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Scalable Clustering for Vision using GPUs

Wasif Mohiuddin

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Tags: Algorithms, Clustering, Computer science, Computer vision, CUDA, Data mining, nVidia, nVidia GeForce 8600, nVidia GeForce GTX 280, nVidia GeForce GTX 480, nVidia GeForce GTX 580, Tesla T10, Thesis

August 26, 2012 by hgpu

Contributions to Parallel Simulation of Equation-Based Models on Graphics Processing Units

Kristian Stavaker

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Tags: Code generation, Computer science, CUDA, nVidia, nVidia GeForce 8600, OpenCL, Tesla C1060, Tesla M2050, Thesis

December 9, 2011 by hgpu

A GPU accelerated storage system

Abdullah Gharaibeh, Samer A. Kiswany, Sathish Gopalakrishnan, Matei Ripeanu

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8600, nVidia GeForce 8800 GT, nVidia GeForce 9800 GX2, Performance, Storage system

November 1, 2010 by hgpu

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Rodinia Benchmarks for DPC++

Evaluation of Intel’s DPC++ Compatibility Tool in heterogeneous computing

DCA++: a state of the art implementation of the dynamical cluster approximation

Optimizing the Performance of Parallel and Concurrent Applications Based on Asynchronous Many-Task Runtimes

tntorch - Tensor Network Learning with PyTorch

tntorch: Tensor Network Learning with PyTorch

Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark

Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark

MapReduce for Counting Word Frequencies with MPI and GPUs

MapReduce for Counting Word Frequencies with MPI and GPUs

CoDL: Efficient CPU-GPU Co-execution for Deep Learning Inference on Mobile Devices

CoDL: Efficient CPU-GPU Co-execution for Deep Learning Inference on Mobile Devices

SYCLCOMP Utilities

On the Compilation Performance of Current SYCL Implementations

M1 GPUs for (scientific) computations in C++

Seamless GPU acceleration for C++ based physics with the Metal Shading Language on Apple’s M series unified chips

CUB: Cooperative primitives for CUDA C++

Onesweep: A Faster Least Significant Digit Radix Sort for GPUs

Simulator and workload generator for Edge-to-Cloud environments

FELARE: Fair Scheduling of Machine Learning Applications on Heterogeneous Edge Systems

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