--- ---

John Owens's calculated h-index is 43. This page was automatically generated on 2016-09-06.

2482Owens:2007:ASOA Survey of General-Purpose Computation on Graphics Hardware
1570Owens:2008:GCGPU Computing
789Rixner:2000:MASMemory Access Scheduling
618Harris:2007:PPSParallel Prefix Sum (Scan) with CUDA
575Sengupta:2007:SPFScan Primitives for GPU Computing
433Owens:2007:RCFResearch Challenges for On-Chip Interconnection Networks
408Khailany:2001:IMPImagine: Media Processing with Streams
375Kapasi:2003:PSPProgrammable Stream Processors
351Rixner:2000:ROFRegister Organization for Media Processing
309Rixner:1998:ABAA Bandwidth-Efficient Architecture for Media Processing
275Kapasi:2002:TISThe Imagine Stream Processor
197Zhang:2011:AQPA Quantitative Performance Analysis Model for GPU Architectures
175Lefohn:2006:GGEGlift: Generic, Efficient, Random-Access GPU Data Structures
168Zhang:2010:FTSFast Tridiagonal Solvers on the GPU
143Stuart:2011:MMOMulti-GPU MapReduce on GPU Clusters
138Owens:2005:SAAStreaming Architectures and Technology Trends
124Alcantara:2009:RPHReal-Time Parallel Hashing on the GPU
121Silberstein:2008:ECOEfficient Computation of Sum-products on GPUs Through Software-Managed Cache
120Kapasi:2000:ECOEfficient Conditional Operations for Data-parallel Architectures
113Muyan-Ozcelik:2008:FDRFast Deformable Registration on the GPU: A CUDA Implementation of Demons
107Samant:2008:HPCHigh performance computing for deformable image registration: Towards a new paradigm in adaptive radiotherapy
106Owens:2002:MPAMedia Processing Applications on the Imagine Stream Processor
86Kass:2006:IDOInteractive Depth of Field Using Simulated Diffusion on a GPU
85Tzeng:2010:TMFTask Management for Irregular-Parallel Workloads on the GPU
75Park:2006:DSIDiscrete Sibson Interpolation
75Sengupta:2006:AWSA Work-Efficient Step-Efficient Prefix Sum Algorithm
74Owens:2000:PROPolygon Rendering on a Stream Architecture
71Stuart:2009:MPOMessage Passing on Data-Parallel Architectures
71Phillips:2009:RAPRapid Aerodynamic Performance Prediction on a Cluster of Graphics Processing Units
71Lefohn:2007:RSMResolution-Matched Shadow Maps
65Khailany:2003:ETVExploring the VLSI Scalability of Stream Processors
57Kapasi:2001:SSStream Scheduling
57Mattson:2000:CSCommunication Scheduling
56Gupta:2012:ASOA Study of Persistent Threads Style GPU Programming for GPGPU Workloads
56Ebeida:2011:EMPEfficient Maximal Poisson-Disk Sampling
55Patney:2008:RRAReal-Time Reyes-Style Adaptive Surface Subdivision
53Davidson:2011:AAMAn Auto-tuned Method for Solving Large Tridiagonal Systems on the GPU
53Owens:2002:CGOComputer Graphics on a Stream Architecture
50Stuart:2010:MVRMulti-GPU Volume Rendering using MapReduce
46Ebeida:2012:ASAA Simple Algorithm for Maximal Poisson-Disk Sampling in High Dimensions
46Moerschell:2008:DTMDistributed Texture Memory in a Multi-GPU Environment
46Lefohn:2005:IEPImplementing Efficient Parallel Data Structures on GPUs
43Budge:2009:ODMOut-of-core Data Management for Path Tracing on Hybrid Resources

