcantara:2009:RPHReal-Time Parallel Hashing on the GPU
158Wang:2016:GAHGunrock: A High-Performance Graph Processing Library on the GPU
151Owens:2005:SAAStreaming Architectures and Technology Trends
145Silberstein:2008:ECOEfficient Computation of Sum-products on GPUs Through Software-Managed Cache
137Muyan-Ozcelik:2008:FDRFast Deformable Registration on the GPU: A CUDA Implementation of Demons
132Kapasi:2000:ECOEfficient Conditional Operations for Data-parallel Architectures
128Gupta:2012:ASOA Study of Persistent Threads Style GPU Programming for GPGPU Workloads
126Tzeng:2010:TMFTask Management for Irregular-Parallel Workloads on the GPU
125Samant:2008:HPCHigh performance computing for deformable image registration: Towards a new paradigm in adaptive radiotherapy
117Owens:2002:MPAMedia Processing Applications on the Imagine Stream Processor
113Davidson:2014:WPGWork-Efficient Parallel GPU Methods for Single Source Shortest Paths
105Kass:2006:IDOInteractive Depth of Field Using Simulated Diffusion on a GPU
93Sengupta:2006:AWSA Work-Efficient Step-Efficient Prefix Sum Algorithm
92Stuart:2009:MPOMessage Passing on Data-Parallel Architectures
92Ebeida:2011:EMPEfficient Maximal Poisson-Disk Sampling
92Owens:2000:PROPolygon Rendering on a Stream Architecture
91Park:2006:DSIDiscrete Sibson Interpolation
83Ebeida:2012:ASAA Simple Algorithm for Maximal Poisson-Disk Sampling in High Dimensions
82Phillips:2009:RAPRapid Aerodynamic Performance Prediction on a Cluster of Graphics Processing Units
79Lefohn:2007:RSMResolution-Matched Shadow Maps
73Davidson:2011:AAMAn Auto-tuned Method for Solving Large Tridiagonal Systems on the GPU
71Patel:2012:PLDParallel Lossless Data Compression on the GPU
69Khailany:2003:ETVExploring the VLSI Scalability of Stream Processors
65Patney:2008:RRAReal-Time Reyes-Style Adaptive Surface Subdivision
63Mattson:2000:CSCommunication Scheduling
63Kapasi:2001:SSStream Scheduling
60Stuart:2010:MVRMulti-GPU Volume Rendering using MapReduce
57Davidson:2012:EPMEfficient Parallel Merge Sort for Fixed and Variable Length Keys
56Owens:2002:CGOComputer Graphics on a Stream Architecture
55Budge:2009:ODMOut-of-core Data Management for Path Tracing on Hybrid Resources
50Szumel:2005:TAMTowards a Mobile Agent Framework for Sensor Networks
49Lefohn:2005:IEPImplementing Efficient Parallel Data Structures on GPUs
48Moerschell:2008:DTMDistributed Texture Memory in a Multi-GPU Environment

47Patney:2009:PVTParallel View-Dependent Tessellation of Catmull-Clark Subdivision Surfaces
46Davidson:2012:TTFToward Techniques for Auto-tuning GPU Algorithms
46Davidson:2010:TTFToward Techniques for Auto-Tuning GPU Algorithms
43Stuart:2011:ESPEfficient Synchronization Primitives for GPUs
42Kepner:2016:MFOMathematical Foundations of the GraphBLAS
39Owens:2002:CRAComparing Reyes and OpenGL on a Stream Architecture
39Davidson:2011:RPFRegister Packing for Cyclic Reduction: A Case Study
38Ebeida:2011:EAGEfficient and Good Delaunay Meshes From Random Points
38Sengupta:2011:EPSEfficient Parallel Scan Algorithms for many-core GPUs
35Lefohn:2005:DASDynamic Adaptive Shadow Maps on Graphics Hardware
35Jenkins:2011:LLFLessons Learned from Exploring the Backtracking Paradigm on the GPU
33Alcantara:2011:BAEBuilding an Efficient Hash Table on the GPU
32Riffel:2004:MFMMio: Fast Multipass Partitioning via Priority-Based Instruction Scheduling
30Owens:2005:AOGAssessment of Graphic Processing Units (GPUs) for Department of Defense (DoD) Digital Signal Processing (DSP) Applications
28Tzeng:2012:AGTA GPU Task-Parallel Model with Dependency Resolution
26Ebeida:2011:ICRIsotropic conforming refinement of quadrilateral and hexahedral meshes using two-refinement templates
23Glavtchev:2011:FSLFeature-Based Speed Limit Sign Detection Using a Graphics Processing Unit
21Stuart:2011:EMTExtending MPI to Accelerators
21Kniss:2005:OTOOctree Textures on Graphics Hardware
21Park:2005:AFFA Framework for Real-Time Volume Visualization of Streaming Scattered Data
20Stuart:2010:GCGPU-to-CPU Callbacks
