high performance computing on graphics processing units: hgpu.org

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DySel: Lightweight Dynamic Selection for Kernel-based Data-parallel Programming Model

Li-Wen Chang, Hee-Seok Kim, Wen-mei Hwu

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, OpenCL, Performance, Tesla K20

March 15, 2016 by hgpu

Faster and Cheaper: Parallelizing Large-Scale Matrix Factorization on GPUs

Wei Tan, Liangliang Cao, Liana Fong

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Tags: Algorithms, Computer science, CUDA, Data parallelism, Factorization, nVidia, nVidia GeForce GTX Titan X, OpenMP, Tesla K80

March 15, 2016 by hgpu

Machine Learning at the Limit

John Canny, Huasha Zhao, Ye Chen, Bobby Jaros, Jiangchang Mao

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Tags: Computer science, CUDA, Machine learning, nVidia, nVidia GeForce GTX 680, nVidia GeForce GTX Titan X, Package

March 12, 2016 by hgpu

Accelerating Wright-Fisher Forward Simulations on the Graphics Processing Unit

David S. Lawrie

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Tags: Algorithms, Biology, CUDA, Genetics, nVidia, nVidia GeForce GTX 780 M, nVidia GeForce GTX 980

March 10, 2016 by hgpu

Study and evaluation of an Irregular Graph Algorithm on Multicore and GPU Processor Architectures

Varun Nagpal

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Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia, Performance, Tesla C1060, Thesis

March 10, 2016 by hgpu

Testing fine-grained parallelism for the ADMM on a factor-graph

Ning Hao, AmirReza Oghbaee, Mohammad Rostami, Nate Derbinsky, Jose Bento

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Tags: Algorithms, Computer science, CUDA, Mathematical Software, nVidia, OpenMP, Package, Tesla K40

March 10, 2016 by hgpu

Pragma Directed Shared Memory Centric Optimizations on GPUs

Jing Li, Lei Liu, Yuan Wu, Xiang-Hua Liu, Yi Gao, Xiao-Bing Feng, Cheng-Yong Wu

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Tags: ATI, ATI Radeon HD 7850, Code generation, Computer science, CUDA, nVidia, nVidia GeForce GTX 690, nVidia GeForce GTX Titan, OpenACC, OpenCL, Tesla C2050

March 10, 2016 by hgpu

D-face: Parallel Implementation of CNN Based Face Classifier using Drone Data On K40 & Jetson TK1

Harish, Ayyappa, Santosh, P K Baruah, Kaliuday Balleda, Sairam K M Menon

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Tags: CNN, Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, nVidia Jetson TK1, Tesla K40

March 8, 2016 by hgpu

Topology optimization design of 3D electrothermomechanical actuators by using GPU as a co-processor

Francisco Javier Ramírez-Gil, Emilío Carlos Nelli Silva, Wilfredo Montealegre-Rubio

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Tags: CUDA

March 7, 2016 by fjramireg

Performance Analysis of kNN on large datasets using CUDA & Pthreads

Sriram Kankatala

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Tags: Computer science, CUDA, Data mining, Databases, Machine learning, Nearest neighbour, nVidia, nVidia GeForce GTX 550 Ti, Pattern recognition, Pthreads, Thesis

March 5, 2016 by hgpu

Fast LZW compression using a GPU

Shunji Funasaka, Koji Nakano, Yasuaki Ito

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Tags: Algorithms, Compression, CUDA, Image processing, nVidia, nVidia GeForce GTX 980

March 5, 2016 by hgpu

Heterogeneous parallel algorithms for Computational Fluid Dynamics on unstructured meshes

Guillermo Andres Oyarzun Altamirano

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Tags: cfd, CUDA, Fluid dynamics, MPI, nVidia, OpenCL, SoC, Tesla M2090, Thesis

March 5, 2016 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

DySel: Lightweight Dynamic Selection for Kernel-based Data-parallel Programming Model

Faster and Cheaper: Parallelizing Large-Scale Matrix Factorization on GPUs

Machine Learning at the Limit

Accelerating Wright-Fisher Forward Simulations on the Graphics Processing Unit

Study and evaluation of an Irregular Graph Algorithm on Multicore and GPU Processor Architectures

Testing fine-grained parallelism for the ADMM on a factor-graph

Pragma Directed Shared Memory Centric Optimizations on GPUs

D-face: Parallel Implementation of CNN Based Face Classifier using Drone Data On K40 & Jetson TK1

Topology optimization design of 3D electrothermomechanical actuators by using GPU as a co-processor

Performance Analysis of kNN on large datasets using CUDA & Pthreads

Fast LZW compression using a GPU

Heterogeneous parallel algorithms for Computational Fluid Dynamics on unstructured meshes

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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