43Patel:2012:PLDParallel Lossless Data Compression on the GPU
42Szumel:2005:TAMTowards a Mobile Agent Framework for Sensor Networks
41Davidson:2014:WPGWork-Efficient Parallel GPU Methods for Single Source Shortest Paths
36Patney:2009:PVTParallel View-Dependent Tessellation of Catmull-Clark Subdivision Surfaces
35Lefohn:2005:DASDynamic Adaptive Shadow Maps on Graphics Hardware
35Davidson:2012:EPMEfficient Parallel Merge Sort for Fixed and Variable Length Keys
31Owens:2002:CRAComparing Reyes and OpenGL on a Stream Architecture
30Sengupta:2011:EPSEfficient Parallel Scan Algorithms for many-core GPUs
29Riffel:2004:MFMMio: Fast Multipass Partitioning via Priority-Based Instruction Scheduling
27Davidson:2011:RPFRegister Packing for Cyclic Reduction: A Case Study
26Davidson:2012:TTFToward Techniques for Auto-tuning GPU Algorithms
26Davidson:2010:TTFToward Techniques for Auto-Tuning GPU Algorithms
25Ebeida:2011:EAGEfficient and Good Delaunay Meshes From Random Points
25Stuart:2011:ESPEfficient Synchronization Primitives for GPUs
25Wang:2016:GAHGunrock: A High-Performance Graph Processing Library on the GPU
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
19Stone:2011:GPAGPGPU parallel algorithms for structured-grid CFD codes
19Jenkins:2011:LLFLessons Learned from Exploring the Backtracking Paradigm on the GPU
19Ebeida:2011:ICRIsotropic conforming refinement of quadrilateral and hexahedral meshes using two-refinement templates
18Kniss:2005:OTOOctree Textures on Graphics Hardware
18Glavtchev:2011:FSLFeature-Based Speed Limit Sign Detection Using a Graphics Processing Unit
18Alcantara:2011:BAEBuilding an Efficient Hash Table on the GPU
16Tzeng:2012:AGTA GPU Task-Parallel Model with Dependency Resolution
15Stuart:2010:GCGPU-to-CPU Callbacks
15Serebrin:2002:ASPA Stream Processor Development Platform
15Muyan-Ozcelik:2010:ATAA Template-Based Approach for Real-Time Speed-Limit-Sign Recognition on an Embedded System using GPU Computing
15Gupta:2009:TOFThree-Layer Optimizations for Fast GMM Computations on GPU-like Parallel Processors
14Patney:2010:FCAFragment-Parallel Composite and Filter
14Stuart:2011:EMTExtending MPI to Accelerators
14Gosink:2009:DPBData Parallel Bin-Based Indexing for Answering Queries on Multi-Core Architectures
14Szumel:2006:TVPThe Virtual Pheromone Communication Primitive
13Phillips:2010:UTSUnsteady Turbulent Simulations on a Cluster of Graphics Processors
12Ma:2007:UVRUltra-Scale Visualization: Research and Education
12Tzeng:2012:FCHFinding Convex Hulls Using Quickhull on the GPU
11Khailany:2000:ISAImagine: Signal and Image Processing Using Streams
11Tzeng: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
7Li:2012:KOTkANN on the GPU with Shifted Sorting
7Ebeida:2013:SDSifted Disks
7Wu:2015:PCOPerformance Characterization of High-Level Programming Models for GPU Graph Analytics
7Gupta:2011:CAMCompute \& Memory Optimizations for High-Quality Speech Recognition on Low-End GPU Processors
5Owens:2004:GTFGPUs tapped for general computing
3Yang:2015:FSMFast Sparse Matrix and Sparse Vector Multiplication Algorithm on the GPU
3Pan:2016:MGAMulti-GPU Graph Analytics
3Owens:2007:TMSTowards Multi-GPU Support for Visualization
2Zhang:2012:PDEPlane-dependent Error Diffusion on a GPU
2Seitz:2013:AGIA GPU Implementation for Two-Dimensional Shallow Water Modeling
2Ashkiani:2016:GMGPU Multisplit
2Weber:2015:PRAParallel Reyes-style Adaptive Subdivision with Bounded Memory Usage
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
1Patney:2015:PAFPiko: A Framework for Authoring Programmable Graphics Pipelines
1Phillips:2011:AO2Acceleration of 2-D Compressible Flow Solvers with Graphics Processing Unit Clusters

---