20Stone:2011:GPAGPGPU parallel algorithms for structured-grid CFD codes
20Wu:2015:PCOPerformance Characterization of High-Level Programming Models for GPU Graph Analytics
20Wang:2016:ACSA Comparative Study on Exact Triangle Counting Algorithms on the GPU
20Phillips:2010:UTSUnsteady Turbulent Simulations on a Cluster of Graphics Processors
19Gupta:2009:TOFThree-Layer Optimizations for Fast GMM Computations on GPU-like Parallel Processors
18Li:2012:KOTkANN on the GPU with Shifted Sorting
18Muyan-Ozcelik:2010:ATAA Template-Based Approach for Real-Time Speed-Limit-Sign Recognition on an Embedded System using GPU Computing
18Pan:2017:MGAMulti-GPU Graph Analytics
17Patney:2010:FCAFragment-Parallel Composite and Filter
16Serebrin:2002:ASPA Stream Processor Development Platform
16Szumel:2006:TVPThe Virtual Pheromone Communication Primitive
15Ma:2007:UVRUltra-Scale Visualization: Research and Education
15Yang:2015:FSMFast Sparse Matrix and Sparse Vector Multiplication Algorithm on the GPU
15Tzeng:2012:FCHFinding Convex Hulls Using Quickhull on the GPU
15Gosink:2009:DPBData Parallel Bin-Based Indexing for Answering Queries on Multi-Core Architectures
15Tzeng:2012:HPDHigh-Quality Parallel Depth-of-Field Using Line Samples
14Gupta:2011:CAMCompute \& Memory Optimizations for High-Quality Speech Recognition on Low-End GPU Processors
13Zhang:2011:APEA Parallel Error Diffusion Implementation on a GPU
13Ashkiani:2016:GMGPU Multisplit
12Wang:2017:GGGGunrock: GPU Graph Analytics
11Khailany:2000:ISAImagine: Signal and Image Processing Using Streams
11Ebeida:2013:SDSifted Disks
11Zhang:2011:AHMA Hybrid Method for Solving Tridiagonal Systems on the GPU
9Muyan-Ozcelik:2011:RSRReal-Time Speed-Limit-Sign Recognition on an Embedded System Using a GPU
9Patney:2015:PAFPiko: A Framework for Authoring Programmable Graphics Pipelines
7Zhang:2012:PDEPlane-dependent Error Diffusion on a GPU
6Owens:2004:GTFGPUs tapped for general computing
6Muyan-Ozcelik:2016:MREMultitasking Real-time Embedded GPU Computing Tasks
6Ebeida:2016:DDTDisk Density Tuning of a Maximal Random Packing
5Ashkiani:2018:GLAGPU LSM: A Dynamic Dictionary Data Structure for the GPU
5Geil:2014:WGCWTF, GPU! Computing Twitter's Who-To-Follow on the GPU
5Ashkiani:2018:ADHA Dynamic Hash Table for the GPU
4Mak:2014:GAEGPU-Accelerated and Efficient Multi-View Triangulation for Scene Reconstruction
4Abdelkader:2017:ACRA Constrained Resampling Strategy for Mesh Improvement
4Owens:2007:TMSTowards Multi-GPU Support for Visualization
3Wang:2016:FPSFast Parallel Skew and Prefix-Doubling Suffix Array Construction on the GPU
3Liu:2018:OLAObject Localization and Motion Transfer learning with Capsules
3Ashkiani:2016:PATParallel Approaches to the String Matching Problem on the GPU
3Yang:2018:DPFDesign Principles for Sparse Matrix Multiplication on the GPU
3Weber:2015:PRAParallel Reyes-style Adaptive Subdivision with Bounded Memory Usage
3Phillips:2011:AO2Acceleration of 2-D Compressible Flow Solvers with Graphics Processing Unit Clusters
2Ashkiani:2017:GMGPU Multisplit: an extended study of a parallel algorithm
2Owens:2004:OTSOn The Scalability of Sensor Network Routing and Compression Algorithms
2Seitz:2013:AGIA GPU Implementation for Two-Dimensional Shallow Water Modeling
1Abdelkader:2018:VITVoroCrust Illustrated: Theory and Challenges (Multimedia Exposition)
1Szumel:2003:OTFOn the Feasibility of the UC Davis Metanet
1Muyan-Ozcelik:2017:MFMMethods for Multitasking among Real-time Embedded Compute Tasks Running on the GPU
1Wang:2017:MALMini-Gunrock: A Lightweight Graph Analytics Framework on the GPU
1Yang:2018:IPEImplementing Push-Pull Efficiently in GraphBLAS
1Gosink:2008:BIABin-Hash Indexing: A Parallel Method For Fast Query Processing
1Geil:2018:QFAQuotient Filters: Approximate Membership Queries on the GPU
1Abdelkader:2018:SCFSampling Conditions for Conforming Voronoi Meshing by the VoroCrust Algorithm
1Silberstein:2011:ASCApplying Software-Managed Caching and CPU/GPU Task Scheduling for Accelerating Dynamic Workloads