--- ---

John Owens's calculated h-index is 42. This page was automatically generated on 2016-05-25.

2427Owens:2007:ASOA Survey of General-Purpose Computation on Graphics Hardware
1512Owens:2008:GCGPU Computing
765Rixner:2000:MASMemory Access Scheduling
601Harris:2007:PPSParallel Prefix Sum (Scan) with CUDA
558Sengupta:2007:SPFScan Primitives for GPU Computing
422Owens:2007:RCFResearch Challenges for On-Chip Interconnection Networks
408Khailany:2001:IMPImagine: Media Processing with Streams
372Kapasi:2003:PSPProgrammable Stream Processors
349Rixner:2000:ROFRegister Organization for Media Processing
308Rixner:1998:ABAA Bandwidth-Efficient Architecture for Media Processing
269Kapasi:2002:TISThe Imagine Stream Processor
189Zhang:2011:AQPA Quantitative Performance Analysis Model for GPU Architectures
173Lefohn:2006:GGEGlift: Generic, Efficient, Random-Access GPU Data Structures
160Zhang:2010:FTSFast Tridiagonal Solvers on the GPU
135Stuart:2011:MMOMulti-GPU MapReduce on GPU Clusters
134Owens:2005:SAAStreaming Architectures and Technology Trends
121Kapasi:2000:ECOEfficient Conditional Operations for Data-parallel Architectures
119Silberstein:2008:ECOEfficient Computation of Sum-products on GPUs Through Software-Managed Cache
114Alcantara:2009:RPHReal-Time Parallel Hashing on the GPU
107Samant:2008:HPCHigh performance computing for deformable image registration: Towards a new paradigm in adaptive radiotherapy
105Muyan-Ozcelik:2008:FDRFast Deformable Registration on the GPU: A CUDA Implementation of Demons
105Owens:2002:MPAMedia Processing Applications on the Imagine Stream Processor
85Kass:2006:IDOInteractive Depth of Field Using Simulated Diffusion on a GPU
82Tzeng:2010:TMFTask Management for Irregular-Parallel Workloads on the GPU
75Sengupta:2006:AWSA Work-Efficient Step-Efficient Prefix Sum Algorithm
74Owens:2000:PROPolygon Rendering on a Stream Architecture
73Park:2006:DSIDiscrete Sibson Interpolation
69Phillips:2009:RAPRapid Aerodynamic Performance Prediction on a Cluster of Graphics Processing Units
69Lefohn:2007:RSMResolution-Matched Shadow Maps
68Stuart:2009:MPOMessage Passing on Data-Parallel Architectures
66Khailany:2003:ETVExploring the VLSI Scalability of Stream Processors
57Mattson:2000:CSCommunication Scheduling
55Kapasi:2001:SSStream Scheduling
54Ebeida:2011:EMPEfficient Maximal Poisson-Disk Sampling
54Patney:2008:RRAReal-Time Reyes-Style Adaptive Surface Subdivision
53Owens:2002:CGOComputer Graphics on a Stream Architecture
50Davidson:2011:AAMAn Auto-tuned Method for Solving Large Tridiagonal Systems on the GPU
48Gupta:2012:ASOA Study of Persistent Threads Style GPU Programming for GPGPU Workloads
44Moerschell:2008:DTMDistributed Texture Memory in a Multi-GPU Environment
44Stuart:2010:MVRMulti-GPU Volume Rendering using MapReduce
44Lefohn:2005:IEPImplementing Efficient Parallel Data Structures on GPUs
43Ebeida:2012:ASAA Simple Algorithm for Maximal Poisson-Disk Sampling in High Dimensions

