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John Owens's calculated h-index is 49. This page was automatically generated on 2019-04-01.

2849Owens:2007:ASOA Survey of General-Purpose Computation on Graphics Hardware
2119Owens:2008:GCGPU Computing
1016Rixner:2000:MASMemory Access Scheduling
775Harris:2007:PPSParallel Prefix Sum (Scan) with CUDA
692Sengupta:2007:SPFScan Primitives for GPU Computing
527Owens:2007:RCFResearch Challenges for On-Chip Interconnection Networks
444Khailany:2001:IMPImagine: Media Processing with Streams
410Kapasi:2003:PSPProgrammable Stream Processors
361Rixner:2000:ROFRegister Organization for Media Processing
334Rixner:1998:ABAA Bandwidth-Efficient Architecture for Media Processing
309Kapasi:2002:TISThe Imagine Stream Processor
276Zhang:2011:AQPA Quantitative Performance Analysis Model for GPU Architectures
229Zhang:2010:FTSFast Tridiagonal Solvers on the GPU
226Stuart:2011:MMOMulti-GPU MapReduce on GPU Clusters
208Lefohn:2006:GGEGlift: Generic, Efficient, Random-Access GPU Data Structures
179Wang:2016:GAHGunrock: A High-Performance Graph Processing Library on the GPU
178Alcantara:2009:RPHReal-Time Parallel Hashing on the GPU
152Owens:2005:SAAStreaming Architectures and Technology Trends
144Silberstein:2008:ECOEfficient Computation of Sum-products on GPUs Through Software-Managed Cache
138Gupta:2012:ASOA Study of Persistent Threads Style GPU Programming for GPGPU Workloads
138Muyan-Ozcelik:2008:FDRFast Deformable Registration on the GPU: A CUDA Implementation of Demons
134Kapasi:2000:ECOEfficient Conditional Operations for Data-parallel Architectures
129Samant:2008:HPCHigh performance computing for deformable image registration: Towards a new paradigm in adaptive radiotherapy
128Tzeng:2010:TMFTask Management for Irregular-Parallel Workloads on the GPU
122Davidson:2014:WPGWork-Efficient Parallel GPU Methods for Single Source Shortest Paths
118Owens:2002:MPAMedia Processing Applications on the Imagine Stream Processor
98Kass:2006:IDOInteractive Depth of Field Using Simulated Diffusion on a GPU
94Park:2006:DSIDiscrete Sibson Interpolation
93Sengupta:2006:AWSA Work-Efficient Step-Efficient Prefix Sum Algorithm
92Stuart:2009:MPOMessage Passing on Data-Parallel Architectures
92Owens:2000:PROPolygon Rendering on a Stream Architecture
90Ebeida:2011:EMPEfficient Maximal Poisson-Disk Sampling
84Ebeida:2012:ASAA Simple Algorithm for Maximal Poisson-Disk Sampling in High Dimensions
84Phillips:2009:RAPRapid Aerodynamic Performance Prediction on a Cluster of Graphics Processing Units
83Lefohn:2007:RSMResolution-Matched Shadow Maps
74Patel:2012:PLDParallel Lossless Data Compression on the GPU
73Davidson:2011:AAMAn Auto-tuned Method for Solving Large Tridiagonal Systems on the GPU
70Khailany:2003:ETVExploring the VLSI Scalability of Stream Processors
69Patney:2008:RRAReal-Time Reyes-Style Adaptive Surface Subdivision
63Mattson:2000:CSCommunication Scheduling
62Kapasi:2001:SSStream Scheduling
61Stuart:2010:MVRMulti-GPU Volume Rendering using MapReduce
57Davidson:2012:EPMEfficient Parallel Merge Sort for Fixed and Variable Length Keys
56Budge:2009:ODMOut-of-core Data Management for Path Tracing on Hybrid Resources
55Owens:2002:CGOComputer Graphics on a Stream Architecture
52Kepner:2016:MFOMathematical Foundations of the GraphBLAS
52Patney:2009:PVTParallel View-Dependent Tessellation of Catmull-Clark Subdivision Surfaces
49Moerschell:2008:DTMDistributed Texture Memory in a Multi-GPU Environment
49Szumel:2005:TAMTowards a Mobile Agent Framework for Sensor Networks

