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John Owens's calculated h-index is 57. This page was automatically generated on 2023-01-29.

3309Owens:2007:ASOA Survey of General-Purpose Computation on Graphics Hardware
2718Owens:2008:GCGPU Computing
1296Rixner:2000:MASMemory Access Scheduling
1081Harris:2007:PPSParallel Prefix Sum (Scan) with CUDA
821Sengupta:2007:SPFScan Primitives for GPU Computing
631Owens:2007:RCFResearch Challenges for On-Chip Interconnection Networks
534Wang:2016:GAHGunrock: A High-Performance Graph Processing Library on the GPU
491Khailany:2001:IMPImagine: Media Processing with Streams
440Kapasi:2003:PSPProgrammable Stream Processors
400Rixner:2000:ROFRegister Organization for Media Processing
379Zhang:2011:AQPA Quantitative Performance Analysis Model for GPU Architectures
362Kapasi:2002:TISThe Imagine Stream Processor
358Liu:2020:EODEnergy-based Out-of-distribution Detection
358Rixner:1998:ABAA Bandwidth-Efficient Architecture for Media Processing
325Zhang:2010:FTSFast Tridiagonal Solvers on the GPU
290Stuart:2011:MMOMulti-GPU MapReduce on GPU Clusters
250Alcantara:2009:RPHReal-Time Parallel Hashing on the GPU
244Gupta:2012:ASOA Study of Persistent Threads Style GPU Programming for GPGPU Workloads
231Kepner:2016:MFOMathematical Foundations of the GraphBLAS
229Lefohn:2006:GGEGlift: Generic, Efficient, Random-Access GPU Data Structures
217Davidson:2014:WPGWork-Efficient Parallel GPU Methods for Single-Source Shortest Paths
177Owens:2005:SAAStreaming Architectures and Technology Trends
163Tzeng:2010:TMFTask Management for Irregular-Parallel Workloads on the GPU
163Muyan-Ozcelik:2008:FDRFast Deformable Registration on the GPU: A CUDA Implementation of Demons
154Silberstein:2008:ECOEfficient Computation of Sum-products on GPUs Through Software-Managed Cache
146Samant:2008:HPCHigh performance computing for deformable image registration: Towards a new paradigm in adaptive radiotherapy
143Park:2006:DSIDiscrete Sibson Interpolation
138Kapasi:2000:ECOEfficient Conditional Operations for Data-parallel Architectures
133Kass:2006:IDOInteractive Depth of Field Using Simulated Diffusion on a GPU
131Owens:2002:MPAMedia Processing Applications on the Imagine Stream Processor
124Patel:2012:PLDParallel Lossless Data Compression on the GPU
122Ebeida:2011:EMPEfficient Maximal Poisson-Disk Sampling
118Sengupta:2006:AWSA Work-Efficient Step-Efficient Prefix Sum Algorithm
116Wang:2017:GGGGunrock: GPU Graph Analytics
114Davidson:2011:AAMAn Auto-tuned Method for Solving Large Tridiagonal Systems on the GPU
112Stuart:2009:MPOMessage Passing on Data-Parallel Architectures
110Ebeida:2012:ASAA Simple Algorithm for Maximal Poisson-Disk Sampling in High Dimensions
95Lefohn:2007:RSMResolution-Matched Shadow Maps
94Phillips:2009:RAPRapid Aerodynamic Performance Prediction on a Cluster of Graphics Processing Units
93Owens:2000:PROPolygon Rendering on a Stream Architecture
90Davidson:2012:EPMEfficient Parallel Merge Sort for Fixed and Variable Length Keys
84Stuart:2010:MVRMulti-GPU Volume Rendering using MapReduce
76Yang:2018:DPFDesign Principles for Sparse Matrix Multiplication on the GPU
75Khailany:2003:ETVExploring the VLSI Scalability of Stream Processors
74Kapasi:2001:SSStream Scheduling
73Patney:2008:RRAReal-Time Reyes-Style Adaptive Surface Subdivision
70Pan:2017:MGAMulti-GPU Graph Analytics
70Alcantara:2011:BAEBuilding an Efficient Hash Table on the GPU
69Stuart:2011:ESPEfficient Synchronization Primitives for GPUs
65Budge:2009:ODMOut-of-core Data Management for Path Tracing on Hybrid Resources
65Mattson:2000:CSCommunication Scheduling
