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John Owens's calculated h-index is 50. This page was automatically generated on 2019-12-04.

2968Owens:2007:ASOA Survey of General-Purpose Computation on Graphics Hardware
2202Owens:2008:GCGPU Computing
1069Rixner:2000:MASMemory Access Scheduling
826Harris:2007:PPSParallel Prefix Sum (Scan) with CUDA
717Sengupta:2007:SPFScan Primitives for GPU Computing
542Owens:2007:RCFResearch Challenges for On-Chip Interconnection Networks
446Khailany:2001:IMPImagine: Media Processing with Streams
414Kapasi:2003:PSPProgrammable Stream Processors
360Rixner:2000:ROFRegister Organization for Media Processing
339Rixner:1998:ABAA Bandwidth-Efficient Architecture for Media Processing
310Kapasi:2002:TISThe Imagine Stream Processor
299Zhang:2011:AQPA Quantitative Performance Analysis Model for GPU Architectures
258Zhang:2010:FTSFast Tridiagonal Solvers on the GPU
238Wang:2016:GAHGunrock: A High-Performance Graph Processing Library on the GPU
233Stuart:2011:MMOMulti-GPU MapReduce on GPU Clusters
213Lefohn:2006:GGEGlift: Generic, Efficient, Random-Access GPU Data Structures
194Alcantara:2009:RPHReal-Time Parallel Hashing on the GPU
158Owens:2005:SAAStreaming Architectures and Technology Trends
157Gupta:2012:ASOA Study of Persistent Threads Style GPU Programming for GPGPU Workloads
147Silberstein:2008:ECOEfficient Computation of Sum-products on GPUs Through Software-Managed Cache
145Muyan-Ozcelik:2008:FDRFast Deformable Registration on the GPU: A CUDA Implementation of Demons
139Davidson:2014:WPGWork-Efficient Parallel GPU Methods for Single Source Shortest Paths
135Samant:2008:HPCHigh performance computing for deformable image registration: Towards a new paradigm in adaptive radiotherapy
135Kapasi:2000:ECOEfficient Conditional Operations for Data-parallel Architectures
134Tzeng:2010:TMFTask Management for Irregular-Parallel Workloads on the GPU
117Owens:2002:MPAMedia Processing Applications on the Imagine Stream Processor
106Park:2006:DSIDiscrete Sibson Interpolation
104Kass:2006:IDOInteractive Depth of Field Using Simulated Diffusion on a GPU
100Ebeida:2011:EMPEfficient Maximal Poisson-Disk Sampling
99Sengupta:2006:AWSA Work-Efficient Step-Efficient Prefix Sum Algorithm
94Stuart:2009:MPOMessage Passing on Data-Parallel Architectures
94Owens:2000:PROPolygon Rendering on a Stream Architecture
92Ebeida:2012:ASAA Simple Algorithm for Maximal Poisson-Disk Sampling in High Dimensions
87Phillips:2009:RAPRapid Aerodynamic Performance Prediction on a Cluster of Graphics Processing Units
87Lefohn:2007:RSMResolution-Matched Shadow Maps
83Patel:2012:PLDParallel Lossless Data Compression on the GPU
80Davidson:2011:AAMAn Auto-tuned Method for Solving Large Tridiagonal Systems on the GPU
77Kepner:2016:MFOMathematical Foundations of the GraphBLAS
70Khailany:2003:ETVExploring the VLSI Scalability of Stream Processors
68Patney:2008:RRAReal-Time Reyes-Style Adaptive Surface Subdivision
66Davidson:2012:EPMEfficient Parallel Merge Sort for Fixed and Variable Length Keys
64Stuart:2010:MVRMulti-GPU Volume Rendering using MapReduce
64Mattson:2000:CSCommunication Scheduling
63Kapasi:2001:SSStream Scheduling
59Budge:2009:ODMOut-of-core Data Management for Path Tracing on Hybrid Resources
58Owens:2002:CGOComputer Graphics on a Stream Architecture
52Patney:2009:PVTParallel View-Dependent Tessellation of Catmull-Clark Subdivision Surfaces
50Moerschell:2008:DTMDistributed Texture Memory in a Multi-GPU Environment
50Stuart:2011:ESPEfficient Synchronization Primitives for GPUs
50Lefohn:2005:IEPImplementing Efficient Parallel Data Structures on GPUs

49Szumel:2005:TAMTowards a Mobile Agent Framework for Sensor Networks
49Davidson:2012:TTFToward Techniques for Auto-tuning GPU Algorithms
49Davidson:2010:TTFToward Techniques for Auto-Tuning GPU Algorithms
44Davidson:2011:RPFRegister Packing for Cyclic Reduction: A Case Study
41Ebeida:2011:EAGEfficient and Good Delaunay Meshes From Random Points
41Sengupta:2011:EPSEfficient Parallel Scan Algorithms for many-core GPUs
40Alcantara:2011:BAEBuilding an Efficient Hash Table on the GPU
39Owens:2002:CRAComparing Reyes and OpenGL on a Stream Architecture
38Jenkins:2011:LLFLessons Learned from Exploring the Backtracking Paradigm on the GPU
35Wang:2016:ACSA Comparative Study on Exact Triangle Counting Algorithms on the GPU
34Lefohn:2005:DASDynamic Adaptive Shadow Maps on Graphics Hardware
33Wang:2017:GGGGunrock: GPU Graph Analytics
32Tzeng:2012:AGTA GPU Task-Parallel Model with Dependency Resolution
32Riffel:2004:MFMMio: Fast Multipass Partitioning via Priority-Based Instruction Scheduling
