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Accelerating BIRCH for Clustering Large Scale Streaming Data Using CUDA Dynamic Parallelism

Jianqiang Dong, Fei Wang, Bo Yuan

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Tags: Clustering, Computer science, CUDA, nVidia, Tesla K20

August 1, 2013 by hgpu

A note on the GPU acceleration of eigenvalue computations

K. Rupp, Ph. Tillet, B. F. Smith, T. Grasser, A. Jungel

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Tags: Algorithms, ATI, ATI Radeon HD 7970, Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 470, OpenCL

August 1, 2013 by hgpu

Matrix Convolution using Parallel Programming

Anirud Pande, Rohit Chandna

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Tags: Image processing, MPI, OpenCL

August 1, 2013 by hgpu

GPU peer-to-peer techniques applied to a cluster interconnect

Roberto Ammendola, Massimo Bernaschi, Andrea Biagioni, Mauro Bisson, Massimiliano Fatica, Ottorino Frezza, Francesca Lo Cicero, Alessandro Lonardo, Enrico Mastrostefano, Pier Stanislao Paolucci, Davide Rossetti, Francesco Simula, Laura Tosoratto, Piero Vicini

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Tags: Computational Physics, CUDA, FPGA, MPI, nVidia, Physics, Tesla S2075

August 1, 2013 by hgpu

Iterative CT Reconstruction on the GPU

Gabor Jakab, Tamas Huszar, Balazs Csebfalvi

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Tags: Algorithms, Computed tomography, CT, CUDA, Image reconstruction, Medicine, nVidia, Tomography

July 31, 2013 by hgpu

The Promises of Hybrid Hexagonal/Classical Tiling for GPU

Tobias Grosser, Sven Verdoolaege, Albert Cohen, P. Sadayappan

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, nVidia GeForce GTX 470, nVidia Quadro NVS 5200 M, PTX

July 31, 2013 by hgpu

Opportunities for Heterogeneous CPUGPU Task Scheduling

Christiaan Arnoldus, Robert Witte

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Tags: ATI, ATI Radeon HD 4850, Computer science, Heterogeneous systems, nVidia, nVidia GeForce 9200 M GS, OpenCL, Task scheduling

July 31, 2013 by hgpu

GPU-based Streaming Algorithm for High-Resolution Cloth Simulation

Min Tang, Dinesh Manocha, Rahul Narain, Ruofeng Tong, Chang Meng

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Tags: 3D Graphics and Realism, Algorithms, Collision detection, Computer science, CUDA, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 680, Tesla K20

July 31, 2013 by hgpu

Graphics Processing Unit acceleration of the Random Phase Approximation in the projector augmented wave method

Jun Yan, Lin Li, Christopher O'Grady

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Tags: Computational Physics, Condensed matter, CUDA, Materials Science, nVidia, Package, Physics, Tesla C2075

July 31, 2013 by hgpu

Image Processing with CUDA

Jia Jun Tse

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Tags: Algorithms, CUDA, Image processing, nVidia, nVidia GeForce GTX 260, Thesis

July 30, 2013 by hgpu

Domain Specific Languages for High Performance Computing

Alejandro Fernandez Suarez

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Tags: Computer science, Heterogeneous systems, High-level Languages, OpenCL, Thesis

July 30, 2013 by hgpu

Counting and Occurrence Sort for GPUs using an Embedded Language

Josef Svenningsson, Bo Joel Svensson, Mary Sheeran

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Tags: Algorithms, Code generation, Computer science, CUDA, High-level Languages, nVidia, nVidia GeForce GTX 670, Package, Sorting

July 30, 2013 by hgpu

OpScanner

A Study of Floating-Point Precision Tuning in Deep Learning Operators Implementations

Atlas CLI: Machine Learning (ML) Lifecycle & Transparency Manager

Scalable GPU-Based Integrity Verification for Large Machine Learning Models

transformers_tvm: Implementation of Encoder Decoder transformer on TVM

Enhancing Transformer Performance and Portability through Auto-tuning Frameworks

INT v.s. FP: A framework to compare low-bit integer and float-point formats

INT v.s. FP: A Comprehensive Study of Fine-Grained Low-bit Quantization Formats

AutoDock-GPU: AutoDock for GPUs and other accelerators

Architecting Tensor Core-Based Reductions for Irregular Molecular Docking Kernels

NCCLX: collective communication framework

Collective Communication for 100k+ GPUs

Tutoring LLM into a Better CUDA Optimizer

Tutoring LLM into a Better CUDA Optimizer

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

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