64Davidson:2012:TTFToward Techniques for Auto-tuning GPU Algorithms
63Davidson:2011:RPFRegister Packing for Cyclic Reduction: A Case Study
61Wang:2016:ACSA Comparative Study on Exact Triangle Counting Algorithms on the GPU
61Owens:2002:CGOComputer Graphics on a Stream Architecture
58Ashkiani:2018:ADHA Dynamic Hash Table for the GPU
58Jenkins:2011:LLFLessons Learned from Exploring the Backtracking Paradigm on the GPU

57Patney:2009:PVTParallel View-Dependent Tessellation of Catmull-Clark Subdivision Surfaces
57Szumel:2005:TAMTowards a Mobile Agent Framework for Sensor Networks
55Moerschell:2008:DTMDistributed Texture Memory in a Multi-GPU Environment
55Lefohn:2005:IEPImplementing Efficient Parallel Data Structures on GPUs
54Yang:2022:GAHGraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU
44Ebeida:2011:EAGEfficient and Good Delaunay Meshes From Random Points
43Tzeng:2012:AGTA GPU Task-Parallel Model with Dependency Resolution
39Owens:2002:CRAComparing Reyes and OpenGL on a Stream Architecture
38Awad:2019:EAHEngineering a High-Performance GPU B-Tree
37Ebeida:2011:ICRIsotropic conforming refinement of quadrilateral and hexahedral meshes using two-refinement templates
37Riffel:2004:MFMMio: Fast Multipass Partitioning via Priority-Based Instruction Scheduling
36Wu:2015:PCOPerformance Characterization of High-Level Programming Models for GPU Graph Analytics
36Lefohn:2005:DASDynamic Adaptive Shadow Maps on Graphics Hardware
33Yang:2015:FSMFast Sparse Matrix and Sparse Vector Multiplication Algorithm on the GPU
32Abdelkader:2020:VVMVoroCrust: Voronoi Meshing Without Clipping
31Yang:2018:IPEImplementing Push-Pull Efficiently in GraphBLAS
31Stuart:2011:EMTExtending MPI to Accelerators
30Owens:2005:AOGAssessment of Graphic Processing Units (GPUs) for Department of Defense (DoD) Digital Signal Processing (DSP) Applications
29Stone:2011:GPAGPGPU parallel algorithms for structured-grid CFD codes
28Glavtchev:2011:FSLFeature-Based Speed Limit Sign Detection Using a Graphics Processing Unit
27Stuart:2010:GCGPU-to-CPU Callbacks
27Zhang:2011:AHMA Hybrid Method for Solving Tridiagonal Systems on the GPU
26Ashkiani:2018:GLAGPU LSM: A Dynamic Dictionary Data Structure for the GPU
25Ashkiani:2016:GMGPU Multisplit
25Gosink:2009:DPBData Parallel Bin-Based Indexing for Answering Queries on Multi-Core Architectures
25Kniss:2005:OTOOctree Textures on Graphics Hardware
24Patney:2010:FCAFragment-Parallel Composite and Filter
24Tzeng:2012:FCHFinding Convex Hulls Using Quickhull on the GPU
23Gupta:2009:TOFThree-Layer Optimizations for Fast GMM Computations on GPU-like Parallel Processors
23Park:2005:AFFA Framework for Real-Time Volume Visualization of Streaming Scattered Data
22Lin:2019:BDLBenchmarking Deep Learning Frameworks and Investigating FPGA Deployment for Traffic Sign Classification and Detection
22Patney:2015:PAFPiko: A Framework for Authoring Programmable Graphics Pipelines
21Wang:2015:FSAFast Parallel Suffix Array on the GPU
21Phillips:2010:UTSUnsteady Turbulent Simulations on a Cluster of Graphics Processors
20Ma:2007:UVRUltra-Scale Visualization: Research and Education
20Gosink:2008:BIABin-Hash Indexing: A Parallel Method For Fast Query Processing
18Osama:2019:GCOGraph Coloring on the GPU
18Ebeida:2014:KDS$k$-d Darts: Sampling by $k$-Dimensional Flat Searches
18Tzeng:2012:HPDHigh-Quality Parallel Depth-of-Field Using Line Samples
18Zhang:2011:APEA Parallel Error Diffusion Implementation on a GPU
18Muyan-Ozcelik:2010:ATAA Template-Based Approach for Real-Time Speed-Limit-Sign Recognition on an Embedded System using GPU Computing
17Awad:2020:DGODynamic Graphs on the GPU
17Geil:2018:QFAQuotient Filters: Approximate Membership Queries on the GPU
