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Scalability Analysis of Parallel Algorithms on GPU Clusters

Fengshun Lu, Yufei Pang, Fukang Yin

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Tags: Algorithms, Benchmarking, Computer science, CUDA, GPU cluster, nVidia, Tesla M2050

September 25, 2014 by hgpu

Calculation of Force Field Grids for Molecular Docking Using Graphics Processing Unit

Mikhail A. Farkov

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Tags: Algorithms, Bioinformatics, Biology, CUDA, molecular docking, nVidia, nVidia GeForce GT 440, Tesla K20, Tesla M2090

September 24, 2014 by hgpu

Advanced Optimizations of An Implicit Navier-Stokes Solver on GPGPU

Lixiang Luo, Jack R. Edwards, Hong Luo, Frank Mueller

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Tags: CUDA, Fluid dynamics, MPI, Navier-Stokes equations, nVidia, OpenACC

September 23, 2014 by hgpu

Explicit Integration with GPU Acceleration for Large Kinetic Networks

Benjamin Brock, Andrew Belt, Jay Jay Billings, Mike Guidry

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Tags: Algorithms, Computational Physics, CUDA, nVidia, Physics, Solar and Stellar Astrophysics, Tesla K20

September 23, 2014 by hgpu

Computational Gravitational Dynamics with Modern Numerical Accelerators

Simon Portegies Zwart, Jeroen Bedorf

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Tags: Algorithms, Astrophysics, CUDA, Gravitation, Instrumentation and Methods for Astrophysics, N-body simulation, nVidia, Stellar dynamics, Tesla K20

September 23, 2014 by hgpu

An optimized GPU implementation of a 2D free surface simulation model on unstructured meshes

Asier Lacasta, Mario Morales-Hernández, Javier Murillo, Pilar García-Navarro

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Tags: CUDA, Finite volume method, Hyperbolic PDEs, nVidia Tesla, Triangular meshes

September 23, 2014 by alacasta

High performance histogramming on massively parallel processors

Wen-Mei Hwu

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Tags: Computer science, Computer vision, CUDA, Databases, Machine learning, nVidia, nVidia GeForce GTX 570, nVidia GeForce GTX 780, Thesis

September 20, 2014 by hgpu

On the evaluation of matrix polynomials using several GPGPUs

Pedro Alonso, Murilo Boratto, J. Peinado, J. Ibanez, Jorge Sastre

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Heterogeneous systems, nVidia, Tesla K20, Thesis

September 20, 2014 by hgpu

Supporting Applications Involving Dynamic Data Structures and Irregular Memory Access on Emerging Parallel Platforms

Bin Ren

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Tags: Algorithms, Computer science, CUDA, Intel Xeon Phi, Memory model, nVidia, nVidia Quadro FX 5800, Prefetch, Tesla C2050, Thesis

September 19, 2014 by hgpu

A Software Framework for the Detection and Classification of Biological Targets in Bio-Nano Sensing

Abdul Hafeez

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Tags: Biology, Biophysics, CUDA, Distributed computing, Filtering, nVidia, nVidia Quadro FX 580, Sensing, Thesis

September 19, 2014 by hgpu

Automated Software Testing of Memory Performance in Embedded GPUs

Sudipta Chattopadhyay, Petru Eles, Zebo Peng

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Tags: Computer science, CUDA, Embedded high-performance computing, GPGPU-sim, Memory, nVidia, Performance

September 19, 2014 by hgpu

GPU accelerated Monte Carlo simulation of Brownian motors dynamics with CUDA

J. Spiechowicz, M. Kostur, L. Machura

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Tags: Computational Physics, CUDA, Differential equations, Monte Carlo integration, Monte Carlo simulation, nVidia, nVidia GeForce GTX 680, Physics, Stochastic simulation

September 19, 2014 by hgpu

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Accelerating a Linear Programming Algorithm on AMD GPUs

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Towards Efficient and Practical GPU Multitasking in the Era of LLM

Bandicoot: C++ library for GPU accelerated linear algebra

Bandicoot: A Templated C++ Library for GPU Linear Algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

The Fused Kernel Library: A C++ API to Develop Highly-Efficient GPU Libraries

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

Block: Balancing Load in LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

SIGMo: High-Throughput Batched Subgraph Isomorphism on GPUs for Molecular Matching

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

DGEMM without FP64 Arithmetic – using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

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high performance computing on graphics processing units: hgpu.org

Scalability Analysis of Parallel Algorithms on GPU Clusters

Calculation of Force Field Grids for Molecular Docking Using Graphics Processing Unit

Advanced Optimizations of An Implicit Navier-Stokes Solver on GPGPU

Explicit Integration with GPU Acceleration for Large Kinetic Networks

Computational Gravitational Dynamics with Modern Numerical Accelerators

An optimized GPU implementation of a 2D free surface simulation model on unstructured meshes

High performance histogramming on massively parallel processors

On the evaluation of matrix polynomials using several GPGPUs

Supporting Applications Involving Dynamic Data Structures and Irregular Memory Access on Emerging Parallel Platforms

A Software Framework for the Detection and Classification of Biological Targets in Bio-Nano Sensing

Automated Software Testing of Memory Performance in Embedded GPUs

GPU accelerated Monte Carlo simulation of Brownian motors dynamics with CUDA

Recent source codes

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Bandicoot: C++ library for GPU accelerated linear algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

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