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John Owens's calculated h-index is 46. This page was automatically generated on 2017-04-13.

2654Owens:2007:ASOA Survey of General-Purpose Computation on Graphics Hardware
1750Owens:2008:GCGPU Computing
858Rixner:2000:MASMemory Access Scheduling
666Harris:2007:PPSParallel Prefix Sum (Scan) with CUDA
615Sengupta:2007:SPFScan Primitives for GPU Computing
471Owens:2007:RCFResearch Challenges for On-Chip Interconnection Networks
420Khailany:2001:IMPImagine: Media Processing with Streams
397Kapasi:2003:PSPProgrammable Stream Processors
362Rixner:2000:ROFRegister Organization for Media Processing
316Rixner:1998:ABAA Bandwidth-Efficient Architecture for Media Processing
292Kapasi:2002:TISThe Imagine Stream Processor
226Zhang:2011:AQPA Quantitative Performance Analysis Model for GPU Architectures
189Lefohn:2006:GGEGlift: Generic, Efficient, Random-Access GPU Data Structures
185Zhang:2010:FTSFast Tridiagonal Solvers on the GPU
171Stuart:2011:MMOMulti-GPU MapReduce on GPU Clusters
145Owens:2005:SAAStreaming Architectures and Technology Trends
141Alcantara:2009:RPHReal-Time Parallel Hashing on the GPU
131Silberstein:2008:ECOEfficient Computation of Sum-products on GPUs Through Software-Managed Cache
128Kapasi:2000:ECOEfficient Conditional Operations for Data-parallel Architectures
114Muyan-Ozcelik:2008:FDRFast Deformable Registration on the GPU: A CUDA Implementation of Demons
113Samant:2008:HPCHigh performance computing for deformable image registration: Towards a new paradigm in adaptive radiotherapy
110Owens:2002:MPAMedia Processing Applications on the Imagine Stream Processor
98Tzeng:2010:TMFTask Management for Irregular-Parallel Workloads on the GPU
90Kass:2006:IDOInteractive Depth of Field Using Simulated Diffusion on a GPU
86Gupta:2012:ASOA Study of Persistent Threads Style GPU Programming for GPGPU Workloads
84Sengupta:2006:AWSA Work-Efficient Step-Efficient Prefix Sum Algorithm
80Park:2006:DSIDiscrete Sibson Interpolation
76Stuart:2009:MPOMessage Passing on Data-Parallel Architectures
76Owens:2000:PROPolygon Rendering on a Stream Architecture
75Phillips:2009:RAPRapid Aerodynamic Performance Prediction on a Cluster of Graphics Processing Units
73Lefohn:2007:RSMResolution-Matched Shadow Maps
72Ebeida:2011:EMPEfficient Maximal Poisson-Disk Sampling
66Khailany:2003:ETVExploring the VLSI Scalability of Stream Processors
62Kapasi:2001:SSStream Scheduling
61Mattson:2000:CSCommunication Scheduling
59Stuart:2010:MVRMulti-GPU Volume Rendering using MapReduce
59Patney:2008:RRAReal-Time Reyes-Style Adaptive Surface Subdivision
57Davidson:2011:AAMAn Auto-tuned Method for Solving Large Tridiagonal Systems on the GPU
56Davidson:2014:WPGWork-Efficient Parallel GPU Methods for Single Source Shortest Paths
56Ebeida:2012:ASAA Simple Algorithm for Maximal Poisson-Disk Sampling in High Dimensions
55Patel:2012:PLDParallel Lossless Data Compression on the GPU
54Owens:2002:CGOComputer Graphics on a Stream Architecture
48Moerschell:2008:DTMDistributed Texture Memory in a Multi-GPU Environment
48Lefohn:2005:IEPImplementing Efficient Parallel Data Structures on GPUs
46Budge:2009:ODMOut-of-core Data Management for Path Tracing on Hybrid Resources
46Szumel:2005:TAMTowards a Mobile Agent Framework for Sensor Networks

44Wang:2016:GAHGunrock: A High-Performance Graph Processing Library on the GPU
41Davidson:2012:EPMEfficient Parallel Merge Sort for Fixed and Variable Length Keys
41Patney:2009:PVTParallel View-Dependent Tessellation of Catmull-Clark Subdivision Surfaces
36Owens:2002:CRAComparing Reyes and OpenGL on a Stream Architecture
35Lefohn:2005:DASDynamic Adaptive Shadow Maps on Graphics Hardware
33Stuart:2011:ESPEfficient Synchronization Primitives for GPUs
30Ebeida:2011:EAGEfficient and Good Delaunay Meshes From Random Points
30Davidson:2012:TTFToward Techniques for Auto-tuning GPU Algorithms