17Szumel:2006:TVPThe Virtual Pheromone Communication Primitive
17Serebrin:2002:ASPA Stream Processor Development Platform
16Wang:2016:FPSFast Parallel Skew and Prefix-Doubling Suffix Array Construction on the GPU
16Gupta:2011:CAMCompute \& Memory Optimizations for High-Quality Speech Recognition on Low-End GPU Processors
15Muyan-Ozcelik:2011:RSRReal-Time Speed-Limit-Sign Recognition on an Embedded System Using a GPU
15Khailany:2000:ISAImagine: Signal and Image Processing Using Streams
13Wang:2019:ADIAccelerating DNN Inference with GraphBLAS and the GPU
12Abdelkader:2018:SCFSampling Conditions for Conforming Voronoi Meshing by the VoroCrust Algorithm
12Ashkiani:2017:GMAGPU Multisplit: an extended study of a parallel algorithm
11Abdelkader:2017:ACRA Constrained Resampling Strategy for Mesh Improvement
11Muyan-Ozcelik:2016:MREMultitasking Real-time Embedded GPU Computing Tasks
11Ebeida:2013:SDSifted Disks
11Zhang:2012:PDEPlane-dependent Error Diffusion on a GPU
11Wang:2020:FGSFast Gunrock Subgraph Matching (GSM) on GPUs
10Ebeida:2016:DDTDisk Density Tuning of a Maximal Random Packing
10Geil:2014:WGCWTF, GPU! Computing Twitter's Who-To-Follow on the GPU
9Wang:2019:FBTFast BFS-Based Triangle Counting on GPUs
9Pan:2018:SBSScalable Breadth-First Search on a GPU Cluster
9Ashkiani:2016:PATParallel Approaches to the String Matching Problem on the GPU
8Liu:2018:OLAObject Localization and Motion Transfer learning with Capsules
7Yih:2018:FVGFPGA versus GPU for Speed-Limit-Sign Recognition
7Owens:2007:TMSTowards Multi-GPU Support for Visualization
6Seitz:2019:SMFStaged Metaprogramming for Shader System Development
6Owens:2004:GTFGPUs tapped for general computing
5Mahmoud:2021:RAGRXMesh: A GPU Mesh Data Structure
5Lin:2018:BDLBenchmarking Deep Learning Frameworks with FPGA-suitable Models on a Traffic Sign Dataset
5Weber:2015:PRAParallel Reyes-style Adaptive Subdivision with Bounded Memory Usage
4Mak:2014:GAEGPU-Accelerated and Efficient Multi-View Triangulation for Scene Reconstruction
4Phillips:2011:AO2Acceleration of 2-D Compressible Flow Solvers with Graphics Processing Unit Clusters
4Szumel:2003:OTFOn the Feasibility of the UC Davis Metanet
3Brock:2019:RVRRDMA vs.\ RPC for Implementing Distributed Data Structures
3Abdelkader:2018:VITVoroCrust Illustrated: Theory and Challenges (Multimedia Exposition)
3Ebeida:2014:EIHExercises in High-Dimensional Sampling: Maximal Poisson-disk Sampling and $k$-d Darts
2Chen:2022:AATAtos: A Task-Parallel GPU Scheduler for Graph Analytics
2Owens:2018:TPGTechnical Perspective: Graphs, Betweenness Centrality, and the GPU
2Wang:2017:MALMini-Gunrock: A Lightweight Graph Analytics Framework on the GPU
2Gegan:2016:RGTReal-Time GPU-based Timing Channel Detection using Entropy
2Seitz:2013:AGIA GPU Implementation for Two-Dimensional Shallow Water Modeling
2Owens:2004:OTSOn The Scalability of Sensor Network Routing and Compression Algorithms
1Lin:2022:BAPBuilding a Performance Model for Deep Learning Recommendation Model Training on GPUs
1Osama:2022:EOPEssentials of Parallel Graph Analytics
1Muyan-Ozcelik:2017:MFMMethods for Multitasking among Real-time Embedded Compute Tasks Running on the GPU
1Kemal:2016:MSAMultidisciplinary simulation acceleration using multiple shared memory graphical processing units
1Silberstein:2011:ASCApplying Software-Managed Caching and CPU/GPU Task Scheduling for Accelerating Dynamic Workloads
1Owens:2006:TIAThe Installation and Use of OpenType Fonts in \LaTeX
1Liu:2019:UOSUnsupervised Object Segmentation with Explicit Localization Module

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