30Pan:2017:MGAMulti-GPU Graph Analytics
29Ebeida: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
25Glavtchev:2011:FSLFeature-Based Speed Limit Sign Detection Using a Graphics Processing Unit
25Li:2012:KOTkANN on the GPU with Shifted Sorting
24Stuart:2010:GCGPU-to-CPU Callbacks
24Wu:2015:PCOPerformance Characterization of High-Level Programming Models for GPU Graph Analytics
22Kniss:2005:OTOOctree Textures on Graphics Hardware
21Stone:2011:GPAGPGPU parallel algorithms for structured-grid CFD codes
20Patney:2010:FCAFragment-Parallel Composite and Filter
20Stuart:2011:EMTExtending MPI to Accelerators
20Yang:2015:FSMFast Sparse Matrix and Sparse Vector Multiplication Algorithm on the GPU
20Park:2005:AFFA Framework for Real-Time Volume Visualization of Streaming Scattered Data
19Phillips:2010:UTSUnsteady Turbulent Simulations on a Cluster of Graphics Processors
19Gupta:2009:TOFThree-Layer Optimizations for Fast GMM Computations on GPU-like Parallel Processors
18Szumel:2006:TVPThe Virtual Pheromone Communication Primitive
17Tzeng:2012:FCHFinding Convex Hulls Using Quickhull on the GPU
17Gosink:2009:DPBData Parallel Bin-Based Indexing for Answering Queries on Multi-Core Architectures
17Tzeng:2012:HPDHigh-Quality Parallel Depth-of-Field Using Line Samples
17Muyan-Ozcelik:2010:ATAA Template-Based Approach for Real-Time Speed-Limit-Sign Recognition on an Embedded System using GPU Computing
17Ashkiani:2016:GMGPU Multisplit
16Serebrin:2002:ASPA Stream Processor Development Platform
16Ashkiani:2018:ADHA Dynamic Hash Table for the GPU
15Ma:2007:UVRUltra-Scale Visualization: Research and Education
14Gupta:2011:CAMCompute \& Memory Optimizations for High-Quality Speech Recognition on Low-End GPU Processors
14Yang:2018:DPFDesign Principles for Sparse Matrix Multiplication on the GPU
14Patney:2015:PAFPiko: A Framework for Authoring Programmable Graphics Pipelines
13Muyan-Ozcelik:2011:RSRReal-Time Speed-Limit-Sign Recognition on an Embedded System Using a GPU
13Zhang:2011:APEA Parallel Error Diffusion Implementation on a GPU
12Wang:2016:FPSFast Parallel Skew and Prefix-Doubling Suffix Array Construction on the GPU
12Zhang:2011:AHMA Hybrid Method for Solving Tridiagonal Systems on the GPU
11Khailany:2000:ISAImagine: Signal and Image Processing Using Streams
11Ebeida:2013:SDSifted Disks
10Ashkiani:2018:GLAGPU LSM: A Dynamic Dictionary Data Structure for the GPU
9Zhang:2012:PDEPlane-dependent Error Diffusion on a GPU
8Awad:2019:EAHEngineering a High-Performance GPU B-Tree
8Muyan-Ozcelik:2016:MREMultitasking Real-time Embedded GPU Computing Tasks
8Ebeida:2016:DDTDisk Density Tuning of a Maximal Random Packing
6Owens:2004:GTFGPUs tapped for general computing
6Yang:2018:IPEImplementing Push-Pull Efficiently in GraphBLAS
6Geil:2014:WGCWTF, GPU! Computing Twitter's Who-To-Follow on the GPU
5Abdelkader:2017:ACRA Constrained Resampling Strategy for Mesh Improvement
5Ashkiani:2016:PATParallel Approaches to the String Matching Problem on the GPU
5Ashkiani:2017:GMAGPU Multisplit: an extended study of a parallel algorithm
4Liu:2018:OLAObject Localization and Motion Transfer learning with Capsules
4Mak:2014:GAEGPU-Accelerated and Efficient Multi-View Triangulation for Scene Reconstruction
4Geil:2018:QFAQuotient Filters: Approximate Membership Queries on the GPU
4Abdelkader:2019:VVMVoroCrust: Voronoi Meshing Without Clipping
4Weber:2015:PRAParallel Reyes-style Adaptive Subdivision with Bounded Memory Usage
4Owens:2007:TMSTowards Multi-GPU Support for Visualization
4Abdelkader:2018:SCFSampling Conditions for Conforming Voronoi Meshing by the VoroCrust Algorithm
3Pan:2018:SBSScalable Breadth-First Search on a GPU Cluster
3Osama:2019:GCOGraph Coloring on the GPU
3Phillips:2011:AO2Acceleration of 2-D Compressible Flow Solvers with Graphics Processing Unit Clusters
2Szumel:2003:OTFOn the Feasibility of the UC Davis Metanet
2Seitz:2013:AGIA GPU Implementation for Two-Dimensional Shallow Water Modeling
2Owens:2004:OTSOn The Scalability of Sensor Network Routing and Compression Algorithms
2Lin:2018:BDLBenchmarking Deep Learning Frameworks with FPGA-suitable Models on a Traffic Sign Dataset
2Gosink:2008:BIABin-Hash Indexing: A Parallel Method For Fast Query Processing
2Abdelkader:2018:VITVoroCrust Illustrated: Theory and Challenges (Multimedia Exposition)
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:2019:GAHGraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU
1Yih:2018:FVGFPGA versus GPU for Speed-Limit-Sign Recognition
1Silberstein:2011:ASCApplying Software-Managed Caching and CPU/GPU Task Scheduling for Accelerating Dynamic Workloads