49Lefohn:2005:IEPImplementing Efficient Parallel Data Structures on GPUs
47Davidson:2012:TTFToward Techniques for Auto-tuning GPU Algorithms
47Davidson:2010:TTFToward Techniques for Auto-Tuning GPU Algorithms
46Stuart:2011:ESPEfficient Synchronization Primitives for GPUs
42Owens:2002:CRAComparing Reyes and OpenGL on a Stream Architecture
39Davidson:2011:RPFRegister Packing for Cyclic Reduction: A Case Study
39Sengupta:2011:EPSEfficient Parallel Scan Algorithms for many-core GPUs
37Ebeida:2011:EAGEfficient and Good Delaunay Meshes From Random Points
36Alcantara:2011:BAEBuilding an Efficient Hash Table on the GPU
35Jenkins:2011:LLFLessons Learned from Exploring the Backtracking Paradigm on the GPU
34Lefohn:2005:DASDynamic Adaptive Shadow Maps on Graphics Hardware
30Riffel:2004:MFMMio: Fast Multipass Partitioning via Priority-Based Instruction Scheduling
29Tzeng:2012:AGTA GPU Task-Parallel Model with Dependency Resolution
28Ebeida:2011:ICRIsotropic conforming refinement of quadrilateral and hexahedral meshes using two-refinement templates
27Owens:2005:AOGAssessment of Graphic Processing Units (GPUs) for Department of Defense (DoD) Digital Signal Processing (DSP) Applications
23Glavtchev:2011:FSLFeature-Based Speed Limit Sign Detection Using a Graphics Processing Unit
23Pan:2017:MGAMulti-GPU Graph Analytics
21Stuart:2011:EMTExtending MPI to Accelerators
21Kniss:2005:OTOOctree Textures on Graphics Hardware
21Wu:2015:PCOPerformance Characterization of High-Level Programming Models for GPU Graph Analytics
21Wang:2016:ACSA Comparative Study on Exact Triangle Counting Algorithms on the GPU
20Stuart:2010:GCGPU-to-CPU Callbacks
20Stone:2011:GPAGPGPU parallel algorithms for structured-grid CFD codes
20Phillips:2010:UTSUnsteady Turbulent Simulations on a Cluster of Graphics Processors
20Park:2005:AFFA Framework for Real-Time Volume Visualization of Streaming Scattered Data
19Li:2012:KOTkANN on the GPU with Shifted Sorting
19Gupta:2009:TOFThree-Layer Optimizations for Fast GMM Computations on GPU-like Parallel Processors
18Patney:2010:FCAFragment-Parallel Composite and Filter
18Szumel:2006:TVPThe Virtual Pheromone Communication Primitive
17Serebrin:2002:ASPA Stream Processor Development Platform
17Muyan-Ozcelik:2010:ATAA Template-Based Approach for Real-Time Speed-Limit-Sign Recognition on an Embedded System using GPU Computing
16Yang:2015:FSMFast Sparse Matrix and Sparse Vector Multiplication Algorithm on the GPU
16Tzeng:2012:FCHFinding Convex Hulls Using Quickhull on the GPU
15Ma:2007:UVRUltra-Scale Visualization: Research and Education
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
15Wang:2017:GGGGunrock: GPU Graph Analytics
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
13Patney:2015:PAFPiko: A Framework for Authoring Programmable Graphics Pipelines
13Ashkiani:2016:GMGPU Multisplit
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
10Muyan-Ozcelik:2011:RSRReal-Time Speed-Limit-Sign Recognition on an Embedded System Using a GPU
8Ashkiani:2018:ADHA Dynamic Hash Table for the GPU
7Zhang:2012:PDEPlane-dependent Error Diffusion on a GPU
7Muyan-Ozcelik:2016:MREMultitasking Real-time Embedded GPU Computing Tasks
7Ebeida:2016:DDTDisk Density Tuning of a Maximal Random Packing
6Owens:2004:GTFGPUs tapped for general computing
6Ashkiani:2018:GLAGPU LSM: A Dynamic Dictionary Data Structure for the GPU
5Ashkiani:2016:PATParallel Approaches to the String Matching Problem on the GPU
5Geil:2014:WGCWTF, GPU! Computing Twitter's Who-To-Follow on the GPU
5Yang:2018:DPFDesign Principles for Sparse Matrix Multiplication on the GPU
5Abdelkader:2017:ACRA Constrained Resampling Strategy for Mesh Improvement
4Wang:2016:FPSFast Parallel Skew and Prefix-Doubling Suffix Array Construction on the GPU
4Mak:2014:GAEGPU-Accelerated and Efficient Multi-View Triangulation for Scene Reconstruction
4Owens:2007:TMSTowards Multi-GPU Support for Visualization
3Liu:2018:OLAObject Localization and Motion Transfer learning with Capsules
3Abdelkader:2019:VVMVoroCrust: Voronoi Meshing Without Clipping
3Weber:2015:PRAParallel Reyes-style Adaptive Subdivision with Bounded Memory Usage
3Abdelkader:2018:SCFSampling Conditions for Conforming Voronoi Meshing by the VoroCrust Algorithm
3Phillips:2011:AO2Acceleration of 2-D Compressible Flow Solvers with Graphics Processing Unit Clusters
2Pan:2018:SBSScalable Breadth-First Search on a GPU Cluster
2Abdelkader:2018:VITVoroCrust Illustrated: Theory and Challenges (Multimedia Exposition)
2Ashkiani:2017:GMGPU Multisplit: an extended study of a parallel algorithm
2Owens:2004:OTSOn The Scalability of Sensor Network Routing and Compression Algorithms
2Yang:2018:IPEImplementing Push-Pull Efficiently in GraphBLAS
2Geil:2018:QFAQuotient Filters: Approximate Membership Queries on the GPU
2Seitz:2013:AGIA GPU Implementation for Two-Dimensional Shallow Water Modeling
1Awad:2019:EAHEngineering a High-Performance GPU B-Tree
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
1Gosink:2008:BIABin-Hash Indexing: A Parallel Method For Fast Query Processing
1Silberstein:2011:ASCApplying Software-Managed Caching and CPU/GPU Task Scheduling for Accelerating Dynamic Workloads

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