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Face Detection on CUDA

Raksha Patel, Isha Vajani

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Tags: Algorithms, Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GT 410 M

February 27, 2015 by hgpu

A Graph-Partition-Based Scheduling Policy for Heterogeneous Architectures

Hao Wu, Daniel Lohmann, Wolfgang Schroder-Preikschat

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX Titan, Task scheduling

February 27, 2015 by hgpu

Very Fast Non-Dominated Sorting

Czeslaw Smutnicki, Jaroslaw Rudy, Dominik Zelazny

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Tags: Algorithms, Computational Complexity, Computer science, CUDA, nVidia, Sorting, Tesla K20

February 24, 2015 by hgpu

Raster2Mesh: Rasterization based CVT meshing

Jonas Martinez, Frederic Claux, Sylvain Lefebvre

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Tags: Algorithms, Computer science, nVidia, nVidia GeForce GTX Titan, OpenGL, Rasterization, Tessellation, Voronoi diagram

February 24, 2015 by hgpu

A Vision for GPU-accelerated Parallel Computation on Geo-Spatial Datasets

Sushil K. Prasad, Michael McDermott, Satish Puri, Dhara Shah, Danial Aghajarian, Shashi Shekhar, Xun Zhou

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Tags: Computer science, CUDA, Databases, nVidia

February 24, 2015 by hgpu

A Virtual Machine Model for Accelerating Relational Database Joins using a General Purpose GPU

Kevin Angstadt, Ed Harcourt

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Tags: Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX 460, nVidia GeForce GTX 760

February 24, 2015 by hgpu

Characterising Bipartite Graph Matching Algorithms on GPUs

Stanley Tsang

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Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia, Tesla K20, Thesis

February 23, 2015 by hgpu

Investigation of the OpenCL SYCL Programming Model

Angelos Trigkas

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Tags: Computer science, Heterogeneous systems, Intel Xeon Phi, OpenCL, Thesis

February 23, 2015 by hgpu

Stochastic Gradient Descent on GPUs

Rashid Kaleem, Sreepathi Pai, Keshav Pingali

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Tags: Algorithms, Computer science, Machine learning, nVidia, OpenCL, Performance, Tesla K40

February 22, 2015 by hgpu

High performance methods for frequent pattern mining

Lan Vu

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Tags: Computer science, CUDA, Data mining, Databases, nVidia, Tesla C2050, Thesis

February 22, 2015 by hgpu

Comparison of SPMV performance on matrices with different matrix format using CUSP, cuSPARSE and ViennaCL

Ang Li, Hammad Mazhar, Radu Serban, Dan Negrut

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Tags: Computer science, CUDA, Linear Algebra, nVidia, OpenCL, Performance, Python, Sparse matrix, Tesla K20

February 22, 2015 by hgpu

QPACE 2 and Domain Decomposition on the Intel Xeon Phi

Paul Arts, Jacques Bloch, Peter Georg, Benjamin Glaessle, Simon Heybrock, Yu Komatsubara, Robert Lohmayer, Simon Mages, Bernhard Mendl, Nils Meyer, Alessio Parcianello, Dirk Pleiter, Florian Rappl, Mauro Rossi, Stefan Solbrig, Giampietro Tecchiolli, Tilo Wettig, Gianpaolo Zanier

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Tags: Benchmarking, Computational Physics, Computer science, High Energy Physics - Lattice, Intel Xeon Phi

February 22, 2015 by hgpu

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

hls4ml: Machine learning on FPGAs using HLS

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

ThunderKittens: Tile primitives for speedy kernels

ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels

NVIDIA Nemotron Parse 1.1

NVIDIA Nemotron Parse 1.1

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

Iris: First-Class Multi-GPU Programming Experience in Triton

HipKittens: Fast and Furious AMD Kernels

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

An MLIR pipeline for offloading Fortran to FPGAs via OpenMP

mt4g: Memory Topology 4 GPUs

MT4G: A Tool for Reliable Auto-Discovery of NVIDIA and AMD GPU Compute and Memory Topologies

Falcon: GPU-Based Floating-point Adaptive Lossless Compression

A High-Throughput GPU Framework for Adaptive Lossless Compression of Floating-Point Data

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

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