30Davidson:2011:RPFRegister Packing for Cyclic Reduction: A Case Study
30Davidson:2010:TTFToward Techniques for Auto-Tuning GPU Algorithms
30Sengupta:2011:EPSEfficient Parallel Scan Algorithms for many-core GPUs
28Riffel:2004:MFMMio: Fast Multipass Partitioning via Priority-Based Instruction Scheduling
26Owens:2005:AOGAssessment of Graphic Processing Units (GPUs) for Department of Defense (DoD) Digital Signal Processing (DSP) Applications
25Jenkins:2011:LLFLessons Learned from Exploring the Backtracking Paradigm on the GPU
23Alcantara:2011:BAEBuilding an Efficient Hash Table on the GPU
23Ebeida:2011:ICRIsotropic conforming refinement of quadrilateral and hexahedral meshes using two-refinement templates
22Glavtchev:2011:FSLFeature-Based Speed Limit Sign Detection Using a Graphics Processing Unit
21Park:2005:AFFA Framework for Real-Time Volume Visualization of Streaming Scattered Data
20Stone:2011:GPAGPGPU parallel algorithms for structured-grid CFD codes
19Kniss:2005:OTOOctree Textures on Graphics Hardware
19Tzeng:2012:AGTA GPU Task-Parallel Model with Dependency Resolution
19Muyan-Ozcelik:2010:ATAA Template-Based Approach for Real-Time Speed-Limit-Sign Recognition on an Embedded System using GPU Computing
17Stuart:2011:EMTExtending MPI to Accelerators
17Stuart:2010:GCGPU-to-CPU Callbacks
17Szumel:2006:TVPThe Virtual Pheromone Communication Primitive
17Phillips:2010:UTSUnsteady Turbulent Simulations on a Cluster of Graphics Processors
16Gupta:2009:TOFThree-Layer Optimizations for Fast GMM Computations on GPU-like Parallel Processors
15Patney:2010:FCAFragment-Parallel Composite and Filter
15Gosink:2009:DPBData Parallel Bin-Based Indexing for Answering Queries on Multi-Core Architectures
15Serebrin:2002:ASPA Stream Processor Development Platform
13Ma:2007:UVRUltra-Scale Visualization: Research and Education
13Tzeng:2012:FCHFinding Convex Hulls Using Quickhull on the GPU
12Tzeng:2012:HPDHigh-Quality Parallel Depth-of-Field Using Line Samples
12Zhang:2011:APEA Parallel Error Diffusion Implementation on a GPU
11Li:2012:KOTkANN on the GPU with Shifted Sorting
11Khailany:2000:ISAImagine: Signal and Image Processing Using Streams
11Zhang:2011:AHMA Hybrid Method for Solving Tridiagonal Systems on the GPU
10Wu:2015:PCOPerformance Characterization of High-Level Programming Models for GPU Graph Analytics
8Ebeida:2013:SDSifted Disks
8Gupta:2011:CAMCompute \& Memory Optimizations for High-Quality Speech Recognition on Low-End GPU Processors
6Zhang:2012:PDEPlane-dependent Error Diffusion on a GPU
6Owens:2004:GTFGPUs tapped for general computing
5Yang:2015:FSMFast Sparse Matrix and Sparse Vector Multiplication Algorithm on the GPU
5Pan:2017:MGAMulti-GPU Graph Analytics
4Kepner:2016:MFOMathematical Foundations of the GraphBLAS
3Patney:2015:PAFPiko: A Framework for Authoring Programmable Graphics Pipelines
3Ashkiani:2016:GMGPU Multisplit
3Weber:2015:PRAParallel Reyes-style Adaptive Subdivision with Bounded Memory Usage
3Owens:2007:TMSTowards Multi-GPU Support for Visualization
2Seitz:2013:AGIA GPU Implementation for Two-Dimensional Shallow Water Modeling
2Ebeida:2016:DDTDisk Density Tuning of a Maximal Random Packing
2Owens:2004:OTSOn The Scalability of Sensor Network Routing and Compression Algorithms
2Wang:2017:GGGGunrock: GPU Graph Analytics
1Szumel:2003:OTFOn the Feasibility of the UC Davis Metanet
1Mak:2014:GAEGPU-Accelerated and Efficient Multi-View Triangulation for Scene Reconstruction
1Wang:2016:ACSA Comparative Study on Exact Triangle Counting Algorithms on the GPU
1Geil:2014:WGCWTF, GPU! Computing Twitter's Who-To-Follow on the GPU
1Gosink:2008:BIABin-Hash Indexing: A Parallel Method For Fast Query Processing
1Wang:2016:FPSFast Parallel Skew and Prefix-Doubling Suffix Array Construction on the GPU
1Phillips:2011:AO2Acceleration of 2-D Compressible Flow Solvers with Graphics Processing Unit Clusters