42Budge:2009:ODMOut-of-core Data Management for Path Tracing on Hybrid Resources
42Szumel:2005:TAMTowards a Mobile Agent Framework for Sensor Networks
37Patel:2012:PLDParallel Lossless Data Compression on the GPU
37Patney:2009:PVTParallel View-Dependent Tessellation of Catmull-Clark Subdivision Surfaces
33Owens:2002:CRAComparing Reyes and OpenGL on a Stream Architecture
33Davidson:2014:WPGWork-Efficient Parallel GPU Methods for Single Source Shortest Paths
33Davidson:2012:EPMEfficient Parallel Merge Sort for Fixed and Variable Length Keys
32Lefohn:2005:DASDynamic Adaptive Shadow Maps on Graphics Hardware
30Riffel:2004:MFMMio: Fast Multipass Partitioning via Priority-Based Instruction Scheduling
28Sengupta:2011:EPSEfficient Parallel Scan Algorithms for many-core GPUs
25Davidson:2012:TTFToward Techniques for Auto-tuning GPU Algorithms
25Davidson:2010:TTFToward Techniques for Auto-Tuning GPU Algorithms
24Ebeida:2011:EAGEfficient and Good Delaunay Meshes From Random Points
24Davidson:2011:RPFRegister Packing for Cyclic Reduction: A Case Study
24Stuart:2011:ESPEfficient Synchronization Primitives for GPUs
24Owens:2005:AOGAssessment of Graphic Processing Units (GPUs) for Department of Defense (DoD) Digital Signal Processing (DSP) Applications
20Park:2005:AFFA Framework for Real-Time Volume Visualization of Streaming Scattered Data
19Kniss:2005:OTOOctree Textures on Graphics Hardware
18Alcantara:2011:BAEBuilding an Efficient Hash Table on the GPU
17Stone:2011:GPAGPGPU parallel algorithms for structured-grid CFD codes
16Jenkins:2011:LLFLessons Learned from Exploring the Backtracking Paradigm on the GPU
15Stuart:2010:GCGPU-to-CPU Callbacks
15Serebrin:2002:ASPA Stream Processor Development Platform
15Gupta:2009:TOFThree-Layer Optimizations for Fast GMM Computations on GPU-like Parallel Processors
14Glavtchev:2011:FSLFeature-Based Speed Limit Sign Detection Using a Graphics Processing Unit
14Gosink:2009:DPBData Parallel Bin-Based Indexing for Answering Queries on Multi-Core Architectures
14Tzeng:2012:AGTA GPU Task-Parallel Model with Dependency Resolution
14Wang:2016:GAHGunrock: A High-Performance Graph Processing Library on the GPU
14Ebeida:2011:ICRIsotropic conforming refinement of quadrilateral and hexahedral meshes using two-refinement templates
13Stuart:2011:EMTExtending MPI to Accelerators
13Muyan-Ozcelik:2010:ATAA Template-Based Approach for Real-Time Speed-Limit-Sign Recognition on an Embedded System using GPU Computing
13Phillips:2010:UTSUnsteady Turbulent Simulations on a Cluster of Graphics Processors
12Patney:2010:FCAFragment-Parallel Composite and Filter
12Ma:2007:UVRUltra-Scale Visualization: Research and Education
12Szumel:2006:TVPThe Virtual Pheromone Communication Primitive
11Khailany:2000:ISAImagine: Signal and Image Processing Using Streams
10Tzeng:2012:HPDHigh-Quality Parallel Depth-of-Field Using Line Samples
10Zhang:2011:APEA Parallel Error Diffusion Implementation on a GPU
10Zhang:2011:AHMA Hybrid Method for Solving Tridiagonal Systems on the GPU
9Tzeng:2012:FCHFinding Convex Hulls Using Quickhull on the GPU
6Li:2012:KOTkANN on the GPU with Shifted Sorting
5Owens:2004:GTFGPUs tapped for general computing
5Ebeida:2013:SDSifted Disks
5Gupta:2011:CAMCompute \& Memory Optimizations for High-Quality Speech Recognition on Low-End GPU Processors
2Zhang:2012:PDEPlane-dependent Error Diffusion on a GPU
2Wu:2015:PCFPerformance Characterization of High-Level Programming Models for GPU Graph Analytics
2Seitz:2013:AGIA GPU Implementation for Two-Dimensional Shallow Water Modeling
2Pan:2016:MGAMulti-GPU Graph Analytics
2Weber:2015:PRAParallel Reyes-style Adaptive Subdivision with Bounded Memory Usage
2Owens:2007:TMSTowards Multi-GPU Support for Visualization
1Yang:2015:FSMFast Sparse Matrix and Sparse Vector Multiplication Algorithm on the GPU
1Szumel:2003:OTFOn the Feasibility of the UC Davis Metanet
1Mak:2014:GAEGPU-Accelerated and Efficient Multi-View Triangulation for Scene Reconstruction
1Owens:2004:OTSOn The Scalability of Sensor Network Routing and Compression Algorithms
1Geil:2014:WGCWTF, GPU! Computing Twitter's Who-To-Follow on the GPU
1Ashkiani:2016:GMGPU Multisplit
1Phillips:2011:AO2Acceleration of 2-D Compressible Flow Solvers with Graphics Processing